Collaborative telemedicine for interactive multiuser segmentation of volumetric medical images

Telemedicine has evolved rapidly in recent years to enable unprecedented access to digital medical data, such as with networked image distribution/sharing and online (distant) collaborative diagnosis, largely due to the advances in telecommunication and multimedia technologies. However, interactive collaboration systems which control editing of an object among multiple users are often limited to a simple “locking” mechanism based on a conventional client/server architecture, where only one user edits the object which is located in a specific server, while all other users become viewers. Such systems fail to provide the needs of a modern day telemedicine applications that demand simultaneous editing of the medical data distributed in diverse local sites. In this study, we introduce a novel system for telemedicine applications, with its application to an interactive segmentation of volumetric medical images. We innovate by proposing a collaborative mechanism with a scalable data sharing architecture which makes users interactively edit on a single shared image scattered in local sites, thus enabling collaborative editing for, e.g., collaborative diagnosis, teaching, and training. We demonstrate our collaborative telemedicine mechanism with a prototype image editing system developed and evaluated with a user case study. Our result suggests that the ability for collaborative editing in a telemedicine context can be of great benefit and hold promising potential for further research.     

An SNS-based model for finding collaborative partners

This paper proposes a model, Recommendation of Appropriate Partners (RAP), used on a Social Networking Service (SNS) for locating appropriate “helpers” for users based on individual users’ Chain of Friends (CoF) relationships. Using the RAP model, individual users can participate in a collaborative online community in remote locations, whereby helpers are willing to help other users solve their tasks/problems, and it is intended that both the users and helpers gain knowledge from these interactive online sessions. An example of the RAP-based system was implemented to invite Program Committee members to an international conference. The system was evaluated and the experimental results show that our model is very effective for discovering collaboration partners and finding users with similar interests in order to create communities for providing future and longer-term helping exchange.

     

基于图卷积神经网络的在线社区行为预测

在线知识社区中,问题的回答可以看作多个回答者用户(领域专家)之间的协作行为。协作行为在知识社区中通常是大规模地发生,协作行为预测对在线社交中领域专家的推荐有重要意义。基于在线知识社区中回答者用户之间的协作行为,构建以领域专家为节点,以他们之间的协作回答关系为边的协作网络。由于协作行为网络的构建与社交关系网络的构建上结构的相似性,可以将协作行为预测构建为协作网络中的链接预测问题。通过构建基于图卷积神经网络的链接预测模型,对在线知识社区中回答者用户的协作行为进行预测。基于“知乎”数据集的实验验证,与其他经典的预测方法进行比较时,发现提出的方法能够更加有效地预测在线知识社区中回答者用户之间的协作行为。     

面向语义属性查询的动画场景图像检索系统

设计并实现了一个交互式检索动画场景图像的系统。通过从动画场景图像中自动提出三种语义属性信息（类别、时间以及复杂度）,用户可以输入例如“一幅不复杂的傍晚的天空”作为查询来找到满意的检索结果。该系统是全自动的,并且是可扩展的。所有的语义属性特征计算在后台完成,从而保证系统的实时检索。此外,还设计了针对用户的几种不同交互式检索界面,极大地增强了检索的灵活性及正确性。     

基于X3D的虚拟场景动态交互技术研究

分布式虚拟现实具有虚拟现实的交互特性和网络的分布特性,满足了异地用户协同工作的需求。由于受到网络带宽、时延以及异构性等因素的限制,分布式虚拟现实系统中的3D图形文件要求尽可能小并具有跨平台的特性。本文介绍了基于X3D的虚拟场景中用户和场景交互的原理、方法以及实现过程,并给出了一个典型的应用实例。     

Collaborative Personalization of Image Enhancement

This paper presents methods for personalization of image enhancement, which could be deployed in photo editing software and also in cloud-based image sharing services. We observe that users do have different preferences for enhancing images and that there are groups of people that share similarities in preferences. Our goal is to predict enhancements for novel images belonging to a particular user based on her specific taste, to facilitate the retouching process on large image collections. To that end, we describe an enhancement framework that can learn user preferences in an individual or collaborative way. The proposed system is based on a novel interactive application that allows to collect user’s enhancement preferences. We propose algorithms to predict personalized enhancements by learning a preference model from the provided information. Furthermore, the algorithm improves prediction performance as more enhancement examples are progressively added. We conducted experiments via Amazon Mechanical Turk to collect preferences from a large group of people. Results show that the proposed framework can suggest image enhancements more targeted to individual users than commercial tools with global auto-enhancement functionalities.     

