Automatic image semantic interpretation using social action and tagging data

The plethora of social actions and annotations (tags, comments, ratings) from online media sharing Websites and collaborative games have induced a paradigm shift in the research on image semantic interpretation. Social inputs with their added context represent a strong substitute for expert annotations. Novel algorithms have been designed to fuse visual features with noisy social labels and behavioral signals. In this survey, we review nearly 200 representative papers to identify the current trends, challenges as well as opportunities presented by social inputs for research on image semantics. Our study builds on an interdisciplinary confluence of insights from image processing, data mining, human computer interaction, and sociology to describe the folksonomic features of users, annotations and images. Applications are categorized into four types: concept semantics, person identification, location semantics and event semantics. The survey concludes with a summary of principle research directions for the present and the future.     

Interactive resource recommendation algorithm based on tag information

With the popularization of social media and the exponential growth of information generated by online users, the recommender system has been popular in helping users to find the desired resources from vast amounts of data. However, the cold-start problem is one of the major challenges for personalized recommendation. In this work, we utilized the tag information associated with different resources, and proposed a tag-based interactive framework to make the resource recommendation for different users. During the interaction, the most effective tag information will be selected for users to choose, and the approach considers the users’ feedback to dynamically adjusts the recommended candidates during the recommendation process. Furthermore, to effectively explore the user preference and resource characteristics, we analyzed the tag information of different resources to represent the user and resource features, considering the users’ personal operations and time factor, based on which we can identify the similar users and resource items. Probabilistic matrix factorization is employed in our work to overcome the rating sparsity, which is enhanced by embedding the similar user and resource information. The experiments on real-world datasets demonstrate that the proposed algorithm can get more accurate predictions and higher recommendation efficiency.     

Understanding why users tag: A survey of tagging motivation literature and results from an empirical study

While recent progress has been achieved in understanding the structure and dynamics of social tagging systems, we know little about the underlying user motivations for tagging, and how they influence resulting folksonomies and tags. This paper addresses three issues related to this question. (1) What distinctions of user motivations are identified by previous research, and in what ways are the motivations of users amenable to quantitative analysis? (2) To what extent does tagging motivation vary across different social tagging systems? (3) How does variability in user motivation influence resulting tags and folksonomies? In this paper, we present measures to detect whether a tagger is primarily motivated by categorizing or describing resources, and apply these measures to datasets from seven different tagging systems. Our results show that (a) users’ motivation for tagging varies not only across, but also within tagging systems, and that (b) tag agreement among users who are motivated by categorizing resources is significantly lower than among users who are motivated by describing resources. Our findings are relevant for (1) the development of tag-based user interfaces, (2) the analysis of tag semantics and (3) the design of search algorithms for social tagging systems.     

Understanding why users tag: A survey of tagging motivation literature and results from an empirical study

While recent progress has been achieved in understanding the structure and dynamics of social tagging systems, we know little about the underlying user motivations for tagging, and how they influence resulting folksonomies and tags. This paper addresses three issues related to this question. (1) What distinctions of user motivations are identified by previous research, and in what ways are the motivations of users amenable to quantitative analysis? (2) To what extent does tagging motivation vary across different social tagging systems? (3) How does variability in user motivation influence resulting tags and folksonomies? In this paper, we present measures to detect whether a tagger is primarily motivated by categorizing or describing resources, and apply these measures to datasets from seven different tagging systems. Our results show that (a) users’ motivation for tagging varies not only across, but also within tagging systems, and that (b) tag agreement among users who are motivated by categorizing resources is significantly lower than among users who are motivated by describing resources. Our findings are relevant for (1) the development of tag-based user interfaces, (2) the analysis of tag semantics and (3) the design of search algorithms for social tagging systems.     

FastTagRec: fast tag recommendation for software information sites

Software information sites such as StackOverflow and Freeecode enable information sharing and communication for developers around the world. To facilitate correct classification and efficient search, developers need to provide tags for their postings. However, tagging is inherently an uncoordinated process that depends not only on developers’ understanding of their own postings but also on other factors, including developers’ English skills and knowledge about existing postings. As a result, developers keep creating new tags even though existing tags are sufficient. The net effect is an ever increasing number of tags with severe redundancy along with more postings over time. Any algorithms based on tags become less efficient and accurate. In this paper we propose FastTagRec, an automated scalable tag recommendation method using neural network-based classification. By learning existing postings and their tags from existing information, FastTagRec is able to very accurately infer tags for new postings. We have implemented a prototype tool and carried out experiments on ten software information sites. Our results show that FastTagRec is not only more accurate but also three orders of magnitude faster than the comparable state-of-the-art tool TagMulRec. In addition to empirical evaluation, we have also conducted an user study which successfully confirms the usefulness of of our approach.     

