智能导师系统在军事训练中应用现状及发展

根据国内当前军事训练所面临的实际困境及智能导师系统(ITS)在教学训练应用中所具有的功能和经济价值,对ITS在军事训练中的应用进行了初步研究.介绍了ITS的发展历程、ITS的相关概念及其技术特点.以ITS在国外应用及发展为主线,重点论述了欧美国家军事训练中ITS应用的现状和相关的典型系统.提出在军事训练领域中我们应用ITS技术的一些建议.     

Intelligent tutoring system using expert knowledge and Kohonen maps with automated training

Neural Computing and Applications - This paper presents an intelligent tutoring system (ITS) model that is capable of driving the didactic transposition of contents. Initially, the tutoring system...     

A hybrid memory built by SSD and DRAM to support in-memory Big Data analytics

Big Data requires a shift in traditional computing architecture. The in-memory computing is a new paradigm for Big Data analytics. However, DRAM-based main memory is neither cost-effective nor energy-effective. This work combines flash-based solid state drive (SSD) and DRAM together to build a hybrid memory, which meets both of the two requirements. As the latency of SSD is much higher than that of DRAM, the hybrid architecture should guarantee that most requests are served by DRAM rather than by SSD. Accordingly, we take two measures to enhance the hit ratio of DRAM. First, the hybrid memory employs an adaptive prefetching mechanism to guarantee that data have already been prepared in DRAM before they are demanded. Second, the DRAM employs a novel replacement policy to give higher priority to replace data that are easy to be prefetched because these data can be served by prefetching once they are demanded once again. On the contrary, the data that are hard to be prefetched are protected by DRAM. The prefetching mechanism and replacement policy employed by the hybrid memory rely on access patterns of files. So, we propose a novel pattern recognition method by improving the LZ data compression algorithm to detect access patterns. We evaluate our proposals via prototype and trace-driven simulations. Experimental results demonstrate that the hybrid memory is able to extend the DRAM by more than twice.     

大数据系统开发中的构件自动选型与参数配置

大数据应用系统包含数据的采集、存储、分析、挖掘、可视化等多个技术环节,各个环节都存在多种解决方案,涉及到的各类系统有数百种之多,且系统配置较为复杂,这给企业的大数据应用系统构建带来了极大的挑战。针对大数据应用系统开发中构件选型的难题,通过建立规范化的需求指标,并采用决策树模型实现了大数据构件的自动选型。从几个主流的分布式存储系统出发,以Cassandra为例,利用多元回归拟合的方法针对硬件参数建立相应的性能模型,将用户需求作为输入,利用性能模型进行系统硬件参数配置;通过研究系统原理、架构、特点及应用场景,构建软件参数配置知识库指导软件参数的配置,从而解决了大数据系统开发中的构件自动选型和参数配置问题。     

平行数据:从大数据到数据智能*

为了解决实际问题,大数据分析处理系统需要获取数据,然而实际场景中收集到的实际数据通常不完备.另外,大多数问题的解决方案通常是由问题引导或者仅仅进行数据分析,运行参数调整和设定带有较大的盲目性,难以达到应用的智能性.为此,文中提出平行数据的概念和框架,根据实际数据经计算实验产生真正的虚拟大数据,结合默顿定律,以期待的解决方案与问题进行广义对偶,引导大数据聚焦到实际问题.实际数据与虚拟数据动态互动,平行演化,形成一个虚实相生、数据动态变化的过程,最终使数据具备智能,进而解决未知的问题.平行数据不但是一种数据表示形式,更是一种数据演化机制与方式,其特色是虚实互动,所有数据的动力学轨迹构成了数据动力学系统.平行数据为数据处理、表示、挖掘和应用提供了一个新的范式.     

支架工作阻力大数据分析系统

为实现支架工作阻力大数据快速分析,提出了支架工作阻力大数据处理流程,设计了一种支架工作阻力大数据分析系统。该系统可实现支架工作阻力大数据的自动分析,并通过数据可视化技术,将矿压分析结果以专业图形和报表等形式直观表达,反映出顶板运动状态,便于指导工作面安全生产。     

大数据环境下地铁自动售检票系统设计与实现

近年来,随着经济领域蓬勃发展,我国加快了现代化建设进程,交通设施建设不断推进;受互联网大数据技术变革的影响,传统地铁售检票系统无法满足高客流量、大数据流处理的高强度工作要求;在日常实践应用中,传统地铁售检票系统经常出现检票识别率低、售票信息运算处理响应速度慢、多人员、多任务操作执行准确率差的问题;针对上述问题,结合大数据资源运算能力,提出大数据环境下地铁自动售检票系统设计;采用大数据实名高检处理引擎(VBDKG)、多路分处运算模组(ICGRU)与动态身份比对算法(DBTDE),针对传统地铁自动售检票系统存在的问题进行解决;通过仿真实验测试证明,提出的大数据环境下地铁自动售检票系统设计具有较强的实施性与可操作性;同时,运行处理准确性高,运行稳定。     

Designing automated adaptive support to improve student helping behaviors in a peer tutoring activity

Adaptive collaborative learning support systems analyze student collaboration as it occurs and provide targeted assistance to the collaborators. Too little is known about how to design adaptive support to have a positive effect on interaction and learning. We investigated this problem in a reciprocal peer tutoring scenario, where two students take turns tutoring each other, so that both may benefit from giving help. We used a social design process to generate three principles for adaptive collaboration assistance. Following these principles, we designed adaptive assistance for improving peer tutor help-giving, and deployed it in a classroom, comparing it to traditional fixed support. We found that the assistance improved the conceptual content of help and the use of interface features. We qualitatively examined how each design principle contributed to the effect, finding that peer tutors responded best to assistance that made them feel accountable for help they gave.     

