记忆计算:概念、特性及研究进展

随着信息技术的发展,尤其是移动互联网与物联网的发展,有关个人工作和生活的数据呈指数型增长。这些海量的数据中蕴含着丰富而有价值的个人信息,如何从这些数据中挖掘出有价值的信息成为当前信息领域的重要问题。针对该问题,介绍了普适计算领域新兴起的研究主题——记忆计算。记忆计算旨在通过各种带感知和计算功能的设备,比如智能手机、可穿戴设备等,实时感知和捕获用户线上线下活动的数据,分析并挖掘其内在价值,进而组织和管理有意义的记忆数据,实现基于情境的记忆数据呈现,以辅助个体记忆,支持社群交流与协作。讨论了基于移动情境感知的记忆计算的概念、特性、系统模型以及当前研究的关键技术与挑战,综述了记忆计算在生活日志、记忆提醒、往事回忆和群体记忆分享等方面的研究进展,并对其未来发展进行了展望。     

面向虚实融合的人机交互

面向虚实融合的人机交互涉及计算机科学、认知心理学、人机工程学、多媒体技术和虚拟现实等领域，旨在提高人机交互的效率，同时响应人类认知与情感的需求，在办公教育、机器人和虚拟/增强现实设备中都有广泛应用。本文从人机交互涉及感知计算、人与机器人交互及协同、个性化人机对话和数据可视化等4个维度系统阐述面向虚实融合人机交互的发展现状。对国内外研究现状进行对比，展望未来的发展趋势。本文认为兼具可迁移与个性化的感知计算、具备用户行为深度理解的人机协同、用户自适应的对话系统等是本领域的重要研究方向。     

基于语义分析的情感计算技术研究进展

随着机器学习和大数据技术的应用发展,基于语义分析的情感计算与分析技术在研究人类的感知、注意力、记忆、决策、社会交流等诸多方面起着重大作用,它不仅影响到了人工智能技术的发展,还影响到了人/机交互的方式,并受到学术界以及企业界的广泛关注.本文在针对情感定义以及相关90多种情感模型分析的基础上,归纳并提出了目前情感分析领域中存在的6项关键性问题与挑战,其中主要包括：情感的来源与本质特征的表示问题;多模态条件下的情感计算问题;外部因素对情感演化过程的影响度量问题;情感的个性化度量问题;情感群体化特征与传播动力学机制问题以及细微情感的表达、算法改进与优化等问题.同时,本文针对其中的关键问题与技术挑战进行了理论探讨、技术分析、实际应用以及当前工作进展与趋势分析,从而为深入研究和解决基于语义分析条件下的情感计算提供了新的研究线索与方向.     

网络大数据:现状与展望

网络大数据是指“人、机、物”三元世界在网络空间(Cyberspace)中交互、融合所产生并在互联网上可获得的大数据.网络大数据的规模和复杂度的增长超出了硬件能力增长的摩尔定律,给现有的IT架构以及机器处理和计算能力带来了极大挑战.同时,也为人们深度挖掘和充分利用网络大数据的大价值带来了巨大机遇.因此,迫切需要探讨大数据的科学问题,发现网络大数据的共性规律,研究网络大数据定性、定量分析的基础理论与基本方法.文中分析了网络大数据的复杂性、不确定性和涌现性,总结了网络空间感知与数据表示、网络大数据存储与管理体系、网络大数据挖掘和社会计算以及网络数据平台系统与应用等方面的主要问题与研究现状,并对大数据科学、数据计算需要的新模式与新范式、新型的IT基础架构和数据的安全与隐私等方面的发展趋势进行了展望.     

移动情境感知及其交互研究

结合情境信息的移动应用和交互研究是普适计算领域中非常重要的内容.综合移动情境感知相关研究的发展,从人机交互研究的角度,提出了基于用户、环境和任务的情境信息分类方法.根据情境信息的数据获取、数据表示、系统架构、数据处理、服务应用和系统评价等六个方面,分析了移动情境感知应用研究的关键问题,总结了移动情境感知对人机交互研究中的研究方法、数据收集、用户控制感及交互方式等产生的影响.最后提出了当前移动情境感知及其交互研究中存在的问题和可能的研究方向.     

