A visual tool for computer supported learning: The robot motion planning example

We introduce an effective computer aided learning visual tool (CALVT) to teach graph-based applications. We present the robot motion planning problem as an example of such applications. The proposed tool can be used to simulate and/or further to implement practical systems in different areas of computer science such as graphics, computational geometry, robotics and networking. In the robot motion planning example, CALVT enables users to setup the working environment by creating obstacles and a robot of different shapes, specifying starting and goal positions, and setting other path or environment parameters from a user-friendly interface. The path planning system involves several phases. Each of these modules is complex and therefore we provide the possibility of visualizing graphically the output of each phase. Based on our experience, this tool has been an effective one in classroom teaching. It not only cuts down, significantly, on the instructor’s time and effort but also motivates senior/graduate students to pursue work in this specific area of research.     

Supporting visually impaired children with software agents in a multimodal learning environment

Visually impaired children have a great disadvantage in the modern society since their ability to use modern computer technology is limited due to inappropriate user interfaces. The aim of the work presented in this paper was to develop a multimodal software architecture and applications to support learning of visually impaired children. The software architecture is based on software agents, and has specific support for visual, auditory and haptic interaction. It has been used successfully with different groups of 7-8 year-old and 12 year-old visually impaired children. In this paper we discuss the enabling software technology and interaction techniques aimed to realize our goal and our experiences in the actual use of the system.     

Learning performance and computer software: an exploration of knowledge transfer

Computer studies educators have a challenging task in keeping pace with the rapidly changing content of computer software. One way to meet this challenge is to examine the nature of knowledge transfer. Instead of focusing on unique software packages, teachers could concentrate on knowledge that is likely to transfer from one software application to another. The purpose of the current study was to describe what kind of knowledge is used in learning new software, assess the relative effectiveness of this knowledge in aiding the learning process, and examine how the results could advance educational learning theory and practice. Thirty-six adults (18 male, 18 female), representing three computer ability levels (beginner, intermediate, and advanced), volunteered to think out loud while they learned the rudimentary steps (moving the cursor, using a menu, entering data) required to use a spreadsheet software package (Lotus 1-2-3). Previous understanding of terminology, software concepts and actions, and other software packages had the largest impact, both positive and negative, on learning. A basic understanding of the keyboard and common movement keys was also important, although higher level knowledge (e.g., terms, concepts, actions) is probably necessary for significant gains in learning performance. Computer ability had little impact on the type of transfer knowledge used, except with respect to the use of software concepts and, to a lesser extent, terminology. The interaction between problem type and effectiveness of a specific transfer area suggests that identifying specific common tasks among software packages is important in detecting useful transfer knowledge. It is equally important that computer users understand labeling idiosyncrasies of these common tasks.     

A morphable template framework for robot learning by demonstration: Integrating one-shot and incremental learning approaches

Robot learning by demonstration is key to bringing robots into daily social environments to interact with and learn from human and other agents. However, teaching a robot to acquire new knowledge is a tedious and repetitive process and often restrictive to a specific setup of the environment. We propose a template-based learning framework for robot learning by demonstration to address both generalisation and adaptability. This novel framework is based upon a one-shot learning model integrated with spectral clustering and an online learning model to learn and adapt actions in similar scenarios. A set of statistical experiments is used to benchmark the framework components and shows that this approach requires no extensive training for generalisation and can adapt to environmental changes flexibly. Two real-world applications of an iCub humanoid robot playing the tic-tac-toe game and soldering a circuit board are used to demonstrate the relative merits of the framework.     

基于机器学习的无参考图像质量评价综述

无参考图像质量评价（NRIQA）因其广泛的应用需求一直以来都是计算机视觉及其交叉领域的研究热点。回顾近十几年来基于机器学习的典型NRIQA模型,介绍图像质量评价的常用数据库、算法性能指标、NRIQA主要难点和现有的解决方法;分析了不同模型的思想、实现、特点;最后统计对比多个数据库上的测试结果。总结研究现状、分析发展趋势,为这一领域的研究者提供文献参考。     