数据建模技术在电信业务支撑系统中的应用研究

主要讨论数据建模技术在电信业务支撑系统中的应用。以异常客户的识别为例,阐述了数学模型的构建与使用。遵循数据挖掘流程,详细介绍了数据属性的约简,模型建立以及模型评估的方法。通过对实际数据的验证,说明了基于数据挖掘技术的电信行业建模应用的有效性。     

A syntactical approach to diagnosing multiple bugs in anintelligent tutoring system

This paper develops a buggy model-based intelligent tutoring system (ITS) capable of diagnosing multiple bugs, pinpointing not only their types but also their exact locations involved. The procedural grammar of context free type is used not only to decompose a complicated procedure involving possible steps of both correct and incorrect arithmetic into simplest, `primitive' procedures but also to express control knowledge essential to implementing an efficient parser. An efficient parser is implemented by narrowing down the search space, exploiting the control knowledge, the selection and consistency conditions in selecting rules in parsing. Parsing of superficial symptoms such as a student's errors in arithmetic essentially reveals the deep structure of the buggy knowledge of the student so that teachers may now devise and offer a more effective means of instructions for acquiring the correct skill of arithmetic     

From Big to Smart Data: Iterative ensemble filter for noise filtering in Big Data classification

The quality of the data is directly related to the quality of the models drawn from that data. For that reason, many research is devoted to improve the quality of the data and to amend errors that it may contain. One of the most common problems is the presence of noise in classification tasks, where noise refers to the incorrect labeling of training instances. This problem is very disruptive, as it changes the decision boundaries of the problem. Big Data problems pose a new challenge in terms of quality data due to the massive and unsupervised accumulation of data. This Big Data scenario also brings new problems to classic data preprocessing algorithms, as they are not prepared for working with such amounts of data, and these algorithms are key to move from Big to Smart Data. In this paper, an iterative ensemble filter for removing noisy instances in Big Data scenarios is proposed. Experiments carried out in six Big Data datasets have shown that our noise filter outperforms the current state-of-the-art noise filter in Big Data domains. It has also proved to be an effective solution for transforming raw Big Data into Smart Data.     

A conversational intelligent tutoring system to automatically predict learning styles

This paper proposes a generic methodology and architecture for developing a novel conversational intelligent tutoring system (CITS) called Oscar that leads a tutoring conversation and dynamically predicts and adapts to a student’s learning style. Oscar aims to mimic a human tutor by implicitly modelling the learning style during tutoring, and personalising the tutorial to boost confidence and improve the effectiveness of the learning experience. Learners can intuitively explore and discuss topics in natural language, helping to establish a deeper understanding of the topic. The Oscar CITS methodology and architecture are independent of the learning styles model and tutoring subject domain. Oscar CITS was implemented using the Index of Learning Styles (ILS) model (Felder & Silverman, 1988) to deliver an SQL tutorial. Empirical studies involving real students have validated the prediction of learning styles in a real-world teaching/learning environment. The results showed that all learning styles in the ILS model were successfully predicted from a natural language tutoring conversation, with an accuracy of 61–100%. Participants also found Oscar’s tutoring helpful and achieved an average learning gain of 13%.     

基于大数据技术的电子商务个性化信息自动推荐算法应用

当前个性化推荐算法应用因未构建增量更新推荐模型,对电子商务平台的大数据进行处理,导致推荐结果不能及时更新,严重影响用户体验.因此,提出基于大数据技术的电子商务个性化信息自动推荐算法.首先对平台中用户与项目的相异度进行计算,利用计算后的相异度矩阵构建数据增量更新模型;然后,将该模型作为个性化推荐模型,引入IU-UserCF推荐算法,提高预测准确性;最后以电商物流为应用对象,将所提方法引入应用进行探索.实验对比结果表明,所提方法在电子商务应用中有着更加出色的推荐效果.     

Automatic nevirapine concentration interpretation system using support vector regression

Follow-up of human immunodeficiency virus (HIV) patients treated with Nevirapine (NVP) is a necessary process to evaluate the drug resistance and the HIV mutation. It is also usually tested by immunochromatographic (IC) strip test. However, it is difficult to estimate the amount of drug the patient gets by visually inspection of color. In this paper, we propose an automatic interpretation system using a commercialized optical scanner. Several IC strips can be placed at any direction as long as they are on the scanner plate. There are three steps in the system, i.e., light intensity normalization, image segmentation and NVP concentration interpretation. We utilized the Support Vector Regression to interpret the NVP concentration. From the results, we found out the performance of the system is promising and better than that of the linear and nonlinear regression.     

