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Hybrid cloud load prediction model for LMS applications based on class activity patterns 基于班级活动模式的LMS应用混合云负载预测模型
Pub Date : 2013-11-01 DOI: 10.1109/ICAWST.2013.6765450
S. Maneewongvatana, S. Maneewongvatana
Hybrid cloud is a cloud computing model that combines internal resources of the organization with external resources. One major advantage of hybrid cloud computing is to accommodate short but significant transient loads that enterprise server cannot handle. A challenge of managing load balancing in this environment is usually on the tradeoff between user satisfaction and cost of external resources. In some applications, like learning management system (LMS), it is possible to predict load in advance using existing class activity patterns stored in its own database, and therefore it makes resource provisioning easier. In this paper, we analyze the class activity data of an LMS site and model the self-aware load prediction based on these patterns.
混合云是一种将组织内部资源与外部资源相结合的云计算模型。混合云计算的一个主要优势是可以容纳企业服务器无法处理的短暂但重要的瞬时负载。在这种环境中管理负载平衡的挑战通常是在用户满意度和外部资源成本之间进行权衡。在一些应用程序中,例如学习管理系统(LMS),可以使用存储在其自己的数据库中的现有类活动模式提前预测负载,因此它使资源配置更容易。本文对某LMS站点的班级活动数据进行了分析,并在此基础上建立了自感知负荷预测模型。
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引用次数: 2
One source signal extraction based on metrics transform 一种基于度量变换的源信号提取方法
Pub Date : 2013-11-01 DOI: 10.1109/ICAWST.2013.6765493
Linlin Chen, Xiaohong Ma, Jifei Song, Shuxue Ding
A new approach for one source signal extraction based on metrics transform is proposed and investigated in this paper. First, the mixing matrix is estimated by employing the K-means algorithm on single-source-points with higher energies. Then, the time-frequency points that incorporate one source, which can be used to approximately denote the source signals that include main information of them, are found out by employing a novel metrics transform separation algorithm. Next, the Mel Frequency Cepstral Coefficients of these signals as well as the referenced signal are extracted respectively. And here we will get the index of the target signal which has the maximum similarity to the referenced signal. After that, we apply the metrics transform step by step to find the time-frequency points that incorporate two sources, three sources and so on. A key point is that only those points which contain the contributions of the target signal will be processed. Finally the target signal is obtained through the inverse short-time Fourier transform. Compared with existing methods, our approach can be used even for the case in which the number of mixtures is smaller than that of sources and does not need any extra process after the separating. Experimental results indicate the validity of the method.
提出并研究了一种基于度量变换的单源信号提取方法。首先,采用K-means算法对能量较高的单源点进行混合矩阵估计;然后,采用一种新的度量变换分离算法,找出包含一个源的时频点,该时频点可以用来近似表示包含源信号主要信息的源信号。然后,分别提取这些信号和参考信号的Mel频率倒谱系数。在这里我们将得到与参考信号相似度最大的目标信号的索引。然后,我们逐步应用度量变换来找到包含两个源、三个源等的时频点。关键的一点是,只有那些包含目标信号贡献的点才会被处理。最后通过短时间傅里叶反变换得到目标信号。与现有方法相比,我们的方法可以用于混合物数量小于源数量的情况,并且在分离后不需要任何额外的处理。实验结果表明了该方法的有效性。
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引用次数: 0
A study of integrating social networking service into the virtual pet web game system 将社交网络服务融入虚拟宠物网页游戏系统的研究
Pub Date : 2013-11-01 DOI: 10.1109/ICAWST.2013.6765475
Lin Hui, Wan-chi Lin, Xiwen Li
As the World Wide Web developed and the utilization rate of the mobile device grew up, a lot of computer game companies chose to expand their product platform to the web games or the smart phone apps. These two kinds of game categories can become as popular as nowadays, because these are the ways that users can easily have access to and to play the games with. Based on the above and the fact that social networking service has become a part of our daily life, we decided to merge all social network features into our web game to make our games more compatible. This research is focused on virtual pet game with ability of allowing interaction between friends' pets and sharing messages among friends and the system are accessible through the web browser. In the future, we will combine social network system and the virtual pet game and also make a smart phone app version out of the combination, so that the users can take care of their own virtual pets anytime and anywhere as long as they have access to the Internet.
