Teaching programming at a distance: the Internet software visualization laboratory

This paper describes recent developments in our approach to teaching computer programming in the context of a part-time Masters course taught at a distance. Within our course, students are sent a pack which contains integrated text, software and video course material, using a uniform graphical representation to tell a consistent story of how the programming language works. The students communicate with their tutors over the phone and through surface mail. Through our empirical studies and experience teaching the course we have identified four current problems: (i) students' difficulty mapping between the graphical representations used in the course and the programs to which they relate, (ii) the lack of a conversational context for tutor help provided over the telephone, (iii) helping students who due to their other commitments tend to study at 'unsociable' hours, and (iv) providing software for the constantly changing and expanding range of platforms and operating systems used by students. We hope to alleviate these problems through our Internet Software Visualization Laboratory (ISVL), which supports individual exploration, and both synchronous and asynchronous communication. As a single user, students are aided by the extra mappings provided between the graphical representations used in the course and their computer programs, overcoming the problems of the original notation. ISVL can also be used as a synchronous communication medium whereby one of the users (generally the tutor) can provide an annotated demonstration of a program and its execution, a far richer alternative to technical discussions over the telephone. Finally, ISVL can be used to support asynchronous communication, helping students who work at unsociable hours by allowing the tutor to prepare short educational movies for them to view when convenient. The ISVL environment runs on a conventional web browser and is therefore platform independent, has modest hardware and bandwidth requirements, and is easy to distribute and maintain. Our planned experiments with ISVL will allow us to investigate ways in which new technology can be most appropriately applied in the service of distance education

Report on the Dagstuhl Seminar on Visualization and Monitoring of Network Traffic

The Dagstuhl Seminar on Visualization and Monitoring of Network Traffic took place May 17-20, 2009 in Dagstuhl, Germany. Dagstuhl seminars promote personal interaction and open discussion of results as well as new ideas. Unlike at most conferences, the focus is not solely on the presentation of established results but also, and in equal parts, to presentation of results, ideas, sketches, and open problems. The aim of this particular seminar was to bring together experts from the information visualization community and the networking community in order to discuss the state of the art of monitoring and visualization of network traffic. People from the different research communities involved jointly organized the seminar. The co-chairs of the seminar from the networking community were Aiko Pras (University of Twente) and Jürgen Schönwälder (Jacobs University Bremen). The co-chairs from the visualization community were Daniel A. Keim (University of Konstanz) and Pak Chung Wong (Pacific Northwest National Laboratory). Florian Mansmann (University of Konstanz) helped with producing this report. The seminar was organized and supported by Schloss Dagstuhl and the European Network of Excellence for the Management of Internet Technologies and Complex Systems (EMANICS)

Updates in metabolomics tools and resources: 2014-2015

Data processing and interpretation represent the most challenging and time-consuming steps in high-throughput metabolomic experiments, regardless of the analytical platforms (MS or NMR spectroscopy based) used for data acquisition. Improved machinery in metabolomics generates increasingly complex datasets that create the need for more and better processing and analysis software and in silico approaches to understand the resulting data. However, a comprehensive source of information describing the utility of the most recently developed and released metabolomics resources—in the form of tools, software, and databases—is currently lacking. Thus, here we provide an overview of freely-available, and open-source, tools, algorithms, and frameworks to make both upcoming and established metabolomics researchers aware of the recent developments in an attempt to advance and facilitate data processing workflows in their metabolomics research. The major topics include tools and researches for data processing, data annotation, and data visualization in MS and NMR-based metabolomics. Most in this review described tools are dedicated to untargeted metabolomics workflows; however, some more specialist tools are described as well. All tools and resources described including their analytical and computational platform dependencies are summarized in an overview Table

Novel proposal for prediction of CO2 course and occupancy recognition in Intelligent Buildings within IoT

Many direct and indirect methods, processes, and sensors available on the market today are used to monitor the occupancy of selected Intelligent Building (IB) premises and the living activities of IB residents. By recognizing the occupancy of individual spaces in IB, IB can be optimally automated in conjunction with energy savings. This article proposes a novel method of indirect occupancy monitoring using CO2, temperature, and relative humidity measured by means of standard operating measurements using the KNX (Konnex (standard EN 50090, ISO/IEC 14543)) technology to monitor laboratory room occupancy in an intelligent building within the Internet of Things (IoT). The article further describes the design and creation of a Software (SW) tool for ensuring connectivity of the KNX technology and the IoT IBM Watson platform in real-time for storing and visualization of the values measured using a Message Queuing Telemetry Transport (MQTT) protocol and data storage into a CouchDB type database. As part of the proposed occupancy determination method, the prediction of the course of CO2 concentration from the measured temperature and relative humidity values were performed using mathematical methods of Linear Regression, Neural Networks, and Random Tree (using IBM SPSS Modeler) with an accuracy higher than 90%. To increase the accuracy of the prediction, the application of suppression of additive noise from the CO2 signal predicted by CO2 using the Least mean squares (LMS) algorithm in adaptive filtering (AF) method was used within the newly designed method. In selected experiments, the prediction accuracy with LMS adaptive filtration was better than 95%.Web of Science1223art. no. 454