Automatic tag expansion using visual similarity for photo sharing websites

In this paper we present an automatic photo tag expansion method designed for photo sharing websites. The purpose of the method is to suggest tags that are relevant to the visual content of a given photo at upload time. Both textual and visual cues are used in the process of tag expansion. When a photo is to be uploaded, the system asks for a couple of initial tags from the user. The initial tags are used to retrieve relevant photos together with their tags. These photos are assumed to be potentially content related to the uploaded target photo. The tag sets of the relevant photos are used to form the candidate tag list, and visual similarities between the target photo and relevant photos are used to give weights to these candidate tags. Tags with the highest weights are suggested to the user. The method is applied on Flickr (). Results show that including visual information in the process of photo tagging increases accuracy with respect to text-based methods.     

Automatic deletion of obsolete information

Information generated in a programming environment is often allowed to grow indefinitely. Designer and user alike are counting on standard backup and disc clearing procedures for archiving old data. In this paper we take the view that one should distinguish between relevant old data that is purposely archived and obsolete information that should automatically be deleted. The two main topics of the paper are the strategies and mechanisms for deleting information and the facilities available to designers of programming environments to specify deletion strategies. Information can be deleted applying a passive or an active strategy. With the passive strategy, information will not actually be deleted until it is certain that there is no interest in it any longer. With the active strategy, an object is immediately deleted when it becomes obsolete, while users of the object are notified of the deletion event. The paper discusses various implementations for these two strategies and shows when they apply. Taking the view that it must be easy to modify and fine tune programming environments, much attention must be given to the designer's support environment for generating programming environments. The paper discusses in particular the facilities for expressing the semantics of names in an environment. Various naming modes are useful for a designer to specify the deletion strategies for his target programming environment. Details are illustrated by applying the ideas to an environment for software development and maintenance.     

踩点式信息搜索的新方法

从小偷踩点获取藏金信息中受到启发，提出了一种互联网信息智能搜索新方法。能够从已经分好类的特定领域网站中，准确高效地搜索出隐藏于其内部的目标网页。把所有的搜索网页根据检索信息分成两类：一类是信息点，一类是信息路径。采用信息路径特征与信息点信息量特征描述有机结合而形成的一种新的搜索知识表示方法。基于这种知识表示方法，智能搜索方法不仅能够对网站中网页进行深度优先的智能搜索，而且还能够通过对其搜索过程和结果的自学习来获取更多更好的搜索知识。     

Action-oriented classification of families' information and communication actions: exploring mothers' viewpoints

Action-oriented service and technology development begins with the idea that people use technologies to reach their action goals. Consequently, we should investigate user needs and how they can be satisfied, and adapt services and technologies to the natural course of actions. Here, we focus on family communication and investigate mobile communication service types for families. For this study 10 mothers were interviewed; we investigated the nature of their everyday information and communication needs and the different knowledge and information transfer actions that were discovered in their families. Qualitative analysis of these interviews was used to generate a taxonomy, which, in turn, can help in providing enhanced individual services and family-centred design models.     

WESONet: Applying semantic web technologies and collaborative tagging to multimedia web information systems

The publication of different media types, like images, audio and video in the World Wide Web is getting more importance each day. However, searching and locating content in multimedia sites is challenging. In this paper, we propose a platform for the development of multimedia web information systems. Our approach is based on the combination between semantic web technologies and collaborative tagging. Producers can add meta-data to multimedia content associating it with different domain-specific ontologies. At the same time, users can tag the content in a collaborative way. The proposed system uses a search engine that combines both kinds of meta-data to locate the desired content. It will also provide browsing capabilities through the ontology concepts and the developed tags.     

