半监督谱聚类特征向量选择算法

对于一个K类问题,Ng-Jordan-Weiss(NJW)谱聚类算法通常采用数据规范化亲和度矩阵的前K个最大特征值对应的特征向量作为数据的一种表示。然而,对于某些模式识别问题,这K个特征向量不一定能够体现原始数据的结构。文中提出一种半监督谱聚类特征向量选择算法。该算法利用一定量的监督信息寻找能够体现数据结构的特征向量组合,进而获得优于传统谱聚类算法的聚类性能。UCI标准数据集和MNIST手写体数据集上的仿真实验验证该算法的有效性和鲁棒性。     

基于学习向量量化神经网络的软件可靠性预测

针对传统的软件可靠性预测模型在实际应用中存在预测泛化性能不佳等问题,提出一种基于学习向量量化（LVQ）神经网络的软件可靠性预测模型。首先分析了LVQ神经网络的结构特点以及它与软件可靠性预测的联系,然后运用该网络来进行软件可靠性的预测,并基于美国国家航空航天局（NASA）软件数据项目中的实例数据集,运用Matlab工具进行了仿真实验。通过与传统预测方法的对比,证明该方法具有可行性和较高的预测泛化性能。     

Early clinical outcomes after small incision lenticule extraction surgery (SMILE)

Purpose

Dry eye is known to impact on clinical outcomes after laser vision correction and the use of a newer ‘all femtosecond laser’ surgical approach may be associated with less impact on the ocular surface post-operatively. The purpose of this study was to evaluate the early clinical outcomes and tear instability after the first small incision lenticule extraction (SMILE) cases undertaken by three surgeons at a single site in the UK.

高等院校艺术设计个性化人才培养的实践教学模式

艺术设计实践教学是艺术设计专业不可缺乏的环节,学生学习和掌握知识必须通过实践来检验,精心组织艺术设计实践教学对培养个性化人才,增强学生创新能力有着重大作用。探讨高校艺术设计个性化人才培养的实践教学模式,提出设想建议开放教育内容、教育过程、教育空间,分析推进实践教学模式的实施途径。     

基于卷积神经网络与迁移学习的玉米籽粒图像分类识别

种子是农业生产最基本,最主要的生产资料。为实现玉米种子的快速鉴定识别与保护,本文提出基于卷积神经网络(Convolution Neural Network, CNN)与迁移学习相结合的玉米种子籽粒图像分类识别方法,可将预训练的CNN模型参数迁移到玉米籽粒图像分类识别任务中。实验采集了6种玉米种子籽粒图像双面图像共1 976张,包括16DX531,京粘1号,科诺58,铁研,小金黄,郑单958,建立胚面,胚乳面和双面混合的3种数据集。按照7∶2∶1的比例随机划分训练集,验证集和测试集,并对训练集图像作数据增强处理。基于4种CNN模型Xception, ResNet50V2,MobileNetV2,DenseNet121进行参数迁移学习。实验结果表明Xception与胚乳数据集建模方法优于其他方法。Xception--胚乳模型训练集与验证集平均识别准确率分别为95.55%和95.97%,测试集准确率为92.78%。基于卷积神经网络与迁移学习相结合的玉米籽粒图像识别方法切实可行。     

Studying a complex knowledge domain by exploration or explanation

Abstract  The teaching of complex knowledge domains should not be restricted to linear explanations but instead should allow the learner to travel through domains along different routes. Simulation programs provide a microworld in which the user has the freedom to explore the embedded concepts and relations by manipulating parameters and studying the resulting changes in the simulation environment. In this study two experiments were conducted to investigate the educational value of a simulation program modelling the relations between erosion and agriculture in a developing country as part of the secondary school geography curriculum. The hypothesis tested was that studying the complex domain through exploration with a simulation program decreases the retention of facts and concepts but increases performance on a problem solving test, compared with studying the same domain through traditional classroom explanation. The first experiment showed that the exploration group outperformed the explanation group on both factual knowledge and problem solving questions. A second experiment was run to control instruction time for both groups and to add a demonstration of the simulation program to the explanation condition. Under these conditions there were no post-test differences between the explanation and the exploration group. It is hypothesized that training in how to use a simulation environment to study a domain is necessary to take full advantage of the facilities to explore the domain.     

A single-machine scheduling problem with learning effects in intermittent batch production

This paper studies a single-machine scheduling problem with three models of learning and forgetting effects in intermittent batch production. They are the models of no transmission, partial transmission and total transmission of learning from batch to batch. The phenomena exist in many realistic production systems. The objective is to minimize the makespan. We show that the problems with the models of no transmission and partial transmission of learning from batch to batch are polynomially solvable. We also provide two polynomial time algorithms for two special cases in the problem with the total transmission model.     

A theory of online learning as online participation

In this paper, an initial theory of online learning as online participation is suggested. It is argued that online learner participation (1) is a complex process of taking part and maintaining relations with others, (2) is supported by physical and psychological tools, (3) is not synonymous with talking or writing, and (4) is supported by all kinds of engaging activities. Participation and learning are argued to be inseparable and jointly constituting. The implication of the theory is straightforward: If we want to enhance online learning, we need to enhance online learner participation.     

Three optimizations for Assume–Guarantee reasoning with L<Superscript>*</Superscript>

The learning-based automated Assume–Guarantee reasoning paradigm has been applied in the last few years for the compositional verification of concurrent systems. Specifically, L^* has been used for learning the assumption, based on strings derived from counterexamples, which are given to it by a model-checker that attempts to verify the Assume–Guarantee rules. We suggest three optimizations to this paradigm. First, we derive from each counterexample multiple strings to L^*, rather than a single one as in previous approaches. This small improvement saves candidate queries and hence model-checking runs. Second, we observe that in existing instances of this paradigm, the learning algorithm is coupled weakly with the teacher. Thus, the learner completely ignores the details of the internal structure of the system and specification being verified, which are available already to the teacher. We suggest an optimization that uses this information in order to avoid many unnecessary membership queries (it reduces the number of such queries by more than an order of magnitude). Finally, we develop a method for minimizing the alphabet used by the assumption, which reduces the size of the assumption and the number of queries required to construct it. We present these three optimizations in the context of verifying trace containment for concurrent systems composed of finite state machines. We have implemented our approach in the ComFoRT tool, and experimented with real-life examples. Our results exhibit an average speedup of between 4 to 11 times, depending on the Assume–Guarantee rule used and the set of activated optimizations. This research was supported by the Predictable Assembly from Certifiable Components (PACC) initiative at the Software Engineering Institute, Pittsburgh.     

The interactive use of a video database in teacher education: Creating a knowledge base for teaching through a learning community

This study explores how pre-service student teachers acquired knowledge of good teaching practices through the interactive use of a video database and an online discussion forum, where the student teachers shared their teaching videos and received comments or suggestions from members of a learning community. A small group of student teachers was involved in this collaborative learning community during their teaching practicum in their third and fourth years of study. Content analysis of the sharing in the discussion forum and individual student teacher reflections provides insight into the use of a collaborative learning community to create a knowledge base for teaching. Suggestions regarding how this new technology can support teacher education are discussed.