Ensemble of multiple instance classifiers for image re-ranking

Text-based image retrieval may perform poorly due to the irrelevant and/or incomplete text surrounding the images in the web pages. In such situations, visual content of the images can be leveraged to improve the image ranking performance. In this paper, we look into this problem of image re-ranking and propose a system that automatically constructs multiple candidate “multi-instance bags (MI-bags)”, which are likely to contain relevant images. These automatically constructed bags are then utilized by ensembles of Multiple Instance Learning (MIL) classifiers and the images are re-ranked according to the final classification responses. Our method is unsupervised in the sense that, the only input to the system is the text query itself, without any user feedback or annotation. The experimental results demonstrate that constructing multiple instance bags based on the retrieval order and utilizing ensembles of MIL classifiers greatly enhance the retrieval performance, achieving on par or better results compared to the state-of-the-art.     

Exploring teachers' perceptions on different CSCL script editing tools

Despite the apparent maturity of the learning design field, and the variety of tooling available to support it, adoption among the teacher community (one of its alleged main targets) is still low. There is a lack of research on teachers' perception and use of different technological learning design tools, as existing evaluations are often restricted to a single tool. In order to explore whether there are common factors hampering teacher adoption, and which tool features might appeal to different teachers, more studies involving multiple authoring tools are needed. This paper provides a first step in this direction, describing a mixed methods study performed around a professional development workshop with 18 university teachers from multiple disciplines. This workshop exposed teachers to two different authoring tools (WebCollage and EDIT2), as they learned to create computer-supported collaborative learning (CSCL) designs and implement them. The findings of our interpretive study (which included questionnaires, observations, or group discussion recordings) support the idea that there is no single tool or set of features that are globally perceived as better, although our evidence also highlights certain factors as important for participant teachers – amongst others, the integration of learning designs with the ICT platforms for enactment, as well as with other tools that they already use in their everyday practice.     

Recommending suitable learning scenarios according to learners’ preferences: An improved swarm based approach

The paper presents a new approach for recommending suitable learning paths for different learners groups. Selection of the learning path is considered as recommendations to choosing and combining the sequences of learning objects (LOs) according to learners’ preferences. Learning path can be selected by applying artificial intelligence techniques, e.g. a swarm intelligence model. If we modify and/or change some LOs in the learning path, we should rearrange the alignment of new and old LOs and reallocate pheromones to achieve effective learning recommendations. To solve this problem, a new method based on the ant colony optimisation algorithm and adaptation of the solution to the changing optimum is proposed. A simulation process with a dynamic change of learning paths when new LOs are inserted was chosen to verify the method proposed. The paper contributes with the following new developments: (1) an approach of dynamic learning paths selection based on swarm intelligence, and (2) a modified ant colony optimisation algorithm for learning paths selection. The elaborated approach effectively assist learners by helping them to reach most suitable LOs according to their preferences, and tutors – by helping them to monitor, refine, and improve e-learning modules and courses according to the learners’ behaviour.     

Emissions control policies under uncertainty and rational learning in a linear-state dynamic model

We compare the use of price-based policies or taxes, and quantity-based policies or quotas, for controlling emissions in a dynamic setup when the regulator faces two sources of uncertainty: (i) market-related uncertainty; and (ii) ecological uncertainty. We assume that the regulator is a rational Bayesian learner and the regulator and firms have asymmetric information. In our model the structure of Bayesian learning is general. Our results suggest that the expected level of emissions is the same under taxes and quotas. However, the comparison of the total benefits related to these policies suggests that taxes dominate quotas, that is, they provide a higher social welfare. Even though taxes have some benefits over quotas, neither learning nor ecological uncertainty affect the choice of policy, i.e., the only factor having such an impact is uncertainty in the instantaneous net emissions benefits (market-related uncertainty). Besides, the more volatile is this uncertainty, the more benefits of taxes over quotas. Ecological uncertainty leads to a difference between the emissions rule under the informed and the rational learning assumptions. However, the direction of this difference depends on the beliefs bias with regard to ecological uncertainty. We also find that a change in the regulator’s beliefs toward more optimistic views will increase the emissions.     