随着万维网的发展和移动设备使用率的提高,许多电脑游戏公司选择将其产品平台扩展到网页游戏或智能手机应用。这两种类型的游戏能够像现在一样流行,因为这是用户能够轻松访问并玩游戏的方式。基于上述情况以及社交网络服务已经成为我们日常生活的一部分这一事实,我们决定将所有社交网络功能整合到我们的网页游戏中,以使我们的游戏更具兼容性。本研究的主要内容是虚拟宠物游戏,该游戏可以让朋友之间的宠物进行互动,并在朋友之间分享信息,该系统可以通过浏览器访问。未来,我们将把社交网络系统和虚拟宠物游戏结合起来,并结合出智能手机应用版本,让用户只要能上网,就可以随时随地照顾自己的虚拟宠物。
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引用次数: 1
A dorsal pathway guided visual attention model 背侧通路引导的视觉注意模型
Pub Date : 2013-11-01 DOI: 10.1109/ICAWST.2013.6765517
Lingxiang Zheng, Xianchao Zheng, Zhanjian Lin, Weiwei Tang, Changle Zhou
Attention computational model is widely used in the embedded intelligent vision system to help it offload the processing effort. In this paper, we proposed a visual attention computational model based on the biological mechanism that the dorsal pathway will guide the ventral visual information process. The model involves two feature processing subsystems, one is the dorsal pathway feature processing subsystem and the other is the ventral pathway feature processing subsystem. Moreover, the dorsal pathway feature processing subsystem will generate a signal based on its processing result to modulate the information processing of the ventral pathway feature processing subsystem. The experiment results show that the proposed model outperforms the comparison models in four different test scenarios, which indicates that the proposed model may be more biologically plausible and can help the embedded intelligent vision system to find out the interested objects more accurately.
注意力计算模型被广泛应用于嵌入式智能视觉系统中,以帮助其减轻处理工作量。本文基于背侧通路引导腹侧视觉信息加工的生物学机制,提出了一个视觉注意计算模型。该模型包括两个特征处理子系统,一个是背侧路径特征处理子系统,另一个是腹侧路径特征处理子系统。此外,背侧通路特征处理子系统将根据其处理结果产生信号,以调制腹侧通路特征处理子系统的信息处理。实验结果表明,该模型在四种不同的测试场景下均优于对比模型,表明该模型具有更强的生物学合理性,可以帮助嵌入式智能视觉系统更准确地找到感兴趣的物体。
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引用次数: 1
Make decision boundary smoother by transition learning 通过迁移学习使决策边界更加平滑
Pub Date : 2013-11-01 DOI: 10.1109/ICAWST.2013.6765409
Y. Liu, Qiangfu Zhao, N. Yen
Transition learning means the short learning period after switching from one learning method to another in this paper. The idea of transition learning is to apply balanced ensemble learning for a certain time, and then to switch to negative correlation learning. Because of the different learning functions between the two methods, the learning behaviors are expected to have a sudden changes in transition learning. Experimental studies had been conducted to examine such learning behaviors in the transition process. It was found that the training error rates jumped immediately in the transition while the testing error rates often appeared to fall slightly. Such large changes in error rates suggested that the decision boundary formed by balanced ensemble learning had been greatly altered in transition learning. This paper presents the explanations of the transition learning from both the ensemble and individual neural network levels.
过渡学习是指从一种学习方法切换到另一种学习方法后的短暂学习时间。过渡学习的思想是在一定时间内应用平衡集成学习,然后切换到负相关学习。由于两种学习方法的学习功能不同,学习行为在过渡学习中会发生突变。在过渡过程中对这种学习行为进行了实验研究。研究发现,训练错误率在转换过程中迅速上升,而测试错误率往往出现小幅下降。如此大的错误率变化表明,平衡集成学习形成的决策边界在过渡学习中发生了很大的变化。本文从整体神经网络和个体神经网络两个层面对过渡学习进行了解释。
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引用次数: 2
Bayesian Network for algorithm selection: Real-world hierarchy for nodes reduction 贝叶斯网络的算法选择:现实世界的层次结构节点缩减
Pub Date : 2013-11-01 DOI: 10.1109/ICAWST.2013.6765411
M. Lukac, M. Kameyama
In order to obtain the best result in image understanding it is desirable to select the best algorithm on a case by case basis. An algorithm can be selected using only image features, however such selected algorithms will often generate errors due to occlusion, shadows and other environmental conditions. To avoid such errors, it is necessary to understand processing errors on a symbolic level. Using symbolic information to determine the best algorithm is however difficult task because the possible combinations of elements and environmental conditions are almost infinite. Consequently it is impossible to predict best algorithm for all possible combinations of objects, environment conditions and context variations. In this paper we investigate selection of algorithms using symbolic image description and the determination of algorithms' error from high level image description. The proposed method transforms and minimize the high level information contained in the symbolic image description in such manner that will preserve the algorithm selection quality. The transformation takes a high level information label and transforms it into a set of generic features while the minimization uses hierarchy to reduce the specific nature of the information. Both methods of information reduction are used in a Bayesian Network because a BN is well known for using the generalization and hierarchy. As is shown in this paper, such representation efficiently reduces the fine grain high-level symbolic description to a coarser-grain hierarchy that preserves the selection quality but reduces the number of nodes.