煤炭企业信息服务能力评价模型

针对煤炭企业信息服务的特点,采用功能团的方法构建了基于信息资源、信息服务基础设施、信息服务满意度、信息服务方式、信息服务结果5个方面的信息服务能力评价指标体系,提出了基于模糊神经网络的信息服务能力评价模型。该模型可对煤炭企业信息服务平台的信息服务能力进行客观的综合评价,具有操作简单、计算速度快、自我学习等优点。     

基于模糊目标信息系统的监狱警察素质分析

论文将基于模糊目标信息系统的数据挖掘方法引入到监狱人民警察素质的分析之中,并研究了针对性的规则获取算法。实验研究表明,该方法能得到实用规则,在监狱机关提高工作绩效方面具有较大实用价值。     

TAFFY: incorporating tag information into a diffusion process for personalized recommendations

The last few years have witnessed an explosion of information caused by the exponential growth of the Internet and World Wide Web, which confronted us with information overload and brought about an era of big data, appealing for efficient personalized recommender systems to assist the screening of useful information from various sources. As for a recommender system with more than the fundamental object-user rating information, such accessorial information as tags can be exploited and integrated into final ranking lists to improve recommendation performance. However, although existing studies have demonstrated that tags, as the additional yet useful resource, can be designed to improve recommendation performance, most network-based approaches take users, objects and tags as two bipartite graphs, or a tripartite graph, and therefore overlook either the important information among homogeneous nodes in each sub-graph, or the bipartite relations between users, objects or tags. Moreover, recent studies have suggested that the filtration of weak relationships in networks may reasonably enhance recommendation performance of collaborative filtering methods, and it has also been demonstrated that approaches based on the diffusion processes could more effectively capture relationships between objects and users, hence exhibiting higher performance than a typical collaborative filtering method. Based on these understandings, we propose a data fusion approach that integrates historical and tag data towards personalized recommendations. Our method coverts historical and tag data into complex networks, resorts to a diffusion kernel to measure the strength of associations between users and objects, and adopts Fisher’s combined probability test to obtain the statistical significance of such associations for personalized recommendations. We validate our approach via 10-fold cross-validation experiments. Results show that our method outperforms existing methods in not only the recommendation accuracy and diversity, but also retrieval performance. We further show the robustness of our method to related parameters.     

Automatic gender detection using on-line and off-line information

In this paper, the problem of classifying handwritten data with respect to gender is addressed. A classification method based on Gaussian Mixture Models is applied to distinguish between male and female handwriting. Two sets of features using on-line and off-line information have been used for the classification. Furthermore, we combined both feature sets and investigated several combination strategies. In our experiments, the on-line features produced a higher classification rate than the off-line features. However, the best results were obtained with the combination. The final gender detection rate on the test set is 67.57%, which is significantly higher than the performance of the on-line and off-line system with about 64.25 and 55.39%, respectively. The combined system also shows an improved performance over human-based classification. To the best of the authors’ knowledge, the system presented in this paper is the first completely automatic gender detection system which works on on-line data. Furthermore, the combination of on-line and off-line features for gender detection is investigated for the first time in the literature.     

Measuring discrete sensing capability for ISAC via task mutual information

<正>Thanks to its ubiquity,using radio frequency (RF) signals for sensing has found widespread applications.In traditional integrated sensing and communication systems,such as joint radar-communication systems,common sensing tasks include target localization and tracking.Recently,increasingly intelligent systems,such as smart agriculture,lowaltitude economy,and smart healthcare,have demanded more comprehensive and continuous information sensing capabilities to support higher-level decision-making.RF sensing has the potential to offer both spatial and temporal continuity,meeting the multi-dimensional sensing needs of these intelligent systems.Consequently,numerous advanced systems have been proposed,expanding the application scope of RF sensing to be more pervasive,including discrete state ubiquitous sensing tasks (such as material identification [1]),and continuous state ubiquitous sensing tasks (such as health monitoring [2]).With the advent of the 6G era,it is anticipated that the sensing potential of RF systems will be further unleashed.     

Intellectual capital and team resilience capability of information system development project teams

Adversities are inherent in the information system development (ISD) process and often put projects to a halt. However, it is unclear what capabilities the team needs to resist and bounce back from adverse events. The purpose of this study is to propose that team resilience capability (TRC), containing affective, cognitive, and behavioral factors is vital for effective project performance. Further, by adopting the conservation of resource perspective, we theorize that intellectual capital, including human capital, technology capital, and political capital fosters TRC. Survey data collected from 149 ISD project teams confirmed our ideas that TRC is strongly tied with project performance and is more affected by human capital, followed by political capital and technology capital.     

Automatic information timeliness assessment of diabetes web sites by evidence based medicine

Studies on health domain have shown that health websites provide imperfect information and give recommendations which are not up to date with the recent literature even when their last modified dates are quite recent. In this paper, we propose a framework which assesses the timeliness of the content of health websites automatically by evidence based medicine. Our aim is to assess the accordance of website contents with the current literature and information timeliness disregarding the update time stated on the websites.