The State of the Art in Text Filtering

This paper develops a conceptual framework for text filtering practice and research, and reviews present practice in the field. Text filtering is an information seeking process in which documents are selected from a dynamic text stream to satisfy a relatively stable and specific information need. A model of the information seeking process is introduced and specialized to define text filtering. The historical development of text filtering is then reviewed and case studies of recent work are used to highlight important design characteristics of modern text filtering systems. User modeling techniques drawn from information retrieval, recommender systems, machine learning and other fields are described. The paper concludes with observations on the present state of the art and implications for future research on text filtering. This revised version was published online in July 2006 with corrections to the Cover Date.     

The State of the Art in Map-Like Visualization

Cartographic maps have been shown to provide cognitive benefits when interpreting data in relation to a geographic location. In visualization, the term map-like describes techniques that incorporate characteristics of cartographic maps in their representation of abstract data. However, the field of map-like visualization is vast and currently lacks a clear classification of the existing techniques. Moreover, choosing the right technique to support a particular visualization task is further complicated, as techniques are scattered across different domains, with each considering different characteristics as map-like. In this paper, we give an overview of the literature on map-like visualization and provide a hierarchical classification of existing techniques along two general perspectives: imitation and schematization of cartographic maps. Each perspective is further divided into four principal categories that group common map-like techniques along the visual primitives they affect. We further discuss this classification from a task-centered view and highlight open research questions.     

The State of the Art in Sentiment Visualization

Visualization of sentiments and opinions extracted from or annotated in texts has become a prominent topic of research over the last decade. From basic pie and bar charts used to illustrate customer reviews to extensive visual analytics systems involving novel representations, sentiment visualization techniques have evolved to deal with complex multidimensional data sets, including temporal, relational and geospatial aspects. This contribution presents a survey of sentiment visualization techniques based on a detailed categorization. We describe the background of sentiment analysis, introduce a categorization for sentiment visualization techniques that includes 7 groups with 35 categories in total, and discuss 132 techniques from peer‐reviewed publications together with an interactive web‐based survey browser. Finally, we discuss insights and opportunities for further research in sentiment visualization. We expect this survey to be useful for visualization researchers whose interests include sentiment or other aspects of text data as well as researchers and practitioners from other disciplines in search of efficient visualization techniques applicable to their tasks and data.     

The State of the Art in Vortex Extraction

Vortices are commonly understood as rotating motions in fluid flows. The analysis of vortices plays an important role in numerous scientific applications, such as in engineering, meteorology, oceanology, medicine and many more. The successful analysis consists of three steps: vortex definition, extraction and visualization. All three have a long history, and the early themes and topics from the 1970s survived to this day, namely, the identification of vortex cores, their extent and the choice of suitable reference frames. This paper provides an overview over the advances that have been made in the last 40 years. We provide sufficient background on differential vector field calculus, extraction techniques like critical point search and the parallel vectors operator, and we introduce the notion of reference frame invariance. We explain the most important region‐based and line‐based methods, integration‐based and geometry‐based approaches, recent objective techniques, the selection of reference frames by means of flow decompositions, as well as a recent local optimization‐based technique. We point out relationships between the various approaches, classify the literature and identify open problems and challenges for future work.     

The State of the Art in Visualizing Multivariate Networks

Multivariate networks are made up of nodes and their relationships (links), but also data about those nodes and links as attributes. Most real‐world networks are associated with several attributes, and many analysis tasks depend on analyzing both, relationships and attributes. Visualization of multivariate networks, however, is challenging, especially when both the topology of the network and the attributes need to be considered concurrently. In this state‐of‐the‐art report, we analyze current practices and classify techniques along four axes: layouts, view operations, layout operations, and data operations. We also provide an analysis of tasks specific to multivariate networks and give recommendations for which technique to use in which scenario. Finally, we survey application areas and evaluation methodologies.     

