When will we learn: key factors and potential barriers

The overall aim of this research was to improve the dissemination of Lessons Learned in construction projects so that contractors’ project teams have access to the most relevant lessons at the most appropriate time, in the most appropriate format. The outcome of the research aimed to provide (1) an understanding of the different systems and tools used for recording Lessons Learned amongst major construction contractors; (2) an understanding of the needs in terms of what sort of lessons are required, the level of detail required and how best these should be made available; and (3) an approach on how best to disseminate Lessons Learned. The key objectives of the research were to: 1. Investigate current practice for recording and disseminating Lessons Learned; 2. Identify potential barriers for successfully disseminating Lessons Learned; and 3. Identify key factors affecting company processes to encourage a more systematic dissemination of Lessons Learned. The study was conducted in three phases. The first investigated contractors’ current practices for recording and disseminating Lessons Learned through a questionnaire survey. The second phase identified key factors that would encourage the institutionalisation of Lessons Learned and also the factors that inhibit their use. The third phase examined how current processes could be adapted to develop a process that would embed the systematic dissemination of Lessons Learned within an organisation’s existing practices. This report focuses on the second stage of the project that identified from the end users those factors that would encourage the institutionalisation of Lessons Learned and also the factors that inhibit their use of Lessons Learned

Aspects of opening play

In this paper, we study opening play in games. We show experiments using minimax and a semi-random player. In the experiment, we let each semi-random player use an opening-book, created by different player. Results show evidence for the following statements. The game length increases. Expert player against an intermediate player should not use an opening book in a tournament match. Some opening books are good for novices and some opening books are bad for novices. The game outcome will approach the outcome of the game when the opening book was created, and if a grandmaster creates the opening book then the outcome will be the same as the grandmaster’s

Practice and theory:mixing labs and small group tutorials

While appropriate for practical topics like SQL, our traditional format of lecture and lab fails to facilitate the discussion of more theoretical database topics with students. This paper describes and analyses the method and effects of adopting a more flexible approach with third year and postgraduate students. Some weeks use supervised labs while in others tutorials are held in seminar rooms, in smaller groups, without the distraction of computers. Requiring tutorials to be prepared in advance allows time to be used effectively, concentrating on more difficult aspects.Initial results, presented in this paper, are encouraging. Many students enjoy tutorials and exam performance has improved dramatically for some. However, as many as 25% of undergraduate students failed to attend a single tutorial, and many of those who did attend came unprepared. Could, and should, this be changed by explicitly assessing tutorials? The paper concludes by investigating approaches reported elsewhere in order to ascertain how the management of tutorials could be improved

Blacon Sure Start parent satisfaction survey

This report evaluates Sure Start in Blacon during 2003.Commissioned and funded by Blacon Sure Start

Rapid Sampling for Visualizations with Ordering Guarantees

Visualizations are frequently used as a means to understand trends and gather insights from datasets, but often take a long time to generate. In this paper, we focus on the problem of rapidly generating approximate visualizations while preserving crucial visual proper- ties of interest to analysts. Our primary focus will be on sampling algorithms that preserve the visual property of ordering; our techniques will also apply to some other visual properties. For instance, our algorithms can be used to generate an approximate visualization of a bar chart very rapidly, where the comparisons between any two bars are correct. We formally show that our sampling algorithms are generally applicable and provably optimal in theory, in that they do not take more samples than necessary to generate the visualizations with ordering guarantees. They also work well in practice, correctly ordering output groups while taking orders of magnitude fewer samples and much less time than conventional sampling schemes.Comment: Tech Report. 17 pages. Condensed version to appear in VLDB Vol. 8 No.

An evaluation framework for stereo-based driver assistance

This is the post-print version of the Article - Copyright @ 2012 Springer VerlagThe accuracy of stereo algorithms or optical flow methods is commonly assessed by comparing the results against the Middlebury database. However, equivalent data for automotive or robotics applications rarely exist as they are difficult to obtain. As our main contribution, we introduce an evaluation framework tailored for stereo-based driver assistance able to deliver excellent performance measures while circumventing manual label effort. Within this framework one can combine several ways of ground-truthing, different comparison metrics, and use large image databases. Using our framework we show examples on several types of ground truthing techniques: implicit ground truthing (e.g. sequence recorded without a crash occurred), robotic vehicles with high precision sensors, and to a small extent, manual labeling. To show the effectiveness of our evaluation framework we compare three different stereo algorithms on pixel and object level. In more detail we evaluate an intermediate representation called the Stixel World. Besides evaluating the accuracy of the Stixels, we investigate the completeness (equivalent to the detection rate) of the StixelWorld vs. the number of phantom Stixels. Among many findings, using this framework enables us to reduce the number of phantom Stixels by a factor of three compared to the base parametrization. This base parametrization has already been optimized by test driving vehicles for distances exceeding 10000 km

Online Circular Calendar

A calendar is a system to organize days for social, commercial or administrative purpose. Many calendar systems are available today. The calendar system helps the user in scheduling his/her events or tasks over a time period. This period may be an hour, a day, or even months. Due to increase in user’s activities, events that need to be scheduled in the calendar grow tremendously. Moreover, there are events that occur every year which require a good visualization for mental manipulation. As a result there is a difficulty in organizing these events in the current calendar system. The main idea of this project is to provide a calendar system in which users can organize the events easily, and to close the gap between the actual software and the mental model of the users. None of the current calendar systems have the ability to manipulate and plot graphs throughout the year. The data is user dependent and can be of any sort like temperature, rainfall, stock analysis etc., Apart from this; good visualization techniques can be used for the calendar system to make the events apparent to the users. By this way user can view the overall picture of the events and will have clear idea about their events. This paper describes the implementation of such a calendar system with good visualization

Chess Endgames and Neural Networks

The existence of endgame databases challenges us to extract higher-grade information and knowledge from their basic data content. Chess players, for example, would like simple and usable endgame theories if such holy grail exists: endgame experts would like to provide such insights and be inspired by computers to do so. Here, we investigate the use of artificial neural networks (NNs) to mine these databases and we report on a first use of NNs on KPK. The results encourage us to suggest further work on chess applications of neural networks and other data-mining techniques