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.     

Iterative equalization combining algorithm in intersymbol interference channel

To realize joint optimization of spatial diversity and equalization combining in the intersymbol interference (ISI) channel, an iterative equalization combining algorithm is proposed. The proposed algorithm uses the coefficients of Turbo equalization to calculate the combination weights without estimating the signal to noise ratio in each diversity branch. The equalized symbols from different diversity branches are combined, and the extrinsic information output from the decoder is fed back to the equalizers, so as to exchange soft information between the equalizers and the decoder. The performance of the proposed algorithm is analyzed using the extrinsic information transfer (EXIT) chart and verified by simulations. Results show that our approach fully exploits time domain information from the multipath channel and spatial domain information from multi receiving antennas, which efficiently improve the performance of the receiver in the severe ISI channel.     

Micro- and macroergonomic changes in mental workload and medication safety following the implementation of new health IT

Health information technology (IT) is a promising way to achieve safer medication management in the delivery of healthcare. However, human factors/ergonomics dictates that in order to make the complex, cognitive work of healthcare delivery safer, health IT must properly support human cognition. This means, for example, that new health IT must reduce, not increase, workload during safety-critical tasks. The present study was the first to quantitatively assess the short- and long-term impact of bar coded medication administration (BCMA) IT on nurses' mental workload as well as on perceived medication safety. One-hundred seventy registered nurses across 3 dissimilar clinical units at an academic, freestanding pediatric hospital in the Midwest US participated in surveys administered before, 3 months after, and 12 months after the hospital implemented BCMA. Nurses rated their external mental workload (interruptions, divided attention, being rushed) and internal mental workload (concentration, mental effort) during medication administration tasks as well as the likelihood of each of three medication safety events: medication administration errors, medication errors on the clinical unit, and clinical unit-level adverse drug events. Clinical unit differences were assessed. Findings generally confirmed the hypothesis that external but not internal mental workload was associated with the perceived likelihood of a medication safety event. Comparisons of mental workload from pre- to post-BCMA revealed statistically significant changes in the critical care unit only. Medication safety appeared to improve over the long term in the hematology/oncology unit only. In the critical care and medical/surgical units, medication safety exhibited short-term improvements that were eliminated over time. Changes in mental workload and medication safety, two classically microergonomic constructs, were deeply embedded in macroergonomic phenomena. These included the fit between the BCMA system and the nature of nursing work, the process of BCMA implementation, and BCMA interactions with concurrent changes occurring in the hospital. Findings raise questions about achieving sustainable performance improvement with health IT as well as the balance between micro- and macroergonomic approaches to studying technology change.Relevance to industryDesigners must consider how technology changes cognitive work, including mental workload. Hospitals and other implementers of technology must ensure that new technology fits its users, their tasks, and the context of use, which may entail tailoring implementation, for example, to specific clinical units. Evaluators must look over time to assess both changes in cognitive work and implementation issues. Healthcare practitioners must also recognize that new technology means a complex transformation to an already complex sociotechnical system, which calls for a macroergonomic approach to design and analysis.     

电信运营商信息安全管控架构及集中策略研究

本文对运营商信息安全集中管控架构演进和信息安全管控策略面临的问题进行了深入分析，提出了信息安全集中管控架构的演进方向建议及对信息安全监控策略工作优化的若干意见。     

Two-level intelligent modeling method for the rate of penetration in complex geological drilling process

The rate of penetration (ROP) model is of great importance in achieving a high efficiency in the complex geological drilling process. In this paper, a novel two-level intelligent modeling method is proposed for the ROP considering the drilling characteristics of data incompleteness, couplings, and strong nonlinearities. Firstly, a piecewise cubic Hermite interpolation method is introduced to complete the lost drilling data. Then, a formation drillability (FD) fusion submodel is established by using Nadaboost extreme learning machine (Nadaboost-ELM) algorithm, and the mutual information method is used to obtain the parameters, strongly correlated with the ROP. Finally, a ROP submodel is established by a neural network with radial basis function optimized by the improved particle swarm optimization (RBFNN-IPSO). This two-level ROP model is applied to a real drilling process and the proposed method shows the best performance in ROP prediction as compared with conventional methods. The proposed ROP model provides the basis for intelligent optimization and control in the complex geological drilling process.     

M-estimator based Chinese Remainder Theorem with few remainders using a Kroenecker product based mapping vector

The Chinese Remainder Theorem (CRT) explains how to estimate an integer-valued number from the knowledge of the remainders obtained by dividing such unknown integer by co-prime integers. As an algebraic theorem, CRT is the basis for several techniques concerning data processing. For instance, considering a single-tone signal whose frequency value is above the sampling rate, the respective peak in the DFT informs the impinging frequency value modulo the sampling rate. CRT is nevertheless sensitive to errors in the remainders, and many efforts have been developed in order to improve its robustness. In this paper, we propose a technique to estimate real-valued numbers by means of CRT, employing for this goal a Kroenecker based M-Estimation (ME), specially suitable for CRT systems with low number of remainders. Since ME schemes are in general computationally expensive, we propose a mapping vector obtained via Kroenecker products which considerably reduces the computational complexity. Furthermore, our proposed technique enhances the probability of estimating an unknown number accurately even when the errors in the remainders surpass 1/4 of the greatest common divisor of all moduli. We also provide a version of the mapping vectors based on tensorial n-mode products, delivering in the end the same information of the original method. Our approach outperforms the state-of-the-art CRT methods not only in terms of percentage of successful estimations but also in terms of smaller average error.     

采用信息增益率的混合入侵检测模型设计

针对现有混合入侵检测模型仅定性选取特征而导致检测精度较低的问题，同时为了充分结合误用检测模型和异常检测模型的优势，提出一种采用信息增益率的混合入侵检测模型.首先，利用信息增益率定量地选择特征子集，最大程度地保留样本信息；其次，采用余弦时变粒子群算法确定支持向量机参数构建误用检测模型，使其更好地平衡粒子在全局和局部的搜索能力，然后，选取灰狼算法确定单类支持向量机参数构建异常检测模型，以此来提高对最优参数的搜索效率和精细程度，综合提高混合入侵检测模型对攻击的检测效果；最后，通过两种数据集进行仿真实验，验证了所提混合入侵检测模型具有较好的检测性能.     

Intelligent Information‐Centric Networking routing scheme under imprecise information

Information‐Centric Networking (ICN) has been accepted to overcome some weaknesses of the current Internet architecture, showing that “what is being exchanged” is more important than “who are exchanging information.” Given the inadequate considerations on Quality of Service (QoS) and energy saving in ICN routing, we propose in this paper a routing algorithm to enhance the two aspects. At first, on one hand, Cauchy distribution is used as a fuzzy model to evaluate users' QoS requirements, such as bandwidth, delay, and error rate; on the other hand, we formulate energy saving problem to evaluate the green quality of routing algorithm. Then, we design a link selection approach by considering QoS and energy saving, which belongs to a multi‐objective decision problem resolved by intelligent drops algorithm. Finally, we implement the proposed algorithm and compare it with the famous adaptive forwarding mechanism in terms of some significant metrics, and the experimental results reveal that the proposed algorithm is more efficient.