Efficient foreground extraction using RGB-D imaging

Image segmentation is one of the most important topics in the field of computer vision. As a result, many image segmentation approaches have been proposed, and interactive methods based on energy minimization such as GrabCut, have shown successful results. Automating the entire segmentation process is, however, very difficult because virtually all interactive methods require a considerable amount of user interaction. We believe that if additional information is provided to users in order to guide them effectively, the amount of interaction required can be reduced. Consequently, in this paper we propose an efficient foreground extraction algorithm, which utilizes depth information from RGB-D sensors such as Microsoft Kinect and offers users guidance in the foreground extraction process. Our approach can be applied as a pre-processing step for interactive and energy-minimization-based segmentation approaches. Our proposed method is able to segment the foreground from images and give hints that reduce interaction with users. In our method, we make use of the characteristics of depth information captured by RGB-D sensors and describe them using information from the structure tensor. Further, we show experimentally that our proposed method separates foreground from background sufficiently well for real world images.     

The design and realization of CoViD: a system for collaborative virtual 3D design

Many important decisions in the design process are made during fairly early on, after designers have presented initial concepts. In many domains, these concepts are already realized as 3D digital models. Then, in a meeting, the stakeholders for the project get together and evaluate these potential solutions. Frequently, the participants in this meeting want to interactively modify the proposed 3D designs to explore the design space better. Today’s systems and tools do not support this, as computer systems typically support only a single user and computer-aided design tools require significant training. This paper presents the design of a new system to facilitate a collaborative 3D design process. First, we discuss a set of guidelines which have been introduced by others and that are relevant to collaborative 3D design systems. Then, we introduce the new system, which consists of two main parts. The first part is an easy-to-use conceptual 3D design tool that can be used productively even by naive users. The tool provides novel interaction techniques that support important properties of conceptual design. The user interface is non-obtrusive, easy-to-learn, and supports rapid creation and modification of 3D models. The second part is a novel infrastructure for collaborative work, which offers an interactive table and several large interactive displays in a semi-immersive setup. It is designed to support multiple users working together. This infrastructure also includes novel pointing devices that work both as a stylus and a remote pointing device. The combination of the (modified) design tool with the collaborative infrastructure forms a new platform for collaborative virtual 3D design. Then, we present an evaluation of the system against the guidelines for collaborative 3D design. Finally, we present results of a preliminary user study, which asked naive users to collaborate in a 3D design task on the new system.     

A methodological approach for user interface development of collaborative applications: A case study

In the last few years, the production of systems which support learning and group work has been high. However, the design and development of these types of systems are difficult, mainly due to the multidisciplinarity involved. Furthermore, the Graphic User Interface (GUI) of an application is receiving greater attention, since it can be decisive in determining if the application is accepted or rejected by users. Model-based design is a widespread technique in the user interface development process. While reviewing approaches that deal with the modeling and design of user interfaces supporting collaborative tasks, we have detected that there is no proposal that links interactive and collaborative issues. We have introduced a methodological approach to solve this shortcoming. This approach is called CIAM (Collaborative Interactive Application Methodology) and it is composed of several stages in which conceptual models are created using CIAN (Collaborative Interactive Application Notation). These models start by modeling the organization in which the application will be used, as well as the tasks that must be supported. In the initial stages, the organization and the collaborative tasks are modeled using high-level specifications. In the following stages, the level of detail increases and, finally, the interaction between the individual users and the application is modeled using ConcurTaskTrees (CTT) notation. The interaction model acts as a bridge between the design and the implementation of the Graphic User Interface. In this paper we present our methodological approach and an example of applying this method for user interface design of collaborative and interactive applications.     

A systematic review of shared visualisation to achieve common ground

This paper reports a systematic review of shared visualisation based on fifteen papers from 2000 to 2013. The findings identified five shared visualisation strategies that represent the ways implemented to process data sharing and knowledge to arrive at the desired level of understanding. Four visualisation techniques were also identified to show how shared cognition is made possible in designing tools for mediating data or knowledge among the users involved. These findings provide research opportunities in integrating rich interactive data visualisation for mobile-based technologies as an effective mean in supporting collaborative work. Finally, social, task and cognitive elements which can be significantly supported by shared visualisation and a guideline for future researchers seeking to design shared visualisation-based systems are presented.     