面向社会计算的集成建模方法与应用系统

复杂社会系统建模是社会计算面临的首要问题.面向社会计算领域的建模流程与需求,提出了一种模型深度集成架构,称为POV框架.该框架由物理层、覆盖层和虚拟层3部分组成,提供了模型的组织、表达和集成方法.基于该方法搭建了面向社会计算数据模型交互共享集成平台,为研究者们提供包括数据资源、分析工具和建模仿真计算环境的社会计算实验平...     

基于Agent的股票价格行为仿真

对于股票市场价格行为的解释一直是现代金融理论的核心问题之一。上世纪80年代以来，基于Agcnt的计算金融学提出：社会经济系统的各种复杂性来源于经济系统中个体(Agent)的适应性行为。该文的基本思路即是借助行为金融学和人工智能研究的成果，采用Agent系统理论和计算机仿真相结合，构建符合现实的仿真股市，并通过适应性个体之间的行为和个体与外界经济环境的交互，对股票市场的价格行为提供一种新颖的理解思路。     

位置感知计算的概论、关键问题和技术探讨

自从Mark Weiser在1991年提出普适计算的思想以来,计算模式经历主机计算、桌面计算的阶段后正朝普适计算方向蓬勃发展.上下文感知是普适计算的最重要的特征,同时也是主要的研究领域.自从二十世纪70年代美国军方推出军民两用的室外定位系统GPS,以及1989年Roy Want等开发出支持大规模使用、价格低廉的室内位置传感系统Active Badge以来,位置感知由于其高鲁棒性和确定性、技术的可获得性而成为当前上下文感知计算的一个真正的研究热点.本文从普适计算和智能空间的特征和要求出发,阐述了位置感知计算的主要概念、思想,并根据当前最新的研究状况,解释了位置感知计算面临的主要问题和相应的技术方法,最后叙述了作为本课题组研究的试验系统--智能教室的技术路线.     

一种普适计算环境下自适应中间件

普适计算环境固有的内在复杂性对当前的基础软件提出了新的挑战,迫切需要一种具有感知和自适应能力的中间件.提出了一个由接口、框架和情境元模型组成的自适应中间件,给出了在CAR构件平台上的设计与实现.为获取构件信息和对外提供服务,接口元模型支持同步和异步接口.情境元模型在构件对象内建模情境信息,计算实体间以基于异步事件通知方式交互.框架元模型分类和管理构件,随着运行时计算环境的变化动态改变中间件的结构和行为.软件实体感知环境的变化,实体间以松耦合的方式交互,动态改变自身的结构和行为,满足普适计算环境下的动态自适应需求.     

物联网信息感知与交互技术

信息感知作为物联网的基本功能,是物联网信息"全面感知"的手段.信息交互是物联网应用与服务的基础,是物联网"物物互联"的目的.随着物联网研究热潮的兴起,以传统无线传感器网络为核心的感知网络研究迅速升温,并在信息感知与交互方面取得了大量研究成果.文章分析了物联网信息感知与交互方面的最新研究进展.在信息感知方面,从数据收集、清洗、压缩、聚集和融合几个方面,梳理归纳了数据获取和处理的主要方法.在信息交互方面,提出了物联网信息交互的基本模型,分析总结了信息交互涉及的主要技术.在此基础上,讨论了物联网信息感知与交互研究的热点问题,包括新的感知技术、能效平衡、信息安全和移动感知网络等.最后,指出了物联网信息感知与交互技术发展面临的问题和挑战,展望了未来的研究方向.     

Methodological design and comparative evaluation of a MAS providing AmI

Researches on Ambient Intelligent and Ubiquitous Computing using wireless technologies have increased in the last years. In this work, we review several scenarios to define a multi-agent architecture that support the information needs of these new technologies, for heterogeneous domain. Our contribution consists of designing in a methodological way a Context Aware System (involving location services) using agents that can be used in very different domains. We describe all the steps followed in the design of the agent system. We apply a hybridizing methodology between GAIA and AUML. Additionally we propose a way to compare different agent architectures for Context Aware System using agent interactions. So, in this paper, we describe the assignment of weight values to agents interaction in two different MAS architectures for Context Aware problems solving different scenarios inspired in FIPA standard negotiation protocols.     