A systematic review of machine learning techniques for software fault prediction

BackgroundSoftware fault prediction is the process of developing models that can be used by the software practitioners in the early phases of software development life cycle for detecting faulty constructs such as modules or classes. There are various machine learning techniques used in the past for predicting faults.MethodIn this study we perform a systematic review of studies from January 1991 to October 2013 in the literature that use the machine learning techniques for software fault prediction. We assess the performance capability of the machine learning techniques in existing research for software fault prediction. We also compare the performance of the machine learning techniques with the statistical techniques and other machine learning techniques. Further the strengths and weaknesses of machine learning techniques are summarized.ResultsIn this paper we have identified 64 primary studies and seven categories of the machine learning techniques. The results prove the prediction capability of the machine learning techniques for classifying module/class as fault prone or not fault prone. The models using the machine learning techniques for estimating software fault proneness outperform the traditional statistical models.ConclusionBased on the results obtained from the systematic review, we conclude that the machine learning techniques have the ability for predicting software fault proneness and can be used by software practitioners and researchers. However, the application of the machine learning techniques in software fault prediction is still limited and more number of studies should be carried out in order to obtain well formed and generalizable results. We provide future guidelines to practitioners and researchers based on the results obtained in this work.     

ModelSet: A labelled dataset of software models for machine learning

Curated collections of models are essential for the success of Machine Learning (ML) and Data Analytics in Model-Driven Engineering (MDE). However, current datasets are either too small or not properly curated. In this paper, we present ModelSet, a dataset composed of 5,466 Ecore models and 5,120 UML models which have been manually labelled to support ML tasks. We describe the structure of the dataset and explain how to use the associated library to develop ML applications in Python. Finally, we present some applications which can be addressed using ModelSet.Tool Website: https://github.com/modelset     

Systematic literature review of machine learning based software development effort estimation models

Context

Software development effort estimation (SDEE) is the process of predicting the effort required to develop a software system. In order to improve estimation accuracy, many researchers have proposed machine learning (ML) based SDEE models (ML models) since 1990s. However, there has been no attempt to analyze the empirical evidence on ML models in a systematic way.

Objective

This research aims to systematically analyze ML models from four aspects: type of ML technique, estimation accuracy, model comparison, and estimation context.

Method

We performed a systematic literature review of empirical studies on ML model published in the last two decades (1991-2010).

Results

We have identified 84 primary studies relevant to the objective of this research. After investigating these studies, we found that eight types of ML techniques have been employed in SDEE models. Overall speaking, the estimation accuracy of these ML models is close to the acceptable level and is better than that of non-ML models. Furthermore, different ML models have different strengths and weaknesses and thus favor different estimation contexts.

Conclusion

ML models are promising in the field of SDEE. However, the application of ML models in industry is still limited, so that more effort and incentives are needed to facilitate the application of ML models. To this end, based on the findings of this review, we provide recommendations for researchers as well as guidelines for practitioners.     

PERT技术在软件项目管理中的研究与实现

软件开发是一项高难度复杂的工作,在软件开发过程中,项目的进度管理是决定项目能否成功的关键因素.在对软件开发的项目管理技术方法上进行了系统的研究后,提出一套基于PERT技术的软件项目进度控制和绘图系统,解决了大部分项目管理软件在绘图和跟踪控制上的不足.基于关键路径的理论,用网络图直观地展现出软件开发项目的进度,网络图的布局更加美观合理,箭线交叉少,使PERT技术的应用变得更加容易、更加直观,对各部门推广使用PERT技术及完善ERP系统具有非常重要的现实意义.     

The role of student learning styles, gender, attitudes and perceptions on information and communication technology assisted learning

The present study adopts an illuminative approach to evaluate students’ initial attitudes towards the use of information and communication technology (ICT). Ninety-nine undergraduate science students participated in this study and their learning styles were classified according to Honey and Mumford (1986) (Honey, P., Mumford, A., 1986. The Manual of Learning styles. Peter Honey, 10 Linden Avenue, Maidenhead) learning style questionnaire. Student learning styles were classified as activist, reflector, theorist, or pragmatist. No significant difference in learning styles was observed between genders and between student cohorts. Six dimensions to student attitudes toward ICT were identified as follows; ‘comfort’, ‘interactivity’, ‘self-satisfaction’, ‘value new technology’, ‘experience’ and ‘context’. Students exhibited low scores in the attitude dimensions of ‘value new technology’, ‘interactivity’ and ‘context’ indicating that they were uncomfortable with computers, were unhappy about the lack of personal contact and would prefer to learn in a more traditional mode. A significant, though weak, negative correlation between the ‘theorist’ and the ‘interactivity’ and ‘context’ attitude dimensions was also observed. In addition, based on the results of this study it appears that first year students exhibit a more positive perception of ICT supported learning than second and third year students. Though the use of ICT in higher education is becoming more widespread based on the results of this study student use of the technology may be limited by a negative attitude toward a style of teaching which is not consistent with their past learning experiences.     