Implementation of Web‐based argumentation in facilitating elementary school students to learn environmental issues

This research develops a Web‐based argumentation system named the Web‐based Interactive Argumentation System (WIAS). WIAS can provide teachers with the scaffolding for argumentation instruction. Students can propose their statements, collect supporting evidence and share and discuss with peers online. This research adopts a quasi‐experimental design, applying WIAS to the teaching of environmental issues, including mudslides, global warming and nuclear power. Fifty‐seven elementary school fifth graders from two classes participated in this research. With each class as a unit, they were divided into the WIAS group (n = 30) and the traditional argumentation instruction (TAI) group (n = 27). Before research, all students took the pre‐test of the ‘achievement test for environmental issues (ATEI)’ and the ‘environmental literacy scale (ELS).’ Then all students received argumentation training and six classes of argumentation instruction. Students in the WIAS group performed argumentation in the WIAS, while those in the TAI group performed argumentation in a traditional classroom. After the six‐class argumentation instruction, all students took the post‐test of the ATEI and ELS. The results show that students in the WIAS group have significantly better learning effectiveness than those in the TAI group. Students in the WIAS group also exhibited significantly better improvement in their environmental literacy.     

JITL-based concentration control for semi-batch pH-shift reactive crystallization of l-glutamic acid

Although concentration control (C-control) strategy has been shown to give effective and robust control performance for batch cooling and semi-batch antisolvent crystallizations in recent years, no research work was reported concerning the potential application of conventional C-control for the more challenging semi-batch pH-shift reactive crystallization that is common in the process industries. To this end, this paper presents detailed analysis to find out that it is not feasible to apply the C-control to semi-batch pH-shift reactive crystallization. To circumvent this problem, a variant of C-control strategy by incorporating the Just-in-Time Learning (JITL) method to cope with strong process nonlinearity inherent in the pH-shift reactive crystallization is developed in this paper. Simulation results are presented to illustrate the proposed design and a comparison with conventional optimal control is made.     

极限训练机在矿山开采最大下沉值估算中的应用

矿山开采过程中采空区地表往往会发生形变,研究其最大下沉值对安全生产具有重要意义,现有的开采最大下沉估算方法还有提升的空间。基于岩移数据决策与极限训练机(Extreme learning machine,ELM)算法,提出了一种估计最大下沉的新方法。该方法将采厚、倾角、平均采深、走向长度、倾向长度和覆岩岩性确定为最大下沉值影响因素;应用以Sigmoid方程为核函数、隐含层神经元个数为114的ELM模型对最大下沉值进行了估算。通过案例分析,ELM模型得到了优于传统算法如CHAID、Boosted Tree、ANN、BPNN和SVM的RMSE、MAE、MAPE、最大残差及秩相关系数,故认为该模型是一种有效的矿山开采最大下沉估算方法。     

Using the cloud to develop applications supporting geo-collaborative Situated Learning

Situated Learning stresses the importance of the context in which learning takes place. It has been therefore frequently associated with informal learning or learning outside the classroom. Cloud technologies can play an important role supporting this type of learning, since it requires ubiquitous computing support, connectivity and access to data across various scenarios: on the field, in the classroom, at home, etc. In this paper we first present the situated learning theory and how we can take advantage of services offered by Cloud Computing to implement computer applications implementing learning activities based on this theory, providing pertinent geographical information and discussion boards. Next we propose a software architecture schema which can be used as a basis for integrating existing cloud services into new applications supporting learning activities. Then we present two examples developed with this approach with its viability and advantages. These are discussed in the concluding chapter.     

基于奇偶树型交互学习机与遗传算法的密文优化系统

为使图像加密系统具备优化功能,并解决当前遗传算法无法实现全局最优、收敛速率慢等问题,提出奇偶树型交互学习机耦合全局离散遗传算法的密文优化系统。定义权值更新机制,耦合混沌映射,构造奇偶树型交互学习机及其互扰模型。将切断型轮盘赌择取机制引入均匀交叉算子中,以图像分块的相邻像素相关系数和密文信息熵为目标,根据权重理论设计加权适应度函数,提出一种全局离散遗传算法,最终形成"初始加密-密文优化"的加密结构。实验结果表明,与超混沌算法、离散遗传算法、元胞自动机相比,该系统的加密质量较好,并且具备全局优化功能,可优化所有迭代结果,使最终输出密文的信息熵最大,相关系数最小。     

JiTT—Blending Learning理念下的C++课程特色网站开发

Ji TT充分体现了Blending Learning的本质特点,把传统学习方式的优势和E-Learning(即网络化学习)的优势结合起来。Ji TT—Blending Learning理念下的C++课程特色网站突破了时间和空间的限制,将多种教学媒体、资源、情境混合,建立了课堂学习和网络学习相结合的模式,给教师和学生提供了一个方便、高效的通过网络教与学C++课程的平台。