From Cognitive Load Theory to Collaborative Cognitive Load Theory

Cognitive load theory has traditionally been associated with individual learning. Based on evolutionary educational psychology and our knowledge of human cognition, particularly the relations between working memory and long-term memory, the theory has been used to generate a variety of instructional effects. Though these instructional effects also influence the efficiency and effectiveness of collaborative learning, be it computer supported or face-to-face, they are often not considered either when designing collaborative learning situations/environments or researching collaborative learning. One reason for this omission is that cognitive load theory has only sporadically concerned itself with certain particulars of collaborative learning such as the concept of a collective working memory when collaborating along with issues associated with transactive activities and their concomitant costs which are inherent to collaboration. We illustrate how and why cognitive load theory, by adding these concepts, can throw light on collaborative learning and generate principles specific to the design and study of collaborative learning.     

A User Study of Visualization Effectiveness Using EEG and Cognitive Load

Effectively evaluating visualization techniques is a difficult task often assessed through feedback from user studies and expert evaluations. This work presents an alternative approach to visualization evaluation in which brain activity is passively recorded using electroencephalography (EEG). These measurements are used to compare different visualization techniques in terms of the burden they place on a viewer's cognitive resources. In this paper, EEG signals and response times are recorded while users interpret different representations of data distributions. This information is processed to provide insight into the cognitive load imposed on the viewer. This paper describes the design of the user study performed, the extraction of cognitive load measures from EEG data, and how those measures are used to quantitatively evaluate the effectiveness of visualizations.     

Measuring Cognitive Load Using Linguistic Features: Implications for Usability Evaluation and Adaptive Interaction Design

An intelligent adaptable system, aware of a user’s experienced cognitive load, may help improve performance in complex, time-critical situations by dynamically deploying more appropriate output strategies to reduce cognitive load. However, measuring a user’s cognitive load robustly, in real-time is not a trivial task. Many research studies have attempted to assess users’ cognitive load using different measurements, but these are often unsuitable for deployment in real-life applications due to high intrusiveness. Relatively novel linguistic behavioral features as potential indices of user’s cognitive load is proposed. These features may be collected implicitly and nonintrusively supporting real-time assessment of users’ cognitive load and accordingly allowing adaptive usability evaluation and interaction. Results from a laboratory experiment show significantly different linguistic patterns under different task complexities and cognitive load levels. Implications of the research for adaptive interaction are also discussed, that is, how the cognitive load measurement-based approach could be used for user interface evaluation and interaction design improvement.     

Cognitive Load Theory and Library Research Guides

Online library research guides are instructional tools that most libraries provide for their patrons. With greater flexibility in Web programming and new products like Springshare's Libguides, librarians have multiple venues for guide creation. This paper seeks to assist research guide editors in assessing their guides based on cognitive load theory. This theory is based on the idea that cognitive capacity for learning is limited and that techniques can be developed to help learners avoid cognitive overload. Addressing the three main sources of cognitive load gives librarians a framework in which to create meaningful and useful research guides.     

利用LoadRunner实现Web负载测试的自动化

对一个软件系统而言，系统的性能包括执行效率、资源占用、稳定性等等，其中负载压力是性能的一个重要方面。负载压力测试有助于确认被测系统是否能够支持性能需求，以及预期的负载增长，并找出系统出现异常的原因，从而对系统性能进行调优。随着Web应用程序的使用越来越广泛，对它的性能进行测试也变得越来越重要，而采用自动化的测试工具极大地方便了Web的测试。文中利用MI的自动化测试工具LoadRenner，初步实现了Web负载测试的自动化。     

利用LoadRunner实现Web负载测试的自动化

对一个软件系统而言,系统的性能包括执行效率、资源占用、稳定性等等,其中负载压力是性能的一个重要方面。负载压力测试有助于确认被测系统是否能够支持性能需求,以及预期的负载增长,并找出系统出现异常的原因,从而对系统性能进行调优。随着Web应用程序的使用越来越广泛,对它的性能进行测试也变得越来越重要,而采用自动化的测试工具极大地方便了Web的测试。文中利用MI的自动化测试工具LoadRunner,初步实现了Web负载测试的自动化。     

应用负载平衡技术增强Web服务性能

该文简要分析了影响Ｗｅｂ服务性能的因素,对浏览器／服务器结构中Ｗｅｂ服务器负荷过载问题提出解决方法－－采用负载平衡器,并给出了一个负载平衡算法。     

认知无线电中基于博弈论的联合功率和速率控制算法

研究了认知无线电(CR)中共享频谱状态下的联合功率和速率控制问题。功率控制技术是认知无线电的关键技术之一,而下一代的网络也对速率的多样化传输提出了要求。将系统中次级用户(SU)间的关系假定为非合作的竞争性关系,同时考虑到次级用户对主用户的干扰容限,提出了基于博弈论的联合功率控制算法(NPRG),并证明了该算法的纳什均衡的存在性、唯一性。仿真结果证明了算法具有较快的收敛性,且能收敛到较低的功率和较高的速率。     

用关联分析法对负荷预测结果进行二次处理

短期电力负荷预测是电力系统管理现代化的重要内容之一,是对发电、输电和电能分配等合理安排的必要前提,对提高电力系统的各方面的效益有非常重要的影响。随着电力系统管理现代化的发展,电力部门对短期负荷预测精度的要求也越来越高。介绍了现有的电力负荷预测技术的研究状况,并在此基础上提出了利用关联规则分析法对负荷预测结果进行二次处理。实验结果表明负荷预测结果通过关联规则分析法的处理,其准确率得到一定程度的提高。     

用关联分析法对负荷预测结果进行二次处理

短期电力负荷预测是电力系统管理现代化的重要内容之一,是对发电、输电和电能分配等合理安排的必要前提,对提高电力系统的各方面的效益有非常重要的影响.随着电力系统管理现代化的发展,电力部门对短期负荷预测精度的要求也越来越高.介绍了现有的电力负荷预测技术的研究状况,并在此基础上提出了利用关联规则分析法对负荷预测结果进行二次处理.实验结果表明负荷预测结果通过关联规则分析法的处理,其准确率得到一定程度的提高.     