Reusing formalisations of legislation in a tutoring system

Statutor names an evolving Prolog program which is being developed as a knowledge-based tutoring system in the legal domain. The system utilises direct manipulation of graphical objects as a means of eliciting complex responses from the user and for providing graphical representations of complex answers to the user. It is a marriage of good interface practice and knowledge-based programming techniques which has presented a number of interesting prospects for tutoring. Perhaps the most interesting of these is the possibility of a dialog in which the student is asked to construct an argument in order to establish the truth of a particular proposition, the system then doing the same, and feedback and student modelling information being derived from a comparison of the two argument structures. This technique is not restricted in its significance to the legal domain but is applicable wherever knowledge of a subject matter can be expressed or tested by the construction of an argument. Finally, the system demonstrates the reusability of declarative knowledge by including additional modules (an expert system shall and an authoring system) with utilise the same knowledge bases as the main Statutor program itself.     

Big Data: from collection to visualization

Organisations are increasingly relying on Big Data to provide the opportunities to discover correlations and patterns in data that would have previously remained hidden, and to subsequently use this new information to increase the quality of their business activities. In this paper we present a ‘story’ of Big Data from the initial data collection and to the end visualization, passing by the data fusion, and the analysis and clustering tasks. For this, we present a complete work flow on (a) how to represent the heterogeneous collected data using the high performance RDF language, how to perform the fusion of the Big Data in RDF by resolving the issue of entity disambiguity and how to query those data to provide more relevant and complete knowledge and (b) as the data are received in data streams, we propose batchStream, a Micro-Batching version of the growing neural gas approach, which is capable of clustering data streams with a single pass over the data. The batchStream algorithm allows us to discover clusters of arbitrary shapes without any assumptions on the number of clusters. This Big Data work flow is implemented in the Spark platform and we demonstrate it on synthetic and real data.     

Using synchronous peer tutoring system to promote elementary students’ learning in mathematics

The face-to-face computer-supported collaborative learning has emerged as an important strategy to elementary students' learning. Few studies have explicitly incorporated the structured peer tutoring to the synchronous environment in mathematics. This study was aimed to explore the effects of the synchronous peer tutoring system on children's mathematics learning. In the project, there were 88 10-11-year-old students who peer tutored each other in mathematics in the face-to-face online environment for a year. Compared to the control group, students in the experimental group had significant gains in mathematics learning, especially in the arithmetic and application types of questions. This study demonstrated the positive effects of peer support via the online synchronous learning on students' self-concept and attitudes toward mathematics learning. The results indicated that the longer the vulnerable pupils engaged in peer tutoring online, the more they benefited from the process. The finding suggests that students demonstrate different mathematics reasoning skills when they are paired with peers at different levels of ability. These findings demonstrate that the synchronous peer tutoring system is an effective tool to enhance elementary students' learning in mathematics, as well as promote positive self-concepts.     

Automatic generation of emotions in tutoring agents for affective e-learning in medical education

Web-based education is particularly appropriate for remote teaching and learning at any time and place, away from classrooms and does not necessarily require the presence of a human instructor. The need for time and place independence is even greater in some cases, such as for medical instructors who are usually doctors that have to treat patients on top of their tutoring duties. However, this independence from real teachers and classrooms may influence negatively the students who may feel deprived of the benefits of human–human interaction. In this paper we describe a novel approach for incorporating affective characteristics into e-learning through an authoring tool. The authoring tool incorporates and adapts principles of a cognitive theory for modeling possible emotional states that a tutoring agent may use for educational purposes. Medical instructors may use this authoring tool to create their own educational characters that will interact affectively with their students in the e-learning environment.     

Automatic system for the detection and analysis of errors to support the personalized feedback

The study of errors in learning and the search for patterns to explain their causes have always been of great interest to researchers and educators alike. Mistakes are a constant in students’ solutions to mathematical problems and are inseparable from the learning process. It is essential, then, to diagnose and address the mistakes made by students so as to allow them to reflect on their errors and adjust their knowledge. To this end, we have created a system that tracks all the actions carried out by a student when solving a mathematical algorithm, not just the final results, and which is capable of diagnosing the faults and possible causes. It can also recommend the actions to be taken based on the individual difficulties encountered. In short, we have created a personalized teaching system whose features could be particularly useful for special-needs students, such as those with Down syndrome. This paper explains the error detection modules in the addition, subtraction and error-adapted assistance algorithms.This work is part of a multidisciplinary research effort financed by R&D project called “Divermates”, of the Ministry of Labor and Social Affairs, and involving personnel from the Computer Engineering and Mathematics and Fine Arts Education Departments of the University of La Laguna, as well as professionals from the Tenerife Trisomic 21 Association (ATT21).