The State of the Art in Multilayer Network Visualization

Modelling relationship between entities in real‐world systems with a simple graph is a standard approach. However, reality is better embraced as several interdependent subsystems (or layers). Recently, the concept of a multilayer network model has emerged from the field of complex systems. This model can be applied to a wide range of real‐world data sets. Examples of multilayer networks can be found in the domains of life sciences, sociology, digital humanities and more. Within the domain of graph visualization, there are many systems which visualize data sets having many characteristics of multilayer graphs. This report provides a state of the art and a structured analysis of contemporary multilayer network visualization, not only for researchers in visualization, but also for those who aim to visualize multilayer networks in the domain of complex systems, as well as those developing systems across application domains. We have explored the visualization literature to survey visualization techniques suitable for multilayer graph visualization, as well as tools, tasks and analytic techniques from within application domains. This report also identifies the outstanding challenges for multilayer graph visualization and suggests future research directions for addressing them.     

The State of the Art in Interactive Global Illumination

The interaction of light and matter in the world surrounding us is of striking complexity and beauty. Since the very beginning of computer graphics, adequate modelling of these processes and efficient computation is an intensively studied research topic and still not a solved problem. The inherent complexity stems from the underlying physical processes as well as the global nature of the interactions that let light travel within a scene. This paper reviews the state of the art in interactive global illumination (GI) computation, i.e., methods that generate an image of a virtual scene in less than 1 s with an as exact as possible, or plausible, solution to the light transport. Additionally, the theoretical background and attempts to classify the broad field of methods are described. The strengths and weaknesses of different approaches, when applied to the different visual phenomena, arising from light interaction are compared and discussed. Finally, the paper concludes by highlighting design patterns for interactive GI and a list of open problems.     

The State of the Art in Mobile Graphics Research

High-quality computer graphics let mobile-device users access more compelling content. Still, the devices' limitations and requirements differ substantially from those of a PC. This survey of mobile graphics research describes current solutions in terms of specialized hardware (including 3D displays), rendering and transmission, visualization, and user interfaces.     

The State of the Art in Agent Communication Languages

Like societies of humans, there is a need for agents in a multi-agent system to rely on one another, enlist the support of peers in order to solve complex tasks. Agents will be able to cooperate only through a meaningful communication language that can bear correctly their mental states and convey precisely the content of their messages. In search for the ideal agent communication language (ACL), several initiatives like the pioneering work of the Knowledge Sharing Effort and the Foundation for Intelligent Physical Agents (FIPA) are paving the way for a platform where all agents would be able to interact regardless of their implementation environment. ACL is a new field of study that could gain from a survey in expanding its application areas. For this purpose, we examine in this paper the state of the art in ACL design and suggest some principles for building a generalized ACL framework. We then evaluate some existing ACL models, and present the current issues in ACL research, and new perspectives. Received 22 July 1999 / Revised 23 March 2000 / Accepted 2 May 2000     

网络认证技术新发展

文章分析了身份认证的必要性和发展现况,详细介绍了常见实体认证手段和基于密码学认证协议。随着移动通信的迅猛发展,身份认证和认证协议都出现许多研究热点。在HMAC算法的基础上还提出了一种改进认证协议,可以有效防止消息的重传。     

The State of the Art of Nurse Rostering

Nurse rostering is a complex scheduling problem that affects hospital personnel on a daily basis all over the world. The need for quality software solutions is acute for a number of reasons. In particular, it is very important to efficiently utilise time and effort, to evenly balance the workload among people and to attempt to satisfy personnel preferences. A high quality roster can lead to a more contented and thus more effective workforce.In this review, we discuss nurse rostering within the global personnel scheduling problem in healthcare. We begin by briefly discussing the review and overview papers that have appeared in the literature and by noting the role that nurse rostering plays within the wider context of longer term hospital personnel planning. The main body of the paper describes and critically evaluates solution approaches which span the interdisciplinary spectrum from operations research techniques to artificial intelligence methods. We conclude by drawing on the strengths and weaknesses of the literature to outline the key issues that need addressing in future nurse rostering research.     

