The Blue Obelisk Community

Poster presented at the VSMF symposium held at the Unilever Centre on 2011-01-17The Internet has brought together a group of chemists who are driven by wanting to do things better, but are frustrated with the Closed systems that chemists currently have to work with. they share a belief in the concepts of Open Data, Open Standards and Open Source. And they express this in software, data, algorithms, specifications, tutorials, demonstrations, articles and anything that helps get the message across. [http://www.blueobelisk.org/

K-Space Interactive Search

In this paper we will present the K-Space1 Interactive Search system for content-based video information retrieval to be demonstrated in the VideOlympics. This system is an exten-sion of the system we developed as part of our participation in TRECVID 2007 [1]. In TRECVID 2007 we created two interfaces, known as the ‘Shot’ based and ‘Broadcast’ based interfaces. Our VideOlympics submission takes these two in-terfaces and the lessons learned from our user experiments, to create a single user interface which attempts to leverage the best aspects of both

The role of dispositional reinvestment in choking during decision-making tasks in sport

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.This thesis examines the moderating effect of dispositional reinvestment upon ‘choking’ in cognitive based tasks such as decision-making. Study 1 tested sixty-three participants’ performances on low- and high-complexity tests of motor skill, psychomotor skill and working memory under low- and high-pressure conditions. The association between reinvestment and choking was shown to extend beyond the motor skill domain to cognitive tasks, particularly those that tax working memory, with task complexity moderating this relationship. Next, a psychometric scale to identify individuals more susceptible to impaired decision-making under pressure was developed. A 13-item decision-specific version of the Reinvestment Scale (Masters, Polman, & Hammond, 1993) measuring an individual’s propensity to engage in conscious control and manifestations of ruminative thoughts emerged following factor analysis. Initial assessment of the scale’s predictive validity showed scores were highly correlated with coaches’ ratings of players’ tendency to choke. The final two studies examined choking using sport specific decision-making tasks. Initial findings were inconclusive, as choking was not observed. It was suggested the task lacked the sufficient cognitive demands to induce reinvestment. The last study, manipulating task complexity, found dispositional reinvestment to be associated with choking in the high complexity condition. The Decision-Specific Reinvestment Scale was also shown to be a better predictor of choking than the original scale. Overall, support was found for the hypothesis that Reinvestment is detrimental to performance under pressure in cognitive based tasks; however may not be the sole cause of disrupted performance. Masters and Maxwell’s (2004) concept of a working memory based explanation and Mullen and Hardy (2000) attentional threshold hypothesis offer a potential explanation to the findings

Legal Tender Cases in the Legal History of the United States

The purpose of this thesis is to find in the financial and legal entanglement surrounding the legal tender cases the salient causes which brought them into being and also the deciding factors which solved the. They were enacted at the beginning of the Civil War and it is our purpose to find out their ultimate causes; whether they were the natural accompaniment of war-time hysteria or the outcome of financial policy. Secondly, it is our purpose to show the fundament­al law and hidden causes which resulted in the legal tender decisions

Inexpensive fusion methods for enhancing feature detection

Recent successful approaches to high-level feature detection in image and video data have treated the problem as a pattern classification task. These typically leverage the techniques learned from statistical machine learning, coupled with ensemble architectures that create multiple feature detection models. Once created, co-occurrence between learned features can be captured to further boost performance. At multiple stages throughout these frameworks, various pieces of evidence can be fused together in order to boost performance. These approaches whilst very successful are computationally expensive, and depending on the task, require the use of significant computational resources. In this paper we propose two fusion methods that aim to combine the output of an initial basic statistical machine learning approach with a lower-quality information source, in order to gain diversity in the classified results whilst requiring only modest computing resources. Our approaches, validated experimentally on TRECVid data, are designed to be complementary to existing frameworks and can be regarded as possible replacements for the more computationally expensive combination strategies used elsewhere

TRECVid 2006 experiments at Dublin City University

In this paper we describe our retrieval system and experiments performed for the automatic search task in TRECVid 2006. We submitted the following six automatic runs: • F A 1 DCU-Base 6: Baseline run using only ASR/MT text features. • F A 2 DCU-TextVisual 2: Run using text and visual features. • F A 2 DCU-TextVisMotion 5: Run using text, visual, and motion features. • F B 2 DCU-Visual-LSCOM 3: Text and visual features combined with concept detectors. • F B 2 DCU-LSCOM-Filters 4: Text, visual, and motion features with concept detectors. • F B 2 DCU-LSCOM-2 1: Text, visual, motion, and concept detectors with negative concepts. The experiments were designed both to study the addition of motion features and separately constructed models for semantic concepts, to runs using only textual and visual features, as well as to establish a baseline for the manually-assisted search runs performed within the collaborative K-Space project and described in the corresponding TRECVid 2006 notebook paper. The results of the experiments indicate that the performance of automatic search can be improved with suitable concept models. This, however, is very topic-dependent and the questions of when to include such models and which concept models should be included, remain unanswered. Secondly, using motion features did not lead to performance improvement in our experiments. Finally, it was observed that our text features, despite displaying a rather poor performance overall, may still be useful even for generic search topics

Measuring the impact of temporal context on video retrieval

In this paper we describe the findings from the K-Space interactive video search experiments in TRECVid 2007, which examined the effects of including temporal context in video retrieval. The traditional approach to presenting video search results is to maximise recall by offering a user as many potentially relevant shots as possible within a limited amount of time. ‘Context’-oriented systems opt to allocate a portion of theresults presentation space to providing additional contextual cues about the returned results. In video retrieval these cues often include temporal information such as a shot’s location within the overall video broadcast and/or its neighbouring shots. We developed two interfaces with identical retrieval functionality in order to measure the effects of such context on user performance. The first system had a ‘recall-oriented’ interface, where results from a query were presented as a ranked list of shots. The second was ‘contextoriented’, with results presented as a ranked list of broadcasts. 10 users participated in the experiments, of which 8 were novices and 2 experts. Participants completed a number of retrieval topics using both the recall-oriented and context-oriented systems

TRECVid 2007 experiments at Dublin City University

In this paper we describe our retrieval system and experiments performed for the automatic search task in TRECVid 2007. We submitted the following six automatic runs: • F A 1 DCU-TextOnly6: Baseline run using only ASR/MT text features. • F A 1 DCU-ImgBaseline4: Baseline visual expert only run, no ASR/MT used. Made use of query-time generation of retrieval expert coefficients for fusion. • F A 2 DCU-ImgOnlyEnt5: Automatic generation of retrieval expert coefficients for fusion at index time. • F A 2 DCU-imgOnlyEntHigh3: Combination of coefficient generation which combined the coefficients generated by the query-time approach, and the index-time approach, with greater weight given to the index-time coefficient. • F A 2 DCU-imgOnlyEntAuto2: As above, except that greater weight is given to the query-time coefficient that was generated. • F A 2 DCU-autoMixed1: Query-time expert coefficient generation that used both visual and text experts