X window based group collaborative editing system

Group collaborative editing is an activity by which people geographically dispersed can edit the same document simultaneously. It makes use of computers and network communication to provide an interactive and convenient environment, so people can save much of their time and cost. We have developed a group collaborative editing system which not only allows participants to jointly view and process multimedia documents but also provides real-time interactive conversation facilities to let users feel realistic and friendly. This paper describes the concepts of group collaboration and the mechanisms of our system. It also presents the interface design strategy and the synchronization control method.     

Exploiting rich user information for one-class collaborative filtering

One-class collaborative filtering (OCCF) is an emerging setup in collaborative filtering in which only positive examples or implicit feedback can be observed. Compared with the traditional collaborative filtering setting where the data have ratings, OCCF is more realistic in many scenarios when no ratings are available. In this paper, we propose to improve OCCF accuracy by exploiting the rich user information that is often naturally available in community-based interactive information systems, including a user’s search query history, and purchasing and browsing activities. We propose two major strategies to incorporate such user information into the OCCF models: One is to linearly combine scores from different sources, and the other is to embed user information into collaborative filtering. Furthermore, we employ the MapReduce framework for similarity computation over millions of users and items. Experimental results on two large-scale retail datasets from a major e-commerce company show that the proposed methods are effective and can improve the performance of the OCCF over baseline methods through leveraging rich user information.     

Providing an appropriate search space to solve the fatigue problem in interactive evolutionary computation

The user fatigue problem in interactive evolutionary computation (IEC) is a complex and interesting issue. If the IEC search space is created from the experience or knowledge of domain experts rather than from users values, it causes two potential problems which lead to fatigue problems in IEC: 1) inefficiency and 2) boredom. Therefore, we propose a customer values-based IEC model, solving the fatigue problem by avoiding the potential problems. A case study involving the design of mineral water bottles was used to verify the anti-fatigue capability of the users when using the proposed model. For comparison with the traditional domain knowledge-based model, we built two IEC systems, a customer values-based system and a traditional system, and conducted a user burden test and a system efficiency test over a two-week period. The results of both tests show that our proposed system performed better than the traditional system in designing mineral water bottles. Fang-Cheng Hsu, Ph.D.: He received his M.S. degree from Tamkang University, Taiwan, in 1985 and Ph.D. degree from Department of Information Management, National Central University, Taiwan, in 2000. He is an associate professor of Information Management at Aletheia University in Taiwan. Prior to this, he was on the faculty at Tamsui Oxford College. His current research interests include interactive evolutionary computation and evolutionary computation-based decision support systems. Peter Huang: He received his B.A. degree from Department of Information Management, Aletheia University, Taiwan, in 1997, and M.B.A. degree from Graduate Institute of Management Science, Aletheia University, Taiwan, in 2003. He is on staff at Information Technology Total Services and is a part-time lecturer at Aletheia University. His current research interests are decision analysis and interactive evolutionary computation.     

基于Virtools的虚拟校园在线交互设计与实现

在虚拟校园中,人性化的人机交互设计可提高系统的可用性和用户友好性。本系统在线展示了校园的仿真模型,并提供了可供用户操作的虚拟人物及其交互功能,使用户能尽可能真实地了解校园。为了使用户在浏览虚拟校园时获得更好的在线交互体验,提出并实现了虚拟校园系统需要解决的几个主要交互功能:人物及其视角交互、场景链接交互、信息链接交互、信息提示交互以及虚拟场景中物体的交互。该系统采用了多场景建模方法,以大化小、分而治之,避免使用过大的场景,缩短了系统的在线响应时间;使用Virtools自带的VSL脚本语言实现系统较复杂的交互功能,提高了程序的可重用性和可维护性,同时减少了系统占用的内存空间,提高了运行速度,并且实现了一些Virtools内置的BB不能实现的功能。     

A Bayesian network and analytic hierarchy process based personalized recommendations for tourist attractions over the Internet

