Although neural rendering has made significant ad-vances in creating lifelike, animatable full-body and head avatars, incorporating detailed expressions into full-body avatars remains largely unexplored. We present DEGAS, the first 3D Gaussian Splatting (3DGS)-based modeling method for full-body avatars with rich facial expressions. Trained on multiview videos of a given subject, our method learns a conditional variational autoencoder that takes both the body motion and facial expression as driving signals to generate Gaussian maps in the UV layout. To drive the facial expressions, instead of the commonly used 3D Mor-phable Models (3DMMs) in 3D head avatars, we propose to adopt the expression latent space trained solely on 2D portrait images, bridging the gap between 2D talking faces and 3D avatars. Leveraging the rendering capability of 3DGS and the rich expressiveness of the expression latent space, the learned avatars can be reenacted to reproduce photo-realistic rendering images with subtle and accurate facial expressions. Experiments on an existing dataset and our newly proposed dataset offull-body talking avatars demonstrate the efficacy of our method. We also propose an audio-driven extension of our method with the help of 2D talking faces, opening new possibilities for interactive AI agents. Project page: https://initialneil.github.io/DEGAS.

Voice agents can construct meaningful conversations with older adults to offer various benefits, such as providing emotional companionship and assisting with memory recall. However, such conversations often follow the simple turn-taking pattern and lack interruption and backchannel of natural human conversation. Previous research has shown that this rigid turn-taking pattern lacks interactivity and initiative, limiting the flexible communication between older adults and voice agents. To address these issues and create a more natural conversational voice agent, we first conducted a formative study to identify common usage of interruption in the natural conversations of older adults. We then designed an LLM-powered Barge-in agent that supports interruption and backchannel. Our within-subject exploratory study showed that participants felt that conversations with Barge-in agents were more natural, engaging, and fluent than with the No barge-in agent. We further present design implications for creating more natural and human-like voice agents for older adults.

Chinese calligraphy offers fruitful visual structure not found in abstract/ figurative paintings or photographic images. It makes it well-suited for studying how personality shapes aesthetic preference, yet few works have explored this link. This paper introduces the first computational framework that models the link between viewer personality and Kai Shu calligraphic preference. It collects a dataset of Kai Shu calligraphy images, user preference scores, and Big Five personality traits. It extracts 160 structural feature descriptors from eight categories, such as stroke curvature, layout, and whitespace. Regression and attribution methods reveal five patterns, such as visual structure predicts perceived style, and that traits like Openness and Neuroticism influence preference patterns. High-Openness users prefer balanced, clean layouts. Low-Neuroticism users favor lighter, irregular forms. Some personality-feature pairs follow inverted-U trends, where moderate structural complexity leads to higher preference. These results connect cognitive traits with visual structure and support interpretable, personality aware modeling of aesthetic response. Our findings support personalized style discovery and open up new directions for interest-driven aesthetic education and digital preservation. Code available at https://github.com/tianchengliu18/kai2trait.

This paper delves into Vilém Flusser's critique of media as mediators that distort the human perception of reality and diminish freedom, particularly within the Mixed Reality context, i.e., Misperceived Reality. It introduces a critical inquiry through Surreal Me, which engages participants to experience a two-phase virtual embodying process and reveal the “Misperceived Reality.” The process examines the obfuscating nature of media; as the Sense of Embodiment inevitably breaks down, users can discover the constructed nature of media-projected reality. When users reflect on reality's authentic and mediated experiences in MR, this work fosters a critical discourse on Flusserian media freedom addressing emerging immersive technologies.

As we navigate the complexities of capitalism, the booming economy is often highlighted. Yet, the hidden incidents surrounding lives often remain obscured. This article investigates these issues in an alternative way, mainly focusing on the disappearance of data related to pet food safety. Our series of artworks, Hidden Incidents of Pet Food, highlights three crucial issues: Hidden Incidents of Taurine Deficiency, Hidden Incidents of Animal Euthanasia, and Hidden Incidents of Melamine Adulteration. These works take inspiration from the erasure of non-human lives and raise questions about the information hegemony of pet capitalism. We refer to these missing data as “Ghosts of Life,” representing the lives impacted by the “Anthropocene” and “Capitalocene,” yet rendered invisible due to incomplete documentation and deliberate neglect. By employing AI to visualize these “Ghosts of Life,” we hope to use art to confront the audience with new experiences that crystallize new perceptions, transforming the elusive into a tangible representation of “life-as-it-might-be.”

Ink Blossom is an interactive installation combining generative AI with the intangible cultural heritage of traditional ethnic embroidery from China. Integrating a tangible wooden embroidery frame with a dynamic digital canvas, the project invites participants to co-create with AI in real-time, transforming hand-drawn sketches into authentic embroidery designs. Utilizing custom-trained LoRA models, the system preserves the distinct visual languages of Miao and Qiang embroidery, allowing users to creatively engage with traditional forms. Participant feedback reveals that generative AI deepens their personal connections with these ancient crafts, proposing an innovative collaborative future for the preservation of intangible cultural heritage.

The progression of AI technology has spurred a growing number of artists to engage in AI Art production. This trend has sparked a multifaceted societal discourse. Given the tight integration of AI Art with society, it is essential to explore the potential art innovations and impacts AI Art might introduce. This article explores the relationship between AI visual art and conventional art, tracing the development of AI visual art and its societal reception. Through historical case studies, the article forecasts the future influence of AI visual art in the following aspects: Regarding artistic creation, AI visual art challenges established criteria for artistic evaluation. It presents artists with enhanced learning and creative capacities, paving the way for a new artist archetype characterized by human-computer symbiosis. From a societal standpoint, AI visual art is to advance the democratization of art, developing alongside traditional art forms.

Landscape renderings are realistic images of landscape sites, allowing stakeholders to perceive better and evaluate design ideas. While recent advances in Generative Artificial Intelligence (GAI (generative artificial intelligence)) enable automated generation