Jim Davies - Profile on Academia.edu (original) (raw)

Papers by Jim Davies

Non-photorealistic rendering (NPR) has been used to produce stylized images, e.g., in a stippled ... more Non-photorealistic rendering (NPR) has been used to produce stylized images, e.g., in a stippled or painted style. To evaluate NPR algorithms, similarity measurements used in image processing have been employed to assess the quality of rendered images. However, there is no standard objective measurement of stylization quality. In many cases, raw side-by-side comparisons are used to demonstrate improvements in aesthetic quality. This means of comparison often fails to be persuasive due to the small size of demonstrations and the subjective choice of images. We conducted a user study and examined responses of 30 subjects in order to determine two things: whether there exists a relationship between the structural quality and aesthetic quality of non-colored non-photorealistic images; and whether the choice of images matters for side-by-side comparisons. Our study revealed a statistically significant correlation between the aesthetic and structure ratings given by participants: increases in structural rating coincided with increases in aesthetic rating. Second, participants' ratings of structure and aesthetic were influenced by image content: that is, choice of input images influenced the results of side-by-side comparisons.

Proceedings of the 2nd ACM International Workshop on Immersive Media Experiences, 2014

Aphasia is a disease that renders its victims unable to effectively use language. Evidence suppor... more Aphasia is a disease that renders its victims unable to effectively use language. Evidence supports the efficacy of treatment for aphasia yet the effectiveness or transferability of learned communicative abilities to everyday conversation continues to be investigated. In this paper we explore an alternative approach to aphasia treatment based on the art and science of storytelling. Inherent in storytelling are the motivations to share an experience, the cognitive abilities to organize story, and the language system to convey the experience. This approach is based on decades of research in aphasia therapy and immersive storytelling (in other fields) and has been used to engage a subject's creativity and emotions to produce transformative results in real life. We report on early, promising results that could radically innovate the rehabilitative practice of aphasia.

Lecture Notes in Computer Science, 2005

We present a computational model of case-based visual problem solving. The Galatea model and the ... more We present a computational model of case-based visual problem solving. The Galatea model and the two experimental participants modeled in it show that 1) visual knowledge is sufficient for transfer of some problemsolving procedures, 2) visual knowledge facilitates transfer even when non-visual knowledge might be available, and 3) the successful transfer of strongly-ordered procedures in which new objects are created requires the reasoner to generate intermediate knowledge states and mappings between the intermediate knowledge states of the source and target cases. We describe Galatea, the two models created with it, and related work.

Knowledge-Based Systems, 2008

Computational models of analogical problem solving have traditionally described source and target... more Computational models of analogical problem solving have traditionally described source and target domains in terms of their causal structure. But psychological research shows that visual reasoning plays a part for many kinds of analogies. This paper describes a model that transfers a solution from a source analog to a new target problem using only visual knowledge represented symbolically. The knowledge representation is based on a language of primitive visual elements and transformations. We found that visual knowledge is sufficient for transfer, but that causal knowledge is needed to determine if the transferred solution is appropriate.

In our study of a biomedical engineering laboratory, we construe the lab as adynamic distributed ... more In our study of a biomedical engineering laboratory, we construe the lab as adynamic distributed cognitive system evolving in time. We have foundthat a full understanding requires indepth observation of the lab as it presently exists as well as research into the history of the lab and theexperimental devices used in it. To do this we use both ethnography andcognitive-historical analysis. The experimental devices in the distributed system have biographies of changes in response to problems over time. Learning requires coming to understand these aspects of devices. It occurs through mentorship and by discovering the locus of sources of knowledge distributed among the researchers within the interdiscpline of BME.

Representation issues in visual analogy

Imagination, Cognition and Personality, 2011

In this article we review work on mental imagery in adults and children. We argue that the fundam... more In this article we review work on mental imagery in adults and children. We argue that the fundamental issue of how we respond to a stimulus to create a descriptive, propositional scene description, and then render this description into an image is poorly understood. In addition to providing a new framework by which to address this issue, we highlight several other topics within the study of mental imagery that require further investigation. These include inhibiting irrelevant information and making inferences, knowing the difference between reality and imagination (or even beliefs and imagination), and point of view. We conclude by suggesting future directions of study to address these additional topics.

Case-based problem solving refers to reasoning about new problems by reusing past cases. Visual c... more Case-based problem solving refers to reasoning about new problems by reusing past cases. Visual case-based problem solving pertains to reuse of past cases that contain only visual knowledge. In this paper, we explore the problem of automated adaptation of diagrammatic cases, i.e., automatic transfer of diagrammatic knowledge from a source case to a target problem. We describe Galatea, a computer program that adapts diagrammatic cases. Galatea explicitly represents knowledge states in the source case in the form of propositions, and transfers visual transformations from the source case to the target problem. A companion paper in the same conference [15] addresses the task of retrieving diagrammatic cases.

Complex problem solving typically involves the generation of a procedure consisting of an ordered... more Complex problem solving typically involves the generation of a procedure consisting of an ordered sequence of steps. Analogical reasoning is one strategy for solving complex problems, and visual reasoning is another. Visual analogies pertain to analogies based only on visual knowledge. In this paper, we describe the use of Galatea, a computational model of visual analogies in problem solving, to model the problem solving of a human subject (L14). L14 was a given the task of solving a complex problem using analogy in a domain that contained both visual and non-visual knowledge, and was encouraged to use visual analogy. We describe how Galatea models L14's use of visual analogy in problem solving.