为了获得最佳的图像理解结果,需要逐案选择最佳算法。可以仅根据图像特征选择算法,但是所选择的算法往往会由于遮挡、阴影等环境条件而产生误差。为了避免这种错误,有必要在符号级别上理解处理错误。然而,使用符号信息来确定最佳算法是一项困难的任务,因为元素和环境条件的可能组合几乎是无限的。因此,不可能预测所有可能的对象、环境条件和上下文变化组合的最佳算法。本文研究了符号图像描述算法的选择以及从高层次图像描述中确定算法误差的问题。该方法对包含在符号图像描述中的高级信息进行变换和最小化,从而保持算法选择的质量。转换采用高级信息标签并将其转换为一组通用特征,而最小化使用层次结构来减少信息的特定性质。这两种信息约简方法都用于贝叶斯网络,因为BN以使用泛化和层次结构而闻名。如本文所示,这种表示有效地将细粒度高级符号描述简化为粗粒度层次结构,在保留选择质量的同时减少了节点数量。
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引用次数: 1
Dynamically re-definable ontology based user intension awareness 基于用户内涵感知的可动态重定义本体
Pub Date : 2013-11-01 DOI: 10.1109/ICAWST.2013.6765471
Yu-Hsiang Lin, R. Chen, Qiangfu Zhao
Context awareness has become an important issue in recent years. However, we still do not have a formal mechanism for solving related problems due to the complexity caused by personalization, service diversity, user intension, and so on. In this paper, we investigate the possibility of solving the problems using re-definable ontology. As a concrete example, we study smart phone based automatic service recommendation. The main idea of this work is to record the user behaviors, estimate the user intention based on the recorded data, induce first-order predicate logic rules, and re-define the structure of an ontology system. The re-definable ontology system can become smarter and smarter to understand the user intention and the correlation between users, and recommend proper services in proper timing. Based on the proposed mechanism, a simple prototype system is implemented. This paper provides some preliminary test results, and points out problems for future study.
近年来,上下文感知已成为一个重要的问题。然而,由于个性化、服务多样性、用户内涵等带来的复杂性,我们仍然没有一个正式的机制来解决相关问题。本文探讨了用可重新定义本体解决这些问题的可能性。作为一个具体的例子,我们研究了基于智能手机的服务自动推荐。该工作的主要思想是记录用户行为,根据记录的数据估计用户意图,归纳一阶谓词逻辑规则,重新定义本体系统的结构。可重新定义的本体系统可以越来越智能地理解用户意图和用户之间的相关性,并在合适的时间推荐合适的服务。基于所提出的机制,实现了一个简单的原型系统。本文给出了一些初步的试验结果,并指出了今后需要进一步研究的问题。
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引用次数: 0
Spider King: Virtual musical instruments based on microsoft Kinect 蜘蛛王:基于微软Kinect的虚拟乐器
Pub Date : 2013-11-01 DOI: 10.1109/ICAWST.2013.6765529
M. Hsu, T. Shih
Human Computer Interaction is becoming a major component in computer science related fields allowing humans to communicate with machines in very simple ways exploring new dimensions of research. Kinect, the 3D sensing device introduced by Microsoft mainly aiming computer games domain now is used in different scopes, one is being generation or controlling of sound signals producing aesthetic music. Here, in this paper, authors' experimental efforts on three virtual music instruments: Drum, Guitar and Spider King, based on Kinect sensor are presented. All three instruments virtually set the relevant sensing input areas, as an example, strings of the guitar or cymbals of the drum, then, the player controls the instrument through those virtual inputs through the Kinect. Sound control data is then generated and fed to the audio library based on the musically oriented human computer interaction gestures, composing a realtime musical expressive performance. A live performance using the presented virtual instruments was carried out at the end.
