An autonomous service‐platform to support distributed ontology‐based context‐aware agents

The use of semantic technology has recently witnessed a huge increase. One of the areas in which this technology is being used increasingly more often is that of context‐aware agents. However, the use of ontologies in general and reasoning in particular can rapidly become resource intensive. Certainly if the data set, called the A‐Box, used by these agents grows considerably over time. Moreover, in order to create context‐aware applications, taking into account a wide range of different data sets and context parameters, agents have to be provided to expose that data. The collaboration between the agents in the system is necessary to correlate the information and augment the intelligence and added value of the context‐aware agents. Therefore, there is a need to have a distributed approach by means of a service‐platform, where the different agents in a context‐aware environment can collaborate. The main focus of this article is on the research on the design of a service‐platform for semantic ontology‐based context‐aware collaboration. The platform architecture to allow the collaboration and scheduling, together with the associated algorithms, will be presented. The engineering and implementation details will be highlighted. By means of detailed UML sequence diagrams, we will present the workflow and collaboration between the different modules in the platform. Additionally, supporting developments, such as the meta‐ontology and our ontology generator, OTAGen, will be presented. Furthermore, we will detail how the platform can operate in an autonomous way, taking into account the changing context of the agents in the platform.     

A cost‐efficient resource provisioning algorithm for DHT‐based cloud storage systems

Personal cloud storage provides users with convenient data access services. Service providers build distributed storage systems by utilizing cloud resources with distributed hash table (DHT), so as to enhance system scalability. Efficient resource provisioning could not only guarantee service performance, but help providers to save cost. However, the interactions among servers in a DHT‐based cloud storage system depend on the routing process, which makes its execution logic more complicated than traditional multi‐tier applications. In addition, production data centers often comprise heterogeneous machines with different capacities. Few studies have fully considered the heterogeneity of cloud resources, which brings new challenges to resource provisioning. To address these challenges, this paper presents a novel resource provisioning model for service providers. The model utilizes queuing network for analysis of both service performance and cost estimation. Then, the problem is defined as a cost optimization with performance constraints. We propose a cost‐efficient algorithm to decompose the original problem into a sub‐optimization one. Furthermore, we implement a prototype system on top of an infrastructure platform built with OpenStack. It has been deployed in our campus network. Based on real‐world traces collected from our system and Dropbox, we validate the efficiency of our proposed algorithms by extensive experiments. Copyright © 2016 John Wiley & Sons, Ltd.     

A task‐based and resource‐aware approach to dynamically generate optimal software architecture for intelligent service robots

Intelligent service robots provide various services to users by understanding the context and goals of a user task. In order to provide more reliable services, intelligent service robots need to consider various factors, such as their surrounding environments, users' changing needs, and constrained resources. To handle these factors, most of the intelligent service robots are controlled by a task‐based control system, which generates a task plan that represents a sequence of actions, and executes those actions by invoking the corresponding functions. However, the traditional task‐based control systems lack the consideration of resource factors even though intelligent service robots have limited resources (limited computational power, memory space, and network bandwidth). Moreover, system‐specific concerns such as the relationships among functional modules are not considered during the task generation phase. Without considering both the resource conditions and interdependencies among software modules as a whole, it will be difficult to efficiently manage the functionalities that are essential to provide core services to users. In this paper, we propose a mechanism for intelligent service robots to efficiently use their resources on‐demand by separating system‐specific information from task generation. We have defined a sub‐architecture that corresponds to each action of a task plan, and provides a way of using the limited resources by minimizing redundant software components and maintaining essential components for the current action. To support the optimization of resource consumption, we have developed a two‐phase optimization process, which is composed of the topological and temporal optimization steps. We have conducted an experiment with these mechanisms for an infotainment robot, and simulated the optimization process. Results show that our approach contributed to increase the utilization rate by 20% of the robot resources. Copyright © 2011 John Wiley & Sons, Ltd.     