移动机器人远程人机交互软件平台设计与实现

基于移动机器人代替人去危险场合执行探测和救援任务的应用背景,设计了一个人机交互的软件平台。采用UML对设计过程进行建模,基于组件对系统进行模块化设计;各组件并行运行,通过消息队列与消息响应的机制实现组件之间的并发式通信。软件平台基于LabVIEW的Actor框架实现,核心Actor组件基于状态机设计,协调其它组件之间的通信;设计了一个远程Actor代理,实现与基于ROS的远程机器人的本地控制系统通信,将人的指令传输给机器人并将机器人的传感信息传回给人机界面。实验系统部署在两台计算机上,一台运行基于LabVIEW的人机界面,一台运行基于ROS的P3AT控制仿真系统。实验表明,设计的软件平台各部分能够协同工作,人机交互自然顺畅,满足预期要求。     

Effects of visual and linguistic anthropomorphic cues on social perception,self-awareness,and information disclosure in a health website

Acknowledging the lack of studies examining both visual and linguistic anthropomorphic cues and the underlying mechanisms of their effects, we investigated how the different modalities of anthropomorphic cues in a health website influenced information disclosure. In a 2 (visual cues: human vs. non-human image) × 2 (linguistic cues: conversational vs. impersonal language) × 2 (question type: less vs. more sensitive questions) between-subjects experiment (N = 254), participants registered with a mock-up health website. We assessed a behavioral outcome of not disclosing personal information and psychological outcomes of social perception and self-awareness as potential mediators. Results revealed distinctive effects of the two modalities of the anthropomorphic cues. Anthropomorphic images, on one hand, increased public and private self-awareness, and public self-awareness in turn led to less information disclosure. Anthropomorphic language, on the other hand, heightened social perception and promoted information disclosure, but social perception did not predict the disclosure. These results indicate unique underlying mechanisms of the effects of anthropomorphism: priming effect of visual cues, and communicative effects of linguistic cues.     

Participants matter: Effectiveness of VR-based training on the knowledge,trust in the robot,and self-efficacy of construction workers and university students

Virtual Reality (VR)-based training has gained attention from the scientific community in the Architecture, Engineering, and Construction (AEC) industry as a cost-effective and safe method that eliminates the safety risks that may impose on workers during the training compared to traditional training methods (e.g., in-person hands-on training, apprenticeship). Although researchers have developed VR-based training for construction workers, some have recruited students rather than workers to understand the effect of their VR-based training. However, students are different from construction workers in many ways, which can threaten the validity of such studies. Hence, research is needed to investigate the extent to which the findings of a VR-based training study are contingent on whether students or construction workers were used as the study sample. This paper strives to compare the effectiveness of VR-based training on university students’ and construction workers’ knowledge acquisition, trust in the robot, and robot operation self-efficacy in remote operation of a construction robot. Twenty-five construction workers and twenty-five graduate construction engineering students were recruited to complete a VR-based training for remote operating a demolition robot. We used quantitative analyses to answer our research questions. Our study shows that the results are dependent on the target sample in that students gained more knowledge, whereas construction workers gained more trust in the robot and more self-efficacy in robot operation. These findings suggest that the effectiveness of VR-based training on students may not necessarily associate with its effectiveness on construction workers.     

基于深度学习的视频检索系统设计与实现

针对摄像头产生的海量视频信息,检索工作需要花费大量的人力、物力以及时间成本问题,分析发现传统检索的功能大多都基于文本关键词,对视频内容的覆盖率低且容易依赖于相关工作人员的主观性。提出如何应用传统的机器视觉技术以及深度学习技术去构建一个高效的视频检索系统。创新点在于从视频帧图像内容的角度去充分发掘其中的信息,其信息挖掘的过程无需人工干预,从而提高了信息利用率。     

Investigations on increasing flexibility of hardware and software of industrial robots

The high flexibility of an industrial robot results from its free programmable control and high moveability. Still, there are some cases of application that demand more flexibility of hardware and software. If a case of application is found that requires more control functions than the standard control offers to the user, a lot of time and work is necessary to implement new control software to the existing control program. This is possible in an easy and quick way if the control program is written in a higher programming language. Also the programming of new robot controls becomes easy, quick and transparent. Other problems are found in the adaption of end effectors to the handling task. One solution is automatic changing of end effectors. Another solution is construction of universal end effectors, like versatile grippers. Both possibilities have their own range of application and must be chosen under the requirements of the special application case.     