人机交互正在成为计算机科学相关领域的一个主要组成部分,允许人类以非常简单的方式与机器交流,探索新的研究维度。Kinect是微软公司推出的主要针对电脑游戏领域的三维传感设备,目前应用于不同的领域,一是产生或控制声音信号,产生美学音乐。本文介绍了基于Kinect传感器的鼓、吉他和蜘蛛王三种虚拟乐器的实验工作。这三种乐器都虚拟地设置了相关的感应输入区域,例如,吉他的弦或鼓的钹,然后,玩家通过这些虚拟输入通过Kinect来控制乐器。然后生成声音控制数据,并根据面向音乐的人机交互手势馈送到音频库,构成实时音乐表达表演。最后,利用虚拟仪器进行了现场表演。
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引用次数: 18
A study for task based recommendation system for travel navigation 基于任务的旅游导航推荐系统研究
Pub Date : 2013-11-01 DOI: 10.1109/ICAWST.2013.6765488
J. C. Hung, V. Hsu, Martin M. Weng
With the development of wireless environment, tourism marketing transfer from the traditional way to the new generation. The mobile commerce technology provides customer more directly and more timely information. Our study purpose a schema based on human portfolio combined with the global satellite positioning system (Global Positioning System; GPS) positioning function to make a proper localization and adaptive for the intelligent mobile devices in the recommendation system. First we catch the tourism position than cross match with the indicator in the Human Portfolio. This could filter the information and personalize the travel information. This provides personalized information via interactions with Smart Travel Service and Human Portfolio, a previously filtered product issues, and an interactions between tourism and provider to promote collaborative problem resolution. Furthermore, we proposed a Task Oriented Petri Net (TOPN) model to implement the award mechanism in order to provide the interaction and feedback for traveler during the journey. It's identifying the construction of different travel mission from users area and find a suitable mission for user. Meanwhile, an adaptive recommendation algorithm, complied with context-awareness and ambient-awareness, also provide an adaptive and omnibearing travel route planning for different user even from the same place.
随着无线环境的发展,旅游营销从传统方式向新一代方式转移。移动商务技术为客户提供了更直接、更及时的信息。我们的研究目的是建立一个基于人类投资组合与全球卫星定位系统(global positioning system;GPS)定位功能,对推荐系统中的智能移动设备进行适当的定位和自适应。首先,我们抓住旅游业的位置,然后与人力投资组合中的指标交叉匹配。这可以过滤信息并个性化旅游信息。它通过与智能旅游服务和人类投资组合的互动提供个性化信息,之前过滤的产品问题,以及旅游业和供应商之间的互动,以促进合作解决问题。此外,我们提出了一个面向任务的Petri网(TOPN)模型来实现奖励机制,以便在旅途中为出行者提供互动和反馈。从用户区域识别不同出行任务的构建,找到适合用户的出行任务。同时,一种符合上下文感知和环境感知的自适应推荐算法,也为不同用户在同一地点提供了自适应的全方位出行路线规划。
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引用次数: 3
Improved stability criteria of ADP control for efficient context-aware decision support systems 改进的ADP控制的稳定性标准,用于高效的上下文感知决策支持系统
Pub Date : 2013-11-01 DOI: 10.1109/ICAWST.2013.6765406
Yury Sokolov, R. Kozma
This paper addresses the issue of stability of approximate dynamic programming (ADP) in various sequential decision making problems, including intelligent control. We employ an ADP control algorithm that iteratively improves an internal model of the external world in the autonomous system based on its continuous interaction with the environment. Through the incremental learning process, the system becomes aware of the consequences of its action into the world. We extend previous results on stability of the ADP control to the case of general multi-layer neural network approximators. We demonstrate the benefit of our results in the control of various systems, including the cart pole balancing problem. Our results show significantly improved learning and control performance as compared to the state-of-art.
本文研究了包括智能控制在内的各种顺序决策问题中近似动态规划(ADP)的稳定性问题。我们采用了一种ADP控制算法,该算法基于自治系统与环境的持续交互,迭代地改进了外部世界的内部模型。通过渐进式学习过程,系统开始意识到其行为对世界的影响。我们将先前关于ADP控制稳定性的结果推广到一般多层神经网络逼近器的情况。我们证明了我们的结果在各种系统的控制,包括推车杆平衡问题的好处。我们的结果显示,与目前的水平相比,学习和控制性能有了显著提高。
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引用次数: 3
期刊
炎黄地理
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