12964519
Development and evaluation of emerging design patterns for ubiquitous computing
Design patterns are a format for capturing and sharing design knowledge. In this paper, we look at a new domain for design patterns, namely ubiquitous computing. The overall goal of this work is to aid practice by speeding up the diffusion of new interaction techniques and evaluation results from researchers, presenting the information in a form more usable to practicing designers. Towards this end, we have developed an initial and emerging pattern language for ubiquitous computing, consisting of 45 pre-patterns describing application genres, physical-virtual spaces, interaction and systems techniques for managing privacy, and techniques for fluid interactions. We evaluated the effectiveness of our pre-patterns with 16 pairs of designers in helping them design location-enhanced applications. We observed that our pre-patterns helped new and experienced designers unfamiliar with ubiquitous computing in generating and communicating ideas, and in avoiding design problems early in the design process.
8944311
Recent shortcuts: using recent interactions to support shared activities
We present an empirical study of teams that revealed the amount of extraneous individual work needed to enable collaboration: finding references to other people, finding files to attach to email, managing incoming email attachments, managing the variety of files used in shared activities, and tracking what work is owed to others. Much of this work involves finding recently accessed objects that are needed again in the user's current task focus. These observations led to the design of Recent Shortcuts, a tool to help support coordination by making recently used objects easily accessible. Recent Shortcuts enables quick access to people (including groups of people), received attachments, files, and file folders that the user interacted with recently for re-use in the user's current context. Recent Shortcuts makes it easy to use these objects across applications with no additional user input and minimal changes to the user's applications or work practice. Early user experiences with a working prototype led to an extension that integrates recently accessed objects across multiple devices.
6272838
A visual language for sketching large and complex interactive designs
Informal, sketch-based design tools closely match the work practices of user interface designers. Current tools, however, are limited in the size and complexity of interaction that can be specified. We have created an advanced sketch-based visual language that allows for easy prototyping of large, complex interactive designs. In its current embodiment in the denim web design tool, the visual language allows designers to sketch reusable components for recurring page elements, such as navigation bars, as well as conditionals to illustrate and test transitions that depend on a user's input. Designers can also specify sites that accept richer user input than simple clicking. Our informal evaluation shows that these features allow designers with little programming experience to quickly create prototypes of large, complex web sites while still working inside an informal, sketch-based environment
8007626
DENIM: finding a tighter fit between tools and practice for Web site design
Through a study of web site design practice, we observed that web site designers design sites at different levels of refinement—site map, storyboard, and individual page—and that designers sketch at all levels during the early stages of design. However, existing web design tools do not support these tasks very well. Informed by these observations, we created DENIM, a system that helps web site designers in the early stages of design. DENIM supports sketching input, allows design at different refinement levels, and unifies the levels through zooming. We performed an informal evaluation with seven professional designers and found that they reacted positively to the concept and were interested in using such a system in their work.
6501439
Visualizing and Discovering Non-Trivial Patterns in Large Time Series Databases
Data visualization techniques are very important for data analysis, since the human eye has been frequently advocated as the ultimate data-mining tool. However, there has been surprisingly little work on visualizing massive time series data sets. To this end, we developed VizTree, a time series pattern discovery and visualization system based on augmenting suffix trees. VizTree visually summarizes both the global and local structures of time series data at the same time. In addition, it provides novel interactive solutions to many pattern discovery problems, including the discovery of frequently occurring patterns (motif discovery), surprising patterns (anomaly detection), and query by content. VizTree works by transforming the time series into a symbolic representation, and encoding the data in a modified suffix tree in which the frequency and other properties of patterns are mapped onto colors and other visual properties. We demonstrate the utility of our system by comparing it with state-of-the-art batch algorithms on several real and synthetic data sets. Based on the tree structure, we further device a coefficient which measures the dissimilarity between any two time series. This coefficient is shown to be competitive with the well-known Euclidean distance.
7688101
(Not) Finding Rules in Time Series: A Surprising Result with Implications for Previous and Future Research
Time series data is perhaps the most frequently encountered type of data examined by the data mining community. Clustering is perhaps the most frequently used data mining algorithm, being useful in it’s own right as an exploratory technique, and also as a subroutine in more complex data mining algorithms such as rule discovery, indexing, summarization, anomaly detection, and classification. Given these two facts, it is hardly surprising that time series clustering has attracted much attention. The data to be clustered can be in one of two formats: many individual time series, or a single time series, from which individual time series are extracted with a sliding window. Given the recent explosion of interest in streaming data and online algorithms, the latter case has received much attention. In this work we make a surprising claim. Clustering of streaming time series is completely meaningless. More concretely, clusters extracted from streaming time series are forced to obey a certain constraint that is pathologically unlikely to be satisfied by any dataset, and because of this, the clusters extracted by any clustering algorithm are essentially random. While this constraint can be intuitively demonstrated with a simple illustration and is simple to prove, it has never appeared in the literature. An important implication of our findings is that widely used, time series rule discovery algorithms are producing random results!
2209704
Capturing natural hand articulation
Vision-based motion capturing of hand articulation is a challenging task, since the hand presents a motion of high degrees of freedom. Model-based approaches could be taken to approach this problem by searching in a high dimensional hand state space, and matching projections of a hand model and image observations. However, it is highly inefficient due to the curse of dimensionality. Fortunately, natural hand articulation is highly constrained, which largely reduces the dimensionality of hand state space. This paper presents a model-based method to capture hand articulation by learning hand natural constraints. Our study shows that natural hand articulation lies in a lower dimensional configurations space characterized by a union of liner manifolds spanned by a set of basis configurations. By integrating hand motion constraints, an efficient articulated motion-capturing algorithm is proposed based on sequential Monte Carlo techniques. Our experiments show that this algorithm is robust and accurate for tracking natural hand movements. This algorithm is easy to extend to other articulated motion capturing tasks.
42438893
Some Decomposition Invariants of Crowns
339805
Answering Clinical Questions with Knowledge-Based and Statistical Techniques
The combination of recent developments in question-answering research and the availability of unparalleled resources developed specifically for automatic semantic processing of text in the medical domain provides a unique opportunity to explore complex question answering in the domain of clinical medicine. This article presents a system designed to satisfy the information needs of physicians practicing evidence-based medicine. We have developed a series of knowledge extractors, which employ a combination of knowledge-based and statistical techniques, for automatically identifying clinically relevant aspects of MEDLINE abstracts. These extracted elements serve as the input to an algorithm that scores the relevance of citations with respect to structured representations of information needs, in accordance with the principles of evidence-based medicine. Starting with an initial list of citations retrieved by PubMed, our system can bring relevant abstracts into higher ranking positions, and from these abstracts generate responses that directly answer physicians' questions. We describe three separate evaluations: one focused on the accuracy of the knowledge extractors, one conceptualized as a document reranking task, and finally, an evaluation of answers by two physicians. Experiments on a collection of real-world clinical questions show that our approach significantly outperforms the already competitive PubMed baseline.