用互感器校验仪测试负载箱的方法

介绍一种利用互感器校验仪测试负载箱阻抗或导纳值的方法.从分析测试原理入手,详细图解如何具体利用广州羊城(或海纳)YC-52互感器校验装置实现对自身所带负载箱的测试.     

Using Bounded Diameter Minimum Spanning Trees to Build Dense Active Appearance Models

We present a method for producing dense active appearance models (AAMs), suitable for video-realistic synthesis. To this end we estimate a joint alignment of all training images using a set of pairwise registrations and ensure that these pairwise registrations are only calculated between similar images. This is achieved by defining a graph on the image set whose edge weights correspond to registration errors and computing a bounded diameter minimum spanning tree. Dense optical flow is used to compute pairwise registration and a flow refinement method to align small scale texture is introduced. Further, given the registration of training images, vertices are added to the AAM to minimise the error between the observed flow fields and the flow fields interpolated between the AAM mesh points. We demonstrate a significant improvement in model compactness.     

利用Nginx实现基于URI的Web负载分配

介绍最新的开源Web服务器Nginx,建立了基于URI的负载分配模型,开发基于URI的Nginx负载分配模块,使得实现了根据用户请求的URI进行负栽分配.该模块的开发结合了共享内存技术和同步Hash技术,解决了高层转发的效率问题.经过测试案例表明,基于URI的Web负栽分配策略可行有效.     

一种采用流切割实现报文保序的负载均衡算法

网络链路过载或链路失效时,使用负载均衡技术可以避免网络发生拥塞。负载的分派粒度决定了负载均衡系统的均衡性能。分派粒度越细,均衡效果越理想。基于包水平粒度的负载分派可以实现理想的均衡性能,但是会造成同一TCP业务流中报文乱序;基于流水平的分派可以保证报文不乱序,但均衡效果不理想。提出了按照报文段粒度分派负载的FSLB算法。仿真实验表明,该算法可避免报文乱序并能达到较理想的均衡效果。     

Using Multilayer Fuzzy Cognitive Maps to diagnose Autism Spectrum Disorder

Autism Spectrum Disorder (ASD) is comprised of a group of heterogeneous neurodevelopmental conditions, typically characterized by a triad of symptoms consisting of (1) impaired communication, (2) restricted interests, and (3) repetitive and stereotypical behavior pattern. An accurate and early diagnosis of autism can provide the basis for an appropriate educational and treatment program. In this work, we propose a computational model using a Multilayer Fuzzy Cognitive Map (hereafter referred to as MFCM) based on standardized behavioral assessments diagnosing the ASD (MFCM-ASD). The two standards used in the model are: the Autism Diagnostic Observation Schedule, Second Edition (ADOS2), and the Autism Diagnostic Interview Revised (ADIR). The MFCM’s are a soft computing technique characterized by robust properties that make it an effective technique for medical decision support systems. For the evaluation of the MFCM-ASD model, we have used real datasets of diagnosed cases, so as to compare against other method/approaches. Initial experiments demonstrated that the proposed model outperforms conventional Fuzzy Cognitive Maps (FCMs) for ASD diagnosis. Our MFCM-ASD model serves as a diagnostic tool required to support the medical decisions when determining the correct diagnosis of Autism in children with different cognitive characteristics.     

微力微位移天平测试方法

介绍了一种简单有效的微力、微位移天平测试方法,通过对薄型硅悬臂梁进行力—挠度特性测试进而提取材料杨氏模量的方法是简便、可行的。还介绍了用于测量薄膜应力的悬臂梁挠曲法,对于硅上热生长1.1μm SiO_2的结构,测得SiO_2膜内的压应力为200～230MPa.微力微位移天平测试方法操作方便,仪器成本低,具有较高精度。     

基于FCGA和改进LSTM-BPNN的燃气负荷预测

准确的燃气负荷预测对于城市合理供应和调度能源起着非常重要的作用.由于燃气负荷数据本身具有周期性,随机性的复杂特点以及单阶段单预测模型的局限性,本文提出了一种基于模糊编码遗传算法(Fuzzy Coding of Genetic Algorithms,FCGA)和改进的LSTM-BPNN残差修正模型的多阶段混合模型.首先第...     

Data-Driven Load Forecasting Using Machine Learning and Meteorological Data

Electrical load forecasting is very crucial for electrical power systems’ planning and operation. Both electrical buildings’ load demand and meteorological datasets may contain hidden patterns that are required to be investigated and studied to show their potential impact on load forecasting. The meteorological data are analyzed in this study through different data mining techniques aiming to predict the electrical load demand of a factory located in Riyadh, Saudi Arabia. The factory load and meteorological data used in this study are recorded hourly between 2016 and 2017. These data are provided by King Abdullah City for Atomic and Renewable Energy and Saudi Electricity Company at a site located in Riyadh. After applying the data pre-processing techniques to prepare the data, different machine learning algorithms, namely Artificial Neural Network and Support Vector Regression (SVR), are applied and compared to predict the factory load. In addition, for the sake of selecting the optimal set of features, 13 different combinations of features are investigated in this study. The outcomes of this study emphasize selecting the optimal set of features as more features may add complexity to the learning process. Finally, the SVR algorithm with six features provides the most accurate prediction values to predict the factory load.