一种用户态活动感知计算模型的设计与实现

活动感知计算通过提取分析出用户某种状态的复合信息减少用户交互,更好的无缝融合用户当前的上下文信息和周围的情景,从而逐渐成为上下文感知计算中一个新的研究热点。本文提出了一种能较为准确表达用户当前整体状态的复合信息概念——用户态,和一套感知、分析、推理其的系统模型。根据模型过滤后的个性化推荐服务内容更加准确,服务质量和使用效率得到进一步提高。     

Cloud-Sea Computing Systems: Towards Thousand-Fold Improvement in Performance per Watt for the Coming Zettabyte Era

We are entering a new era of computing, characterized by the need to handle over one zettabyte （1021 bytes, or ZB） of data. The world＇s capacities to sense, transmit, store, and process information need to grow three orders of magnitude, while maintain an energy consumption level similar to that of the year 2010. In other words, we need to produce thousand-fold improvement in performance per watt. To face this challenge, in 2012 the Chinese Academy of Sciences launched a 10-year strategic priority research initiative called the Next Generation Information and Communication Technology initiative （the NICT initiative）. A research thrust of the NICT program is the Cloud-Sea Computing Systems project. The main idea is to augment conventional cloud computing by cooperation and integration of the cloud-side systems and the sea-side systems, where the ＂sea-side＂ refers to an augmented client side consisting of human facing and physical world facing devices and subsystems. The Cloud-Sea Computing Systems project consists of four research tasks： a new computing model called REST 2.0 which extends the REST （representational state transfer） architectural style of Web computing to cloud-sea computing, a three-tier storage system architecture capable of managing ZB of data, a billion-thread datacenter server with high energy efficiency, and an elastic processor aiming at energy efficiency of one trillion operations per second per watt. This special section contains 12 papers produced by the Cloud-Sea Computing Systems project team, presenting research results relating to sensing and REST 2.0, the elastic processor, the hyperparallel server, and the cloud-sea storage.     

Detection and analysis of resource usage anomalies in large distributed systems through multi‐scale visualization

Understanding the behavior of large scale distributed systems is generally extremely difficult as it requires to observe a very large number of components over very large time. Most analysis tools for distributed systems gather basic information such as individual processor or network utilization. Although scalable because of the data reduction techniques applied before the analysis, these tools are often insufficient to detect or fully understand anomalies in the dynamic behavior of resource utilization and their influence on the applications performance. In this paper, we propose a methodology for detecting resource usage anomalies in large scale distributed systems. The methodology relies on four functionalities: characterized trace collection, multi‐scale data aggregation, specifically tailored user interaction techniques, and visualization techniques. We show the efficiency of this approach through the analysis of simulations of the volunteer computing Berkeley Open Infrastructure for Network Computing architecture. Three scenarios are analyzed in this paper: analysis of the resource sharing mechanism, resource usage considering response time instead of throughput, and the evaluation of input file size on Berkeley Open Infrastructure for Network Computing architecture. The results show that our methodology enables to easily identify resource usage anomalies, such as unfair resource sharing, contention, moving network bottlenecks, and harmful short‐term resource sharing. Copyright © 2011 John Wiley & Sons, Ltd.     

MidHDC: Advanced topics on middleware services for heterogeneous distributed computing. Part 2

Currently distributes systems support different computing paradigms like Cluster Computing, Grid Computing, Peer-to-Peer Computing, and Cloud Computing all involving elements of heterogeneity. These computing distributed systems are often characterized by a variety of resources that may or may not be coupled with specific platforms or environments. All these topics challenge today researchers, due to the strong dynamic behavior of the user communities and of resource collections they use.The second part of this special issue presents advances in allocation algorithms, service selection, VM consolidation and mobility policies, scheduling multiple virtual environments and scientific workflows, optimization in scheduling process, energy-aware scheduling models, failure Recovery in shared Big Data processing systems, distributed transaction processing middleware, data storage, trust evaluation, information diffusion, mobile systems, integration of robots in Cloud systems.     