A simulation interface designed for improved user interaction and learning in water quality modelling software

Traditional simulation software that supports management decisions is configured and run by experienced scientists. However, it is often criticised for its lack of interactivity, not only in the application of decisions but also in the display of results. This paper presents the simulation interface of software with management strategy evaluation capabilities and its capacity to enable resource managers to learn about water quality management as evaluated in a workshop setting. The software ‘MSE Tool’ is not intended to produce definitive real-world advice but provides a test-bed for managers to interactively design strategies and explore the complexities inherent to water quality management using a simple, yet effective, user interface. MSE Tool has been used in a pilot application that simulated the effects of management strategies applied in catchments and their effects on riverine, estuarine and marine water quality in South East Queensland, Australia. The approach and the software are suitable for reuse in other management strategy evaluation projects.     

Increasing teacher candidates' ways of interaction and levels of learning through action research in a blended course

Within the scope of this research, efforts were exerted to increase teacher candidates' interaction ways through action research in a blended teaching profession course in higher education level. Teacher candidates participated in various blended learning activities during a semester-long course, and the problems related to learners' participation in blended learning activities and their interactions during learning process were solved via action decisions. Blended learning activities were developed according to Felder-Silverman Learning Style Model and the lessons learned from previous two pilot studies. In order to conduct the activities; face-to-face and synchronous virtual classroom sessions were combined with asynchronous from discussions and blog. At the beginning of the course, teacher candidates chosed activity sets according to learning style model and throughout a semester-long course, they participated in the activities. Throughout the action research process, the ways of interactions between students, students and instructor, and students and the content were examined as well as the levels of learning accomplished by students during the learning process. This study has revealed indicators pointing to an increase both in students’ interactions and levels of learning during the blended learning process.     

The effects of change control and management review on software flexibility and project performance

Software flexibility and project efficiency are deemed to be desirable but conflicting goals during software development. We considered the link between project performance, software flexibility, and management interventions. Specially, we examined software flexibility as a mediator between two recommended management control mechanisms (management review and change control) and project performance. The model was empirically evaluated using data collected from 212 project managers in the Project Management Institute. Our results confirmed that the level of control activities during the system development process was a significant facilitator of software flexibility, which, in turn, enhanced project success. A mediator role of software flexibility implied that higher levels of management controls could achieve higher levels of software flexibility and that this was beneficial not only to the maintainability of complex applications but also to project performance.     

CLPL: Providing software infrastructure for the systematic and effective construction of complex collaborative learning systems

Over the last decade, e-Learning and in particular Computer-Supported Collaborative Learning (CSCL) needs have been evolving accordingly with more and more demanding pedagogical and technological requirements. As a result, high customization and flexibility are a must in this context, meaning that collaborative learning practices need to be continuously adapted, adjusted, and personalized to each specific target learning group. These very demanding needs of the CSCL domain represent a great challenge for the research community on software development to satisfy.This contribution presents and evaluates a previous research effort in the form of a generic software infrastructure called Collaborative Learning Purpose Library (CLPL) with the aim of meeting the current and demanding needs found in the CSCL domain. To this end, we experiment with the CLPL in order to offer an advanced reuse-based service-oriented software engineering methodology for developing CSCL applications in an effective and timely fashion. A validation process is provided by reporting on the use of the CLPL platform as the primary resource for the Master's thesis courses at the Open University of Catalonia when developing complex software applications in the CSCL domain.The ultimate aim of the whole research is to yield effective CSCL software systems capable of supporting and enhancing the current on-line collaborative learning practices.     

计算机软件的日常管理与维护研究

随着科技的高速发展,计算机已经变得与我们的生活息息相关.而针对于计算机的使用,我们除了了解正确的使用方法,更要明白计算机的日常管理与维护.计算机的软硬件对于计算机而言是不可或缺的两个部分.计算机软件,是指计算机程序中的数据文档和程序.软件是以及计算机及计算机正常运营的重要依据.所以,我们在使用计算机软件的时候,也必须主义对于计算机软件日常管理和维护的进行深入研究.