Virtual Machines and Networks - Installation, Performance Study, Advantages and Virtualization Options

The interest in virtualization has been growing rapidly in the IT industry because of inherent benefits like better resource utilization and ease of system manageability. The experimentation and use of virtualization as well as the simultaneous deployment of virtual software are increasingly getting popular and in use by educational institutions for research and teaching. This paper stresses on the potential advantages associated with virtualization and the use of virtual machines for scenarios, which cannot be easily implemented and/or studied in a traditional academic network environment, but need to be explored and experimented by students to meet the raising needs and knowledge-base demanded by the IT industry. In this context, we discuss various aspects of virtualization - starting from the working principle of virtual machines, installation procedure for a virtual guest operating system on a physical host operating system, virtualization options and a performance study measuring the throughput obtained on a network of virtual machines and physical host machines. In addition, the paper extensively evaluates the use of virtual machines and virtual networks in an academic environment and also specifically discusses sample projects on network security, which may not be feasible enough to be conducted in a physical network of personal computers; but could be conducted only using virtual machines

Application of a virtual scientific experiment model in different educational contexts

E-learning practice is continuously using experimentation in order to enhance the basic information transfer model where knowledge is passed from the system/ tutors to the students. Boosting student productivity through on-line experimentation is not simple since many organizational, educational and technological issues need to be dealt with. This work describes the application of a Learning Model for Virtual Scientific Experiments (VSEs) in two different scenarios: Information and Communication Technologies and Physics. As part of the first, a VSE for Wireless Sensor Networks was specified and deployed while the second involved the specification and design of a collaborative VSE for physics experiments. Preliminary implementation and deployment results are also discussed

Succesful teaching of experimental vibration research

For more than 20 years, master students have been offered a practical training on experimental vibration research by the Structural Dynamics & Acoustics Section of the University of Twente. The basic theoretical knowledge, necessary to attend this practical training, is provided for the Master part of their study and it consists of a series of lectures on advanced dynamics, measurement techniques and the concept of modal analysis. The practical training consists of performing vibration experiments on a well defined simple structure. Use is made of a digital signal processing (DSP) Siglab system, together with ME'scope as analysis tool. In order to guarantee maximal transfer of knowledge toward the participants, small groups consisting of two students are formed. These groups are supervised by an experienced tutor, who intensively monitors the progress of the practical training. It lasts one day and the students have to write down their findings in a report. In order to attend the practical training in an efficient way, students have to study the theoretical basics of experimental vibration research in advance. In order to achieve an optimal preparation to the practical, a ‘virtual’ vibration measurement based on Labview is developed for the next academic year. Students will thus be able to run this experiment remotely from behind their PC by activating a real-life test case placed in the laboratory. In this paper the content and execution of the practical training is described. The experience of the authors is that the vast amount of interesting educational ingredients contributes to a profound understanding of both theoretical and experimental vibration research for Mechanical Engineering students

ONLINE MONITORING USING KISMET

Colleges and universities currently use online exams for student evaluation. Stu- dents can take assigned exams using their laptop computers and email their results to their instructor; this process makes testing more efficient and convenient for both students and faculty. However, taking exams while connected to the Internet opens many opportunities for plagiarism and cheating. In this project, we design, implement, and test a tool that instructors can use to monitor the online activity of students during an in-class online examination. This tool uses a wireless sniffer, Kismet, to capture and classify packets in real time. If a student attempts to access a site that is not allowed, the instructor is notified via an Android application or via Internet. Identifying a student who is cheating is challenging since many applications send packets without user intervention. We provide experimental results from realistic test environments to illustrate the success of our proposed approach

Personalising Vibrotactile Displays through Perceptual Sensitivity Adjustment

Haptic displays are commonly limited to transmitting a discrete set of tactile motives. In this paper, we explore the transmission of real-valued information through vibrotactile displays. We simulate spatial continuity with three perceptual models commonly used to create phantom sensations: the linear, logarithmic and power model. We show that these generic models lead to limited decoding precision, and propose a method for model personalization adjusting to idiosyncratic and spatial variations in perceptual sensitivity. We evaluate this approach using two haptic display layouts: circular, worn around the wrist and the upper arm, and straight, worn along the forearm. Results of a user study measuring continuous value decoding precision show that users were able to decode continuous values with relatively high accuracy (4.4% mean error), circular layouts performed particularly well, and personalisation through sensitivity adjustment increased decoding precision

Becoming the Expert - Interactive Multi-Class Machine Teaching

Compared to machines, humans are extremely good at classifying images into categories, especially when they possess prior knowledge of the categories at hand. If this prior information is not available, supervision in the form of teaching images is required. To learn categories more quickly, people should see important and representative images first, followed by less important images later - or not at all. However, image-importance is individual-specific, i.e. a teaching image is important to a student if it changes their overall ability to discriminate between classes. Further, students keep learning, so while image-importance depends on their current knowledge, it also varies with time. In this work we propose an Interactive Machine Teaching algorithm that enables a computer to teach challenging visual concepts to a human. Our adaptive algorithm chooses, online, which labeled images from a teaching set should be shown to the student as they learn. We show that a teaching strategy that probabilistically models the student's ability and progress, based on their correct and incorrect answers, produces better 'experts'. We present results using real human participants across several varied and challenging real-world datasets.Comment: CVPR 201