SketchIT: A Sketch Interpretation Tool for Conceptual Mechanical Design

We describe a program called SketchIT capable of producing multiple families of designs from a single sketch. The program is given a rough sketch (drawn using line segments for part faces and icons for springs and kinematic joints) and a description of the desired behavior. The sketch is "rough" in the sense that taken literally, it may not work. From this single, perhaps flawed sketch and the behavior description, the program produces an entire family of working designs. The program also produces design variants, each of which is itself a family of designs. SketchIT represents each family of designs with a "behavior ensuring parametric model" (BEP-Model), a parametric model augmented with a set of constraints that ensure the geometry provides the desired behavior. The construction of the BEP-Model from the sketch and behavior description is the primary task and source of difficulty in this undertaking. SketchIT begins by abstracting the sketch to produce a qualitative configuration space (qc-space) which it then uses as its primary representation of behavior. SketchIT modifies this initial qc-space until qualitative simulation verifies that it produces the desired behavior. SketchIT's task is then to find geometries that implement this qc-space. It does this using a library of qc-space fragments. Each fragment is a piece of parametric geometry with a set of constraints that ensure the geometry implements a specific kind of boundary (qcs-curve) in qc-space. SketchIT assembles the fragments to produce the BEP-Model. SketchIT produces design variants by mapping the qc-space to multiple implementations, and by transforming rotating parts to translating parts and vice versa.

The Sales Process Practice of Food and Drink SMEs - A Holistic Conceptual Framework: Steps, dimensions, barriers and enablers.

Purpose –The research examines the sales process practised by SMEs, and barriers and enablers that hinder and support effective selling practices from the selling organisation’s perspective in Scottish-based Food and Drink firms. Design/methodology approach - – The paper adopts an interpretivist perspective with qualitative data gathered through face-to-face semi-structured interviews. 20 people involved in selling activities were interviewed from 15 SMEs across Scotland. Thematic analysis established key findings regarding the sales process practice. Findings – Five themes emerged that affect the operationalisation of the selling process: the owner manager has considerable involvement in the sales process, SMEs with some degree of sales knowledge take a more systematic approach, SMEs lack awareness of how CRM technology can assist them, power is tipped in favour of the buyer and, the geographic location of the SME places constraints on how SMEs conduct business Research limitation/implication – Thematic analysis was chosen over other more traditional methods due to the lack of relevant quantitative data. The phenomenon of the research and research methodology means that it will not be possible to repeat this study and replicate its findings. However, the process that has been adopted does provide a basis for future research. Originality/value - The paper identifies areas where future research is required in the field alongside suggestions where policy makers and government business agencies might focus intervention to assist SMEs improve delivery of the sales process and selling effectiveness

Tactile Sensing for Mechatronics: A State of the Art Survey

Lee M.H. and Nicholls H.R., Tactile Sensing for Mechatronics: A State of the Art Survey, Mechatronics, 9, Jan 1999, pp1-31.

ART: An ontology based tool for the translation of papers into Semantic Web format

To be presented at SIG/ISMB07 ontology workshop: http://bio-ontologies.org.uk/index.php To be published in BMC Bioinformatics. Sponsorship: JISC

From Art to Avant Garde? Television, Formalism and the Arts Documentary in 1960's Britain

Sexton, J. (2008). From Art to Avant Garde? Television, Formalism and the Arts Documentary in 1960's Britain. In L. Mulvey and J. Sexton (Eds.), Experimental British Television (pp.89-105). Manchester: Manchester University Press. RAE2008

Appreciating ecological complexity: Habitat contours as a conceptual landscape model

Joern Fischer, David B. Lindermayer, and Ioan Fazey (2004). Appreciating Ecological Complexity: Habitat Contours as a Conceptual Landscape Model. Conservation Biology, 18 (5)pp.1245-1253 RAE2008

O antropomorfizowaniu i potrzebie przedstawień figuralnych w sztuce greckiej

Estetyka w archeologii. Antropomorfizacje w pradziejach i starożytności, eds. E. Bugaj, A. P. Kowalski, Poznań: Wydawnictwo Poznańskie.

Bring ART into the ACT

ACT is compared with a particular type of connectionist model that cannot handle symbols and use non-biological operations that cannot learn in real time. This focus continues an unfortunate trend of straw man "debates" in cognitive science. Adaptive Resonance Theory, or ART, neural models of cognition can handle both symbols and sub-symbolic representations, and meets the Newell criteria at least as well as these models.