The State of the Art in Visualizing Dynamic Multivariate Networks

Most real-world networks are both dynamic and multivariate in nature, meaning that the network is associated with various attributes and both the network structure and attributes evolve over time. Visualizing dynamic multivariate networks is of great significance to the visualization community because of their wide applications across multiple domains. However, it remains challenging because the techniques should focus on representing the network structure, attributes and their evolution concurrently. Many real-world network analysis tasks require the concurrent usage of the three aspects of the dynamic multivariate networks. In this paper, we analyze current techniques and present a taxonomy to classify the existing visualization techniques based on three aspects: temporal encoding, topology encoding, and attribute encoding. Finally, we survey application areas and evaluation methods; and discuss challenges for future research.     

光学神经网络的现状与进展

通过介绍两种典型的光学神经网络ＯＮＮ（ＯｐｔｉｃａｌＮｅｕｒａｌＮｅｔｗ ｏｒｋ）——透镜阵列互连网络和全息互连网络,讨论了当前ＯＮＮ 的现状、存在问题及发展趋势。还介绍了图样间联想和洗牌网等ＯＮＮ模型中的新算法和新拓扑结构。     

光学神经网络的现状与进展

通过介绍两种典型的光学神经网络ＯＮＮ－透镜阵列互连网络和全息互 网张,讨论了不前ＯＮＮ的现状,存在问题及发展趋势。还介绍了图样间联想和洗牌网等ＯＮＮ模型中的新算法和新拓扑结构。     

Survivable Network Design: The State of the Art

In the past few years the telecommunications industry has undergone significant changes. We are rapidly evolving to a state where audio/visual and data traffic is all provided on the same networks. Telecommunication companies are investing billions of dollars in the design and maintenance of telecommunication networks to provide the users with the better quality of service that they have begun to expect. Due to their high capacity, fiber optic cables have become the medium of choice in the deployment of such new networks worldwide. Such high capacities encourage telecommunication providers to create networks that are substantially more sparse than previous copper based networks. Unfortunately, with sparsity comes vulnerability to failure. Given the dependence on the varied services offered by the modern networks, the magnitudes of the investments involved and the costs of disasters it is only logical that researchers look at problems in survivable network design as an interesting research question. There have been a number of papers that have addressed these and other related issues. In this paper we try to classify the area of survivable network design and provide a classification scheme for the same.     

Cooperative Multi-Agent Learning: The State of the Art

Cooperative multi-agent systems (MAS) are ones in which several agents attempt, through their interaction, to jointly solve tasks or to maximize utility. Due to the interactions among the agents, multi-agent problem complexity can rise rapidly with the number of agents or their behavioral sophistication. The challenge this presents to the task of programming solutions to MAS problems has spawned increasing interest in machine learning techniques to automate the search and optimization process. We provide a broad survey of the cooperative multi-agent learning literature. Previous surveys of this area have largely focused on issues common to specific subareas (for example, reinforcement learning, RL or robotics). In this survey we attempt to draw from multi-agent learning work in a spectrum of areas, including RL, evolutionary computation, game theory, complex systems, agent modeling, and robotics. We find that this broad view leads to a division of the work into two categories, each with its own special issues: applying a single learner to discover joint solutions to multi-agent problems (team learning), or using multiple simultaneous learners, often one per agent (concurrent learning). Additionally, we discuss direct and indirect communication in connection with learning, plus open issues in task decomposition, scalability, and adaptive dynamics. We conclude with a presentation of multi-agent learning problem domains, and a list of multi-agent learning resources.     

传感器的现状和未来

当今传感器的明显发展趋势是从传统的传感器设计和应用转向以微机械加工技术为基础的微传感器和智能化传感器的设计和应用,使智能结构的实现成为可能。本文就传感器技术的现状和未来的发展予以综述,发展微传感器对我国仪器仪表工业的重要性略加讨论。