基于主动推理的情境感知系统框架

情境感知计算是普及计算引发的新的研究领域。在分析情境感知计算系统模型的基础上，研究并改进了系统开发模型CTK，提出基于主动推理的系统框架ACTK，以智能家庭原型的实现证明了它对情境感知应用的开发和研究更好的支持。     

A review of motion analysis methods for human Nonverbal Communication Computing

Human Nonverbal Communication Computing aims to investigate how people exploit nonverbal aspects of their communication to coordinate their activities and social relationships. Nonverbal behavior plays important roles in message production and processing, relational communication, social interaction and networks, deception and impression management, and emotional expression. This is a fundamental yet challenging research topic. To effectively analyze Nonverbal Communication Computing, motion analysis methods have been widely investigated and employed. In this paper, we introduce the concept and applications of Nonverbal Communication Computing and also review some of the motion analysis methods employed in this area. They include face tracking, expression recognition, body reconstruction, and group activity analysis. In addition, we also discuss some open problems and the future directions of this area.     

In-depth behavior understanding and use: The behavior informatics approach

The in-depth analysis of human behavior has been increasingly recognized as a crucial means for disclosing interior driving forces, causes and impact on businesses in handling many challenging issues such as behavior modeling and analysis in virtual organizations, web community analysis, counter-terrorism and stopping crime. The modeling and analysis of behaviors in virtual organizations is an open area. Traditional behavior modeling mainly relies on qualitative methods from behavioral science and social science perspectives. On the other hand, so-called behavior analysis is actually based on human demographic and business usage data, such as churn prediction in the telecommunication industry, in which behavior-oriented elements are hidden in routinely collected transactional data. As a result, it is ineffective or even impossible to deeply scrutinize native behavior intention, lifecycle and impact on complex problems and business issues. In this paper, we propose the approach of behavior informatics (BI), in order to support explicit and quantitative behavior involvement through a conversion from source data to behavioral data, and further conduct genuine analysis of behavior patterns and impacts. BI consists of key components including behavior representation, behavioral data construction, behavior impact analysis, behavior pattern analysis, behavior simulation, and behavior presentation and behavior use. We discuss the concepts of behavior and an abstract behavioral model, as well as the research tasks, process and theoretical underpinnings of BI. Two real-world case studies are demonstrated to illustrate the use of BI in dealing with complex enterprise problems, namely analyzing exceptional market microstructure behavior for market surveillance and mining for high impact behavior patterns in social security data for governmental debt prevention. Substantial experiments have shown that BI has the potential to greatly complement the existing empirical and specific means by finding deeper and more informative patterns leading to greater in-depth behavior understanding. BI creates new directions and means to enhance the quantitative, formal and systematic modeling and analysis of behaviors in both physical and virtual organizations.     

A lightweight clustering–based approach to discover different emotional shades from social message streams

With the explosion of social media, automatic analysis of sentiment and emotion from user-generated content has attracted the attention of many research areas and commercial-marketing domains targeted at studying the social behavior of web users and their public attitudes toward brands, social events, and political actions. Capturing the emotions expressed in the written language could be crucial to support the decision-making processes: the emotion resulting from a tweet or a review about an item could affect the way to advertise or to trade on the web and then to make predictions about future changes in popularity or market behavior. This paper presents an experience with the emotion-based classification of textual data from a social network by using an extended version of the fuzzy C-means algorithm called extension of fuzzy C-means. The algorithm shows interesting results due to its intrinsic fuzzy nature that reflects the human feeling expressed in the text, often composed of a mix of blurred emotions, and at the same time, the benefits of the extended version yield better classification results.     

基于可信计算的终端安全体系结构研究与进展

基于可信计算的终端安全体系结构研究是当前信息安全领域研究的新方向。本文首先对可信计算的关键模块TPM以及若干关键技术进行了深入的分析，而后概述了几个典型的基于可信计算的终端安全体系结构，最后讨论了当前体系结构研究存在的问题和今后的研究方向。