Flexible Natural Language Generation in Multiple Contexts

We present a practical approach to Natural Language Generation (NLG) for spoken dialogue systems. The approach is based on small template fragments (mini-templates). The system’s object architecture facilitates generation of phrases across pre-defined business domains and registers, as well as into different languages. The architecture simplifies NLG in well-understood application contexts, while providing the flexibility for a developer and for the system, to vary linguistic output according to dialogue context, including any intended affective impact. Mini-templates are used with a suite of domain term objects, resulting in an NLG system (MINTGEN – MINi-Template GENerator) whose extensibility and ease of maintenance is enhanced by the sparsity of information devoted to individual domains. The system also avoids the need for specialist linguistic competence on the part of the system maintainer.

Growth of Gold Nanocrystals Incorporated with Magnetic Microbead-based Separation as a Tool for Quantification of Biological Interactions

Au nanoparticles (AuNPs) have been widely used not only as optical labels or ‘weight” labels for the detections of biorecognition events but also an amplifier of surface plasmon resonance biosensors. The intrinsic property of gold nuclei composing of a group of Au atoms to catalyze the reduction of metal ions on the NPs and thereby to enlarge the metallic nanoparticles is employed in different biosensing paths. In a solution containing Au+ ions (e.g. HAuCl4) and the Au clusters, hydrated electrons which are reduced from oxidation of reducers (H2O2, sodium citrate, ascorbic acid, or NaBH4) will be used to reduce the Au+ ion leading to the deposition of Au+ to the Au0 (Au clusters). The reaction will be catalyzed continuously by the Au0 until the Au+ ions and hydrated electrons are exhausted. As a result, the AuNPs will be grown and their optical properties are also changed. If the AuNP nanoclusters are used as probes, the color change will be dependent on amount of analytes, thus give a quantitative monitoring of the analytes.

In this study, we incorporate the use of magnetic beads with the nanocrystalline growth to quantify a target protein based on immunoreactions. Prostate specific antigen (PSA) is chosen as the target analyte because of its values in diagnosis of prostate cancer. A double-sandwiched immunoassay is performed by gold-tagged monoclonal PSA antibody-PSA antigen – magnetic bead-tagged polyclonal PSA antibody interactions. After the immunoreactions, the target analytes are preconcentrated and separated by the magnetic beads while the nanogrowth plays a role of colorimetric signal developer.

The result shows that this is a very sensitive, robust and excellent strategy to detect biological interactions. PSA antigen is detected at femtomolar level with very high specificity under the presence of undesired proteins of crude samples. Furthermore, the method also shows great potential to detect other biological interactions. More details will be described in our presentation.

Strange, But True? Object-oriented Programming Is Best Taught, And Learnt, While Sitting On The Floor.

There is a perception amongst some of those learning computer programming that the principles of object-oriented programming (where behaviour is often encapsulated across multiple class files) can be difficult to grasp, especially when taught through a traditional, didactic ‘talk-and-chalk’ method or in a lecture-based environment.
We propose a non-traditional teaching method, developed for a government funded teaching training project delivered by Queen’s University, we call it bigCode. In this scenario, learners are provided with many printed, poster-sized fragments of code (in this case either Java or C#). The learners sit on the floor in groups and assemble these fragments into the many classes which make-up an object-oriented program.
Early trials indicate that bigCode is an effective method for teaching object-orientation. The requirement to physically organise the code fragments imitates closely the thought processes of a good software developer when developing object-oriented code.
Furthermore, in addition to teaching the principles involved in object-orientation, bigCode is also an extremely useful technique for teaching learners the organisation and structure of individual classes in Java or C# (as well as the organisation of procedural code). The mechanics of organising fragments of code into complete, correct computer programs give the users first-hand practice of this important skill, and as a result they subsequently find it much easier to develop well-structured code on a computer.
Yet, open questions remain. Is bigCode successful only because we have unknowingly predominantly targeted kinesthetic learners? Is bigCode also an effective teaching approach for other forms of learners, such as visual learners? How scalable is bigCode: in its current form can it be used with large class sizes, or outside the classroom?

Exploiting Significance of Computations for Energy-Constrained Approximate Computing

Approximate execution is a viable technique for environments with energy constraints, provided that applications are given the mechanisms to produce outputs of the highest possible quality within the available energy budget. This paper introduces a framework for energy-constrained execution with controlled and graceful quality loss. A simple programming model allows developers to structure the computation in different tasks, and to express the relative importance of these tasks for the quality of the end result. For non-significant tasks, the developer can also supply less costly, approximate versions. The target energy consumption for a given execution is specified when the application is launched. A significance-aware runtime system employs an application-specific analytical energy model to decide how many cores to use for the execution, the operating frequency for these cores, as well as the degree of task approximation, so as to maximize the quality of the output while meeting the user-specified energy constraints. Evaluation on a dual-socket 16-core Intel platform using 9 benchmark kernels shows that the proposed framework picks the optimal configuration with high accuracy. Also, a comparison with loop perforation (a well-known compile-time approximation technique), shows that the proposed framework results in significantly higher quality for the same energy budget.

The Changing Face of Neolithic and Bronze Age Ireland: A Big Data Approach to the Settlement and Burial Records

This paper synthesizes and discusses the spatial and temporal patterns of archaeological sites in Ireland, spanning the Neolithic period and the Bronze Age transition (4300–1900 cal BC), in order to explore the timing and implications of the main changes that occurred in the archaeological record of that period. Large amounts of new data are sourced from unpublished developer-led excavations and combined with national archives, published excavations and online databases. Bayesian radiocarbon models and context- and sample-sensitive summed radiocarbon probabilities are used to examine the dataset. The study captures the scale and timing of the initial expansion of Early Neolithic settlement and the ensuing attenuation of all such activity—an apparent boom-and-bust cycle. The Late Neolithic and Chalcolithic periods are characterised by a resurgence and diversification of activity. Contextualisation and spatial analysis of radiocarbon data reveals finer-scale patterning than is usually possible with summed-probability approaches: the boom-and-bust models of prehistoric populations may, in fact, be a misinterpretation of more subtle demographic changes occurring at the same time as cultural change and attendant differences in the archaeological record.