Selecting tourist attractions to visit at a destination is a main stage in planning a trip. Although various online travel recommendation systems have been developed to support users in the task of travel planning during the last decade, few systems focus on recommending specific tourist attractions. In this paper, an intelligent system to provide personalized recommendations of tourist attractions in an unfamiliar city is presented. Through a tourism ontology, the system allows integration of heterogeneous online travel information. Based on Bayesian network technique and the analytic hierarchy process (AHP) method, the system recommends tourist attractions to a user by taking into account the travel behavior both of the user and of other users. Spatial web services technology is embedded in the system to provide GIS functions. In addition, the system provides an interactive geographic interface for displaying the recommendation results as well as obtaining users’ feedback. The experiments show that the system can provide personalized recommendations on tourist attractions that satisfy the user.     

Interactive Depixelization of Pixel Art through Spring Simulation

We introduce an approach for converting pixel art into high-quality vector images. While much progress has been made on automatic conversion, there is an inherent ambiguity in pixel art, which can lead to a mismatch with the artist's original intent. Further, there is room for incorporating aesthetic preferences during the conversion. In consequence, this work introduces an interactive framework to enable users to guide the conversion process towards high-quality vector illustrations. A key idea of the method is to cast the conversion process into a spring-system optimization that can be influenced by the user. Hereby, it is possible to resolve various ambiguities that cannot be handled by an automatic algorithm.     

GRIP: A Group Recommender Based on Interactive Preference Model

Numerous applications of recommender systems can provide us a tool to understand users. A group recommender reflects the analysis of multiple users’ behavior, and aims to provide each user of the group with the things they involve according to users’ preferences. Currently, most of the existing group recommenders ignore the interaction among the users. However, in the course of group activities, the interactive preferences will dramatically affect the success of recommenders. The problem becomes even more challenging when some unknown preferences of users are partly influenced by other users in the group. An interaction-based method named GRIP (Group Recommender Based on Interactive Preference) is presented which can use group activity history information and recommender post-rating feedback mechanism to generate interactive preference parameters. To evaluate the performance of the proposed method, it is compared with traditional collaborative filtering on the MovieLens dataset. The results indicate the superiority of the GRIP recommender for multi-users regarding both validity and accuracy.     

Web 2.0 Recommendation service by multi-collaborative filtering trust network algorithm

Recommendation Services (RS) are an essential part of online marketing campaigns. They make it possible to automatically suggest advertisements and promotions that fit the interests of individual users. Social networking websites, and the Web 2.0 in general, offer a collaborative online platform where users socialize, interact and discuss topics of interest with each other. These websites have created an abundance of information about users and their interests. The computational challenge however is to analyze and filter this information in order to generate useful recommendations for each user. Collaborative Filtering (CF) is a recommendation service technique that collects information from a user’s preferences and from trusted peer users in order to infer a new targeted suggestion. CF and its variants have been studied extensively in the literature on online recommending, marketing and advertising systems. However, most of the work done was based on Web 1.0, where all the information necessary for the computations is assumed to always be completely available. By contrast, in the distributed environment of Web 2.0, such as in current social networks, the required information may be either incomplete or scattered over different sources. In this paper, we propose the Multi-Collaborative Filtering Trust Network algorithm, an improved version of the CF algorithm designed to work on the Web 2.0 platform. Our simulation experiments show that the new algorithm yields a clear improvement in prediction accuracy compared to the original CF algorithm.     

基于P2P的协作学习平台的研究与设计

分析目前Web协作学习平台的研究现状,提出利用P2P网络的技术优势,打造一个"平台+资源+交流"的新型协作学习应用模式,并利用JXTA P2P技术设计一个协作学习平台,该平台支持协作学习全过程,主要包括学习小组支持、组内协作学习、协作学习成果评价,以及方便灵活的资源管理和多形式多层次的在线交互支持。     

基于合作模式的文本过滤模型

文本过滤为因特网上用户提供信息服务,旨在帮助用户选择和处理自己感兴趣的文本。本文提出了基于合作模式的文本过滤模型。其基本思想是根据用户评注将用户分成若干类别,综合类别内外用户评注影响,给出了文本推荐机制,将相关文本推荐给用户。此外,利用相关反馈进行类别和参数重新调整,可以有效地改善过滤的效率。该方法不仅适用于单纯文本介质,而且还可以应用到其他非文本介质。