Biased ART: A Neural Architecture that Shifts Attention Toward Previously Disregarded Features Following an Incorrect Prediction

Memories in Adaptive Resonance Theory (ART) networks are based on matched patterns that focus attention on those portions of bottom-up inputs that match active top-down expectations. While this learning strategy has proved successful for both brain models and applications, computational examples show that attention to early critical features may later distort memory representations during online fast learning. For supervised learning, biased ARTMAP (bARTMAP) solves the problem of over-emphasis on early critical features by directing attention away from previously attended features after the system makes a predictive error. Small-scale, hand-computed analog and binary examples illustrate key model dynamics. Twodimensional simulation examples demonstrate the evolution of bARTMAP memories as they are learned online. Benchmark simulations show that featural biasing also improves performance on large-scale examples. One example, which predicts movie genres and is based, in part, on the Netflix Prize database, was developed for this project. Both first principles and consistent performance improvements on all simulation studies suggest that featural biasing should be incorporated by default in all ARTMAP systems. Benchmark datasets and bARTMAP code are available from the CNS Technology Lab Website: http://techlab.bu.edu/bART/.

On the Relationship Between the Generalized Equality Classifier and ART 2 Neural Networks

In this paper, we introduce the Generalized Equality Classifier (GEC) for use as an unsupervised clustering algorithm in categorizing analog data. GEC is based on a formal definition of inexact equality originally developed for voting in fault tolerant software applications. GEC is defined using a metric space framework. The only parameter in GEC is a scalar threshold which defines the approximate equality of two patterns. Here, we compare the characteristics of GEC to the ART2-A algorithm (Carpenter, Grossberg, and Rosen, 1991). In particular, we show that GEC with the Hamming distance performs the same optimization as ART2. Moreover, GEC has lower computational requirements than AR12 on serial machines.

ART-EMAP: A Neural Network Architecture for Learning and Prediction by Evidence Accumulation

This paper introduces ART-EMAP, a neural architecture that uses spatial and temporal evidence accumulation to extend the capabilities of fuzzy ARTMAP. ART-EMAP combines supervised and unsupervised learning and a medium-term memory process to accomplish stable pattern category recognition in a noisy input environment. The ART-EMAP system features (i) distributed pattern registration at a view category field; (ii) a decision criterion for mapping between view and object categories which can delay categorization of ambiguous objects and trigger an evidence accumulation process when faced with a low confidence prediction; (iii) a process that accumulates evidence at a medium-term memory (MTM) field; and (iv) an unsupervised learning algorithm to fine-tune performance after a limited initial period of supervised network training. ART-EMAP dynamics are illustrated with a benchmark simulation example. Applications include 3-D object recognition from a series of ambiguous 2-D views.

Sequential Memory with ART: A Self-Organizing Network Capable of Learning Sequences of Patterns

A model which extends the adaptive resonance theory model to sequential memory is presented. This new model learns sequences of events and recalls a sequence when presented with parts of the sequence. A sequence can have repeated events and different sequences can share events. The ART model is modified by creating interconnected sublayers within ART's F2 layer. Nodes within F2 learn temporal patterns by forming recency gradients within LTM. Versions of the ART model like ART I, ART 2, and fuzzy ART can be used.

ART-EMAP: A Neural Network Architecture for Object Recognition by Evidence Accumulation

A new neural network architecture is introduced for the recognition of pattern classes after supervised and unsupervised learning. Applications include spatio-temporal image understanding and prediction and 3-D object recognition from a series of ambiguous 2-D views. The architecture, called ART-EMAP, achieves a synthesis of adaptive resonance theory (ART) and spatial and temporal evidence integration for dynamic predictive mapping (EMAP). ART-EMAP extends the capabilities of fuzzy ARTMAP in four incremental stages. Stage 1 introduces distributed pattern representation at a view category field. Stage 2 adds a decision criterion to the mapping between view and object categories, delaying identification of ambiguous objects when faced with a low confidence prediction. Stage 3 augments the system with a field where evidence accumulates in medium-term memory (MTM). Stage 4 adds an unsupervised learning process to fine-tune performance after the limited initial period of supervised network training. Each ART-EMAP stage is illustrated with a benchmark simulation example, using both noisy and noise-free data. A concluding set of simulations demonstrate ART-EMAP performance on a difficult 3-D object recognition problem.