An empirical evaluation of multi-media based learning of a procedural task

The present study investigated the effects of multi-media modules and their combinations on the learning of procedural tasks. In the experiment, 72 participants were classified as having either low- or high spatial ability based on their spatial ability test. They were randomly assigned to one of the six experimental conditions in a 2 × 3 factorial design with verbal modality (on-screen text procedure vs. auditory procedure) and the format of visual representation (static visual representation vs. static visual representation with motion cues vs. animated visual representation). After they completed their learning session, the ability to perform the procedural task was directly measured in a realistic setting. The results revealed that: (1) in the condition of static visual representation, the high spatial ability group outperformed the low spatial ability group, (2) for the low spatial ability participants, the animated visual representation group outperformed the static visual representation group, however, the static visual representation with motion cues group did not outperform the static visual representation group, (3) the use of animated visual representation helped participants with low spatial ability more than those with high spatial ability, and (4) a modality effect was found for the measure of satisfaction when viewing the animated visual representation. Since the participants with low spatial ability benefited from the use of animation, the results might support an idea that people are better able to retrieve the procedural information by viewing animated representation. The findings also might reflect a preference for the auditory mode of presentation with greater familiarity with the type of visual representation.     

Remote sensing image classification based on neural network ensemble algorithm

The amounts and types of remote sensing data have increased rapidly, and the classification of these datasets has become more and more overwhelming for a single classifier in practical applications. In this paper, an ensemble algorithm based on Diversity Ensemble Creation by Oppositional Relabeling of Artificial Training Examples (DECORATEs) and Rotation Forest is proposed to solve the classification problem of remote sensing image. In this ensemble algorithm, the RBF neural networks are employed as base classifiers. Furthermore, interpolation technology for identical distribution is used to remold the input datasets. These remolded datasets will construct new classifiers besides the initial classifiers constructed by the Rotation Forest algorithm. The change of classification error is used to decide whether to add another new classifier. Therefore, the diversity among these classifiers will be enhanced and the accuracy of classification will be improved. Adaptability of the proposed algorithm is verified in experiments implemented on standard datasets and actual remote sensing dataset.     

Multi-level delegations with trust management in access control systems

Delegation is a mechanism that allows one agent to act on another’s privilege. It is important that the privileges should be delegated to a person who is trustworthy. In this paper, we propose a multi-level delegation model with trust management in access control systems. We organize the delegation tasks into three levels, Low, Medium, and High, according to the sensitivity of the information contained in the delegation tasks. It motivates us that the more sensitive the delegated task is, the more trustworthy the delegatee should be. In order to assess how trustworthy a delegatee is, we devise trust evaluation techniques to describe a delegatee’s trust history and also predict the future trend of trust. In our proposed delegation model, a delegatee with a higher trust level could be assigned with a higher level delegation task. Extensive experiments show that our proposed multi-level delegation model is effective in accurately predicting trust and avoiding sensitive information disclosure.     

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.     

Time-of-flight sensor and color camera calibration for multi-view acquisition

This paper presents a multi-view acquisition system using multi-modal sensors, composed of time-of-flight (ToF) range sensors and color cameras. Our system captures the multiple pairs of color images and depth maps at multiple viewing directions. In order to ensure the acceptable accuracy of measurements, we compensate errors in sensor measurement and calibrate multi-modal devices. Upon manifold experiments and extensive analysis, we identify the major sources of systematic error in sensor measurement and construct an error model for compensation. As a result, we provide a practical solution for the real-time error compensation of depth measurement. Moreover, we implement the calibration scheme for multi-modal devices, unifying the spatial coordinate for multi-modal sensors. The main contribution of this work is to present the thorough analysis of systematic error in sensor measurement and therefore provide a reliable methodology for robust error compensation. The proposed system offers a real-time multi-modal sensor calibration method and thereby is applicable for the 3D reconstruction of dynamic scenes.     

Balancing energy consumption with mobile agents in wireless sensor networks

For Wireless Sensor Networks (WSNs), an unbalanced energy consumption will decrease the lifetime of network. In this paper, we leverage mobile agent technology to investigate the problem of how to balance the energy consumption during data collection in WSNs. We first demonstrate that for a sensor network with uniform node distribution and constant data reporting, balancing the energy of the whole network cannot be realized when the distribution of data among sensor nodes is unbalanced. We design a method to mitigate the uneven energy dissipation problem by controlling the mobility of agents, which is achieved by an energy prediction strategy to find their positions. Finally, we propose energy balancing cluster routing based on a mobile agent (EBMA) for WSNs. To obtain better performance, the cluster structure is formed based on cellular topology taking into consideration the energy balancing of inter-cluster and intra-cluster environments. Extensive simulation experiments are carried out to evaluate EBMA with several performance criteria. Our simulation results show that EBMA can effectively balance energy consumption and perform high efficiency in large-scale network deployment.     

A Stylized Approach for Pencil Drawing from Photographs

We present a stylized scheme that produces pencil drawings in a range of styles from an image. To produce controllable pencil drawing effects and remedy the problems of existing convolution‐based schemes, we develop a swing bilateral LIC (SBL) filter. Our first approach to express the styled pencil drawings is to control the directions of pencil strokes that depicts both shapes and smooth tone. Another approach is to produce colors of pencil drawings by sampling colors from real color pencils. The third approach is to mimic the artistic technique that increases the details of drawings in a progressive manner. We present drawings in several styles and compare some of them directly with illustrations taken from an artists' work.     

A new view of fuzzy ideals in \Upgamma-rings

By means of a kind of new idea, we redefine fuzzy ideals and fuzzy interior ideals in a $\Upgamma$ -ring and investigate some of their related properties. In particular, we show that the regular and semisimple $\Upgamma$ -rings can be described by using these kinds of generalized fuzzy ideals.     

PTA和CHDM改性聚丁二酸丁二醇酯的性能比较研究

将含有苯环的对苯二甲酸(PTA)和六元环的1,4-环己烷二甲醇(1,4-CHDM)引入PBS分子主链中,得到PBS-co-PTA和PBS-co-CHDM的无规共聚物.1 H-NMR鉴定了共聚物为预期产物,并对共聚物的热性能、结晶性和力学性能进行了对比研究.结果表明:PTA和1,4-CHDM改性后共聚物的结晶度变化截然不同,随着PTA含量的增加,PBS-co-PTA的结晶度、结晶尺寸逐渐增大,同时Tg逐渐降低;而随1,4-CHDM含量的增加,PBS-co-CHDM的结晶度和结晶尺寸却减小,Tg逐渐上升.当1,4-CHDM增至30%时,其断裂伸长率可达到PBS的3倍,证明了共聚物PBS-co-CHDM比PBS-co-PTA具有更好的柔韧性.