Semantically enriched Massive Open Online Courses (MOOCs) platform

Massive Open Online Courses (MOOCs) are becoming an essential source of information for both students and teachers. Noticeably, MOOCs have to adapt to the fast development of new technologies; they also have to satisfy the current generation of online students. The current MOOCs’ Management Systems, such as Coursera, Udacity, edX, etc., use content management platforms where content are organized in a hierarchical structure. We envision a new generation of MOOCs that support interpretability with formal semantics by using the SemanticWeb and the online social networks. Semantic technologies support more flexible information management than that offered by the current MOOCs’ platforms. Annotated information about courses, video lectures, assignments, students, teachers, etc., can be composed from heterogeneous sources, including contributions from the communities in the forum space. These annotations, combined with legacy data, build foundations for more efficient information discovery in MOOCs’ platforms. In this article we review various Collaborative Semantic Filtering technologies for building Semantic MOOCs’ management system, then, we present a prototype of a semantic middle-sized platform implemented at Western Kentucky University that answers these aforementioned requirements.     

海量教育资源中小文件的存储研究

Hadoop作为成熟的分布式云平台,能提供可靠高效的存储服务,常用来解决大文件的存储问题,但在处理海量小文件时效率显著降低。提出了基于Hadoop的海量教育资源中小文件的存储优化方案,即利用教育资源小文件间的关联关系,将小文件合并成大文件以减少文件数量,并用索引机制访问小文件及元数据缓存和关联小文件预取机制来提高文件的读取效率。实验证明,以上方法提高了Hadoop文件系统对小文件的存取效率。     

基于粒计算与区分能力的属性约简算法?

传统的属性约简方法将整个数据集一次性装入内存,很难适应大数据背景下的数据分析。为此文中提出基于粒计算与区分能力的属性约简算法。该算法运用统计学中的分层抽样技术,拆分原始大数据集为多个样本子集(粒),在每个粒上运用属性的区分能力进行属性约简,最后将各粒约简结果进行加权融合,得到原始大数据集的属性约简结果。实验表明该算法对海量数据集进行属性约简的可行性和高效性。     

MUAVs系统的混合组织模型

分析多UAV系统的协作控制以及2种基本的组织模型及其优缺点,结合大规模UAVs(MUAVs)系统协作的复杂特征,提出适用于MUAVs系统的几种分级混合网络组织模型以及具有自适应能力的动态杂类混合组织模型。研究结果表明,该方法可有效降低MUAVs系统管理和通信复杂度,提高系统的可靠性与鲁棒性。     

Observer-invariant histopathology using genetics-based machine learning

Prostate cancer accounts for one-third of noncutaneous cancers diagnosed in US men and is a leading cause of cancer-related death. Advances in Fourier transform infrared spectroscopic imaging now provide very large data sets describing both the structural and local chemical properties of cells within prostate tissue. Uniting spectroscopic imaging data and computer-aided diagnoses (CADx), our long term goal is to provide a new approach to pathology by automating the recognition of cancer in complex tissue. The first step toward the creation of such CADx tools requires mechanisms for automatically learning to classify tissue types—a key step on the diagnosis process. Here we demonstrate that genetics-based machine learning (GBML) can be used to approach such a problem. However, to efficiently analyze this problem there is a need to develop efficient and scalable GBML implementations that are able to process very large data sets. In this paper, we propose and validate an efficient GBML technique——based on an incremental genetics-based rule learner. exploits massive parallelisms via the message passing interface (MPI) and efficient rule-matching using hardware-implemented operations. Results demonstrate that is capable of performing prostate tissue classification efficiently, making a compelling case for using GBML implementations as efficient and powerful tools for biomedical image processing.     

HDFS中海量小文件合并与预取优化方法的研究

HDFS在存储海量文件时具有明显的优势, 但在存储小文件占绝大多数的海量文件时,HDFS单个NameNode的存储架构会导致其性能严重降低。为此,提出一种基于合并思想的方案,即将小文件合并为大文件,同时建立小文件到合并文件的映射关系,并将其存于HBase中。为了提高读取速度,建立了基于LRU的预取机制。实验表明,该方法能明显提高HDFS在处理海量文件时的整体性能。     

Supporting early pruning in top-k query processing on massive data

This paper analyzes the execution behavior of “No Random Accesses” (NRA) and determines the depths to which each sorted file is scanned in growing phase and shrinking phase of NRA respectively. The analysis shows that NRA needs to maintain a large quantity of candidate tuples in growing phase on massive data. Based on the analysis, this paper proposes a novel top-k algorithm Top-K with Early Pruning (TKEP) which performs early pruning in growing phase. General rule and mathematical analysis for early pruning are presented in this paper. The theoretical analysis shows that early pruning can prune most of the candidate tuples. Although TKEP is an approximate method to obtain the top-k result, the probability for correctness is extremely high. Extensive experiments show that TKEP has a significant advantage over NRA.     

基于构件的海量信息存储中间件的研究与实现

目前,中间件技术正成为构建大规模信息系统的主流技术。通过构件化一个已有的基于CORBA对象模型技术的海量数据存储中间件,构建了一个海量信息存储中间件。该中间件基于CORBA CCM技术实现,在保持原有中间件高性能,可扩展特性的同时,增强了其可管理性、可重用性和可维护性。     

双相机空间定位硬件系统的构成研究

本文针对交汇测量的工作原理，设计并具体实现了同步精度高，数据存储量大的测量系统，并详细介绍了该系统的硬件组成及各部分的工作原理和实现的功能，改进了现有系统工作方式的缺陷，解决了多台测量摄像机的同步工作和图像海量数据的存储难题，拓宽了此类系统的应用范围。     

Study of the dead layer in germanium for the CDMS detectors

We present new measurements on a Cryogenic Dark Matter Search (CDMS) detector with electron, neutron, and gamma sources. The measurements have been performed to investigate the dead layer of one of the CDMS Z-dependent Ionization Phonon germanium detectors. The dead layer has been studied at both charge electrodes and at different electric field intensities. We also present a method to remove the dependence of athermal phonon measurements on event position.