Lecture Notes in Computer Science, 2007

We show that visio-spatial representations and reasoning techniques can be used as a similarity m... more We show that visio-spatial representations and reasoning techniques can be used as a similarity metric for analogical protein structure prediction. Our system retrieves pairs of α-helices based on contact map similarity, then transfers and adapts the structure information to an unknown helix pair, showing that similar protein contact maps predict similar 3D protein structure. The success of this method provides support for the notion that changing representations can enable similarity metrics in analogy.

Proceedings of the 8th ACM conference on Creativity and cognition, 2011

A presentation of an Artificial Intelligence (AI) called Visuo that stores and guesses quantitati... more A presentation of an Artificial Intelligence (AI) called Visuo that stores and guesses quantitative visual-spatial magnitudes (e.g., sizes of objects). In this analysis, Visuo is used to store polar (angle and distance) relationships between objects in images. It uses a database of tagged images as its memory and approximates unexperienced magnitudes by analogy with semantically related concepts. This shows the transferring of information from high semantically related concepts yielding significantly higher accuracy in angle and distance estimations over using medium or low semantically similar items.

A cognitive model of the visual imagination will produce "incoherent" results when it adds elemen... more A cognitive model of the visual imagination will produce "incoherent" results when it adds elements to an imagined scene that come from different contexts (e.g., "computer" and "cheese" with "mouse"). We approach this problem with a model that infers coherence relations from co-occurrence probabilities of labels in images. We show that this algorithm's serial traversal of networks of co-occurrence relations for a particular query produces greater coherence than one leading model in the field of computational coherence: Thagard's connectionist model.

Proceedings of the International Conference on Evolutionary Computation Theory and Applications, 2014

We propose a new algorithm and formal description of generative cognition in terms of the multi-l... more We propose a new algorithm and formal description of generative cognition in terms of the multi-label bagof-words paradigm. The algorithm, Coherence Net, takes its inspiration from evolutionary strategies, genetic programming, and neural networks. We approach generative cognition in spatial reasoning as the decompression of images that were compressed into lossy feature sets, namely, conditional probabilities of labels. We show that the globally parallel and locally serial optimization technique described by Coherence Net is better at accurately generating contextually coherent subsections of the original compressed images than a competitive, purely serial model from the literature: Coherencer.

Lecture Notes in Computer Science, 2014

This paper proposes that decompression is an important and often overlooked component of cognitio... more This paper proposes that decompression is an important and often overlooked component of cognition in all domains where compressive stimuli reduction is a requirement. We support this claim by comparing two compression representations, co-occurrence probabilities and holographic vectors, and two decompression procedures, top-n and Coherencer, on a context generation task from the visual imagination literature. We tentatively conclude that better decompression procedures increase optimality across compression types.

The Knowledge Engineering Review, 2013

Visuo is an implemented Python program that models visual reasoning. It takes as input a descript... more Visuo is an implemented Python program that models visual reasoning. It takes as input a description of a scene in words (e.g. ‘small dog on a sunny street’) and produces estimates of the quantitative magnitudes of the qualitative input (e.g. the size of the dog and the brightness of the street). We claim that reasoners transfer quantitative knowledge to new concepts from distributions of familiar concepts in memory. We also claim that visuospatial magnitudes should be stored as distributions over fuzzy sets. We show that Visuo successfully predicts quantitative knowledge to new concepts.

Computational Intelligence, 2006

We show that visio-spatial representations and reasoning can be used as a similarity metric for c... more We show that visio-spatial representations and reasoning can be used as a similarity metric for case-based protein structure prediction. Our system retrieves pairs of α-helices based on contact map similarity, then transfers and adapts the structure information to an unknown helix pair. We show that similar protein contact maps predict similar 3D protein structure. The success of this method provides support for the notion that changing representations can enable similarity metrics in case-based reasoning.

Information Systems Frontiers, 2006

Determining the three-dimensional structure of a protein is an important step in understanding bi... more Determining the three-dimensional structure of a protein is an important step in understanding biological function. Despite advances in experimental methods (crystallography and NMR) and protein structure prediction techniques, the gap between the number of known protein sequences and determined structures continues to grow. Approaches to protein structure prediction vary from those that apply physical principles to those that consider known amino acid sequences and previously determined protein structures. In this paper we consider a two-step approach to structure prediction: (1) predict contacts between amino acids using sequence data; (2) predict protein structure using the predicted contact maps. Our focus is on the second step of this approach. In particular, we apply a case-based reasoning framework to determine the alignment of secondary structures based on previous experiences stored in a case base, along with detailed knowledge of the chemical and physical properties of proteins. Case-based reasoning is founded on the premise that similar problems have similar solutions. Our hypothesis is that we can use previously determined structures and their contact maps to predict the structure for novel proteins from their contact maps.

Cognitive science, Jan 10, 2017

An incoherent visualization is when aspects of different senses of a word (e.g., the biological &... more An incoherent visualization is when aspects of different senses of a word (e.g., the biological "mouse" vs. the computer "mouse") are present in the same visualization (e.g., a visualization of a biological mouse in the same image with a computer tower). We describe and implement a new model of creating contextual coherence in the visual imagination called Coherencer, based on the SOILIE model of imagination. We show that Coherencer is able to generate scene descriptions that are more coherent than SOILIE's original approach as well as a parallel connectionist algorithm that is considered competitive in the literature on general coherence. We also show that co-occurrence probabilities are a better association representation than holographic vectors and that better models of coherence improve the resulting output independent of the association type that is used. Theoretically, we show that Coherencer is consistent with other models of cognitive generation. In ...

The cognitive importance of testimony