Collaborative recommendation of e‐learning resources: an experimental investigation

Repositories with educational resources can support the formation of online learning communities by providing a platform for collaboration. Users (e.g. teachers, tutors and learners) access repositories, search for interesting resources to access and use, and in many cases, also exchange experiences and opinions. A particular class of online services that take advantage of the collected knowledge and experience of users are collaborative filtering ones. The successful operation of such services in the context of real‐life applications requires careful testing and parameterization before their actual deployment. In this paper, the case of developing a learning resources' collaborative filtering service for an online community of teachers in Europe was examined. More specifically, a data set of evaluations of learning resources was collected from the teachers that use the European Schoolnet's learning resource portal. These evaluations were then used to support the experimental investigation of design choices for an online collaborative filtering service for the portal's learning resources. A candidate multi‐attribute utility collaborative filtering algorithm was appropriately parameterized and tested for this purpose. Results indicated that the development of such systems should be taking place considering the particularities of the actual communities that are to be served.     

Neural network‐based multi‐agent approach for scheduling in distributed systems

A distributed system consists of a collection of autonomous heterogeneous resources that provide resource sharing and a common platform for running parallel compute‐intensive applications. The different application characteristics combined with the heterogeneity and performance variations of the distributed system make it difficult to find the optimal set of needed resources. When deployed, user applications are usually handled by application domain experts or system administrators who depending on the infrastructure provide a scheduling strategy for selecting the best candidate resource over a set of available resources. However, the provided strategy is usually generic, aimed at handling a wide array of applications and does not take into consideration specific application resource requirements. As such, an intelligent method for selecting the best resources based on expert knowledge is needed. In this paper, we propose a neural network‐based multi‐agent resource selection technique capable of mimicking the services of an expert user. In addition, to cope with the geographical distribution of the underlying system, we employ a multi‐agent coordination mechanism. The proposed neural network‐based scheduling framework combined with the multi‐agent intelligence is a unique approach to efficiently deal with the resource selection problem. Results run on a simulated environment show the efficiency of our proposed method. Several scheduling simulations were conducted to compare the performance of some conventional resource selection methods against the proposed agent‐based neural network technique. The results obtained indicate that the agent‐based approach outperformed the classical algorithms by reducing the amount of time required to search for suitable resources irrespective of the resource size. Copyright © 2016 John Wiley & Sons, Ltd.     

An ontology‐based mechanism for automatic categorization of web services

The addition of semantic information into Web services (WS) results in more accurate search and retrieval in service registries. The key issue to facilitate organization of services, taking into account their semantics, is the development of automatic mechanisms that generate appropriate mappings between Web service elements and their semantics‐enabled counterparts. In this paper, we introduce an ontology‐based mechanism for automatic semantic categorization of WS and their structural components. The presented approach, as opposed to similar ones, takes into account the lexicographic, structural, and data type characteristics of WS. Moreover, a software tool that implements the proposed service categorization mechanism is presented, and a benchmark process is executed that reveals outstanding performance of the developed mechanism in comparison with a relevant state‐of‐the‐art approach. Copyright © 2011 John Wiley & Sons, Ltd.     

Learning a Generative Model for Multi‐Step Human‐Object Interactions from Videos

Creating dynamic virtual environments consisting of humans interacting with objects is a fundamental problem in computer graphics. While it is well‐accepted that agent interactions play an essential role in synthesizing such scenes, most extant techniques exclusively focus on static scenes, leaving the dynamic component out. In this paper, we present a generative model to synthesize plausible multi‐step dynamic human‐object interactions. Generating multi‐step interactions is challenging since the space of such interactions is exponential in the number of objects, activities, and time steps. We propose to handle this combinatorial complexity by learning a lower dimensional space of plausible human‐object interactions. We use action plots to represent interactions as a sequence of discrete actions along with the participating objects and their states. To build action plots, we present an automatic method that uses state‐of‐the‐art computer vision techniques on RGB videos in order to detect individual objects and their states, extract the involved hands, and recognize the actions performed. The action plots are built from observing videos of everyday activities and are used to train a generative model based on a Recurrent Neural Network (RNN). The network learns the causal dependencies and constraints between individual actions and can be used to generate novel and diverse multi‐step human‐object interactions. Our representation and generative model allows new capabilities in a variety of applications such as interaction prediction, animation synthesis, and motion planning for a real robotic agent.     

基于语义空间距离的相似问句识别方法研究

前沿相关研究将相似问句识别转化为二元问句匹配识别并取得很大进展。但是在自动问答系统的实际应用场景中存在大量数据,这些方法受限于二元问句匹配识别模式,导致时效性不高。针对这一问题,受人脸识别相关研究的启发,该文提出基于语义空间距离衡量的相似问句识别方法（Semantic Space Distance Method,SSDM）。该方法将相似问句识别作为多分类问题进行训练,通过利用人脸识别任务中Margin Softmax损失函数得到语义编码模型。该语义编码模型能够将相似问句在语义空间中聚合,不相似问句在语义空间中远离。SSDM方法将相似问句识别转化成语义空间中的向量距离计算,突破二元问句匹配的方式,保证了一定的高时效性,并且仍然能够在深层语义层面对相似问句进行识别。该方法在Biendata的ASQD数据集中实验测试,取得了比基线方法更优的性能,验证了SSDM方法的有效性。     

基于语义向量模型的Web服务匹配方法*

针对当前Web服务匹配方法中存在的缺陷,将语义Web服务匹配方法与信息检索技术相结合,提出一种利用语义相似度扩展向量模型的Web服务匹配方法,并通过实验对匹配效果进行了检验与分析。实验结果证明,与基于经典向量模型的匹配方法和相关语义匹配方法相比,该匹配方法具有更高的准确率。     

基于资源本体的Web服务发现与组合研究

将语义Web技术引入Web服务研究,提出一种基于资源本体的Web服务发现和组合方法。通过分析资源之间关系,建立了基于上下位资源查找的Web服务发现方法;通过分析资源与服务的输入输出关系,建立了一种逐级查找的Web服务组合方法。与基于关键字和Web服务语义的Web服务发现和组合方法相比,基于资源本体的方法能较好地满足服务查全率和查准率的要求,具有较高的组合质量和效率,非常适合与资源密切相关的Web服务。     

Multi‐targets localization and elliptical circumnavigation by multi‐agents using bearing‐only measurements in two‐dimensional space

In order to enable the multi‐agents to elliptically circumnavigate the multi‐targets in more complex environment, we propose a geometric center estimator and an elliptical circumnavigation controller in two‐dimensional space by only employing bearing measurements without knowing the target's position and velocity. The stability of the algorithm is proved for both stationary targets and dynamic targets. Finally, a series of numerical simulations is presented to verify the correctness of the algorithm both in ideal networks and in networks with communication delays.     

Projected Field Similarity for Comparative Visualization of Multi‐Run Multi‐Field Time‐Varying Spatial Data

The purpose of multi‐run simulations is often to capture the variability of the output with respect to different initial settings. Comparative analysis of multi‐run spatio‐temporal simulation data requires us to investigate the differences in the dynamics of the simulations' changes over time. To capture the changes and differences, aggregated statistical information may often be insufficient, and it is desirable to capture the local differences between spatial data fields at different times and between different runs. To calculate the pairwise similarity between data fields, we generalize the concept of isosurface similarity from individual surfaces to entire fields and propose efficient computation strategies. The described approach can be applied considering a single scalar field for all simulation runs or can be generalized to a similarity measure capturing all data fields of a multi‐field data set simultaneously. Given the field similarity, we use multi‐dimensional scaling approaches to visualize the similarity in two‐dimensional or three‐dimensional projected views as well as plotting one‐dimensional similarity projections over time. Each simulation run is depicted as a polyline within the similarity maps. The overall visual analysis concept can be applied using our proposed field similarity or any other existing measure for field similarity. We evaluate our measure in comparison to popular existing measures for different configurations and discuss their advantages and limitations. We apply them to generate similarity maps for real‐world data sets within the overall concept for comparative visualization of multi‐run spatio‐temporal data and discuss the results.     

Depth‐map generation for multi‐view autostereoscopic 3‐D displays based on the SIFT algorithm constrained by boundary

Abstract— A depth‐map estimation method, which converts two‐dimensional images into three‐dimensional (3‐D) images for multi‐view autostereoscopic 3‐D displays, is presented. The proposed method utilizes the Scale Invariant Feature Transform (SIFT) matching algorithm to create the sparse depth map. The image boundaries are labeled by using the Sobel operator. A dense depth map is obtained by using the Zero‐Mean Normalized Cross‐Correlation (ZNCC) propagation matching method, which is constrained by the labeled boundaries. Finally, by using depth rendering, the parallax images are generated and synthesized into a stereoscopic image for multi‐view autostereoscopic 3‐D displays. Experimental results show that this scheme achieves good performances on both parallax image generation and multi‐view autostereoscopic 3‐D displays.     

Multi‐agent rotating consensus without relative velocity measurements in three‐dimensional space

In this paper, we study distributed rotating consensus in networks of second‐order agents using only local position information in three‐dimensional space. We propose a protocol to make the multi‐agent system finally move together around a common point and present corresponding conditions for rotating consensus of the system. Finally, a numerical example is provided to demonstrate the effectiveness of the theoretical results. Copyright © 2011 John Wiley & Sons, Ltd.     

Visual Analysis of Multi‐Dimensional Categorical Data Sets

We present a set of interactive techniques for the visual analysis of multi‐dimensional categorical data. Our approach is based on multiple correspondence analysis (MCA), which allows one to analyse relationships, patterns, trends and outliers among dependent categorical variables. We use MCA as a dimensionality reduction technique to project both observations and their attributes in the same 2D space. We use a treeview to show attributes and their domains, a histogram of their representativity in the data set and as a compact overview of attribute‐related facts. A second view shows both attributes and observations. We use a Voronoi diagram whose cells can be interactively merged to discover salient attributes, cluster values and bin categories. Bar chart legends help assigning meaning to the 2D view axes and 2D point clusters. We illustrate our techniques with real‐world application data.     

Fuzzy multi‐attribute cost–benefit analysis of e‐services

E‐service evaluation is a complex problem in which many qualitative attributes must be considered. These kinds of attributes make the evaluation process hard and vague. Cost–benefit analyses applied to various areas are usually based on the data under certainty or risk. In case of uncertain, vague, and/or linguistic data, the fuzzy set theory can be used to handle the analysis. In this article, after the evaluation attributes of e‐services and the fuzzy multi‐attribute decision‐making methods are introduced, a fuzzy hierarchical TOPSIS model is developed and applied to an e‐service provider selection problem with some sensitivity analyses. The developed model is a useful tool for the companies that prefer outsourcing for e‐activities. It is shown that service systems can be effectively evaluated by the proposed method. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 547–565, 2007.     

Dynamic Multi‐View Exploration of Shape Spaces

Statistical shape modeling is a widely used technique for the representation and analysis of the shapes and shape variations present in a population. A statistical shape model models the distribution in a high dimensional shape space, where each shape is represented by a single point. We present a design study on the intuitive exploration and visualization of shape spaces and shape models. Our approach focuses on the dual‐space nature of these spaces. The high‐dimensional shape space represents the population, whereas object space represents the shape of the 3D object associated with a point in shape space. A 3D object view provides local details for a single shape. The high dimensional points in shape space are visualized using a 2D scatter plot projection, the axes of which can be manipulated interactively. This results in a dynamic scatter plot, with the further extension that each point is visualized as a small version of the object shape that it represents. We further enhance the population‐object duality with a new type of view aimed at shape comparison. This new “shape evolution view” visualizes shape variability along a single trajectory in shape space, and serves as a link between the two spaces described above. Our three‐view exploration concept strongly emphasizes linked interaction between all spaces. Moving the cursor over the scatter plot or evolution views, shapes are dynamically interpolated and shown in the object view. Conversely, camera manipulation in the object view affects the object visualizations in the other views. We present a GPU‐accelerated implementation, and show the effectiveness of the three‐view approach using a number of real‐world cases. In these, we demonstrate how this multi‐view approach can be used to visually explore important aspects of a statistical shape model, including specificity, compactness and reconstruction error.     

Real‐Time Multi‐View Facial Capture with Synthetic Training

We present a real‐time multi‐view facial capture system facilitated by synthetic training imagery. Our method is able to achieve high‐quality markerless facial performance capture in real‐time from multi‐view helmet camera data, employing an actor specific regressor. The regressor training is tailored to specified actor appearance and we further condition it for the expected illumination conditions and the physical capture rig by generating the training data synthetically. In order to leverage the information present in live imagery, which is typically provided by multiple cameras, we propose a novel multi‐view regression algorithm that uses multi‐dimensional random ferns. We show that higher quality can be achieved by regressing on multiple video streams than previous approaches that were designed to operate on only a single view. Furthermore, we evaluate possible camera placements and propose a novel camera configuration that allows to mount cameras outside the field of view of the actor, which is very beneficial as the cameras are then less of a distraction for the actor and allow for an unobstructed line of sight to the director and other actors. Our new real‐time facial capture approach has immediate application in on‐set virtual production, in particular with the ever‐growing demand for motion‐captured facial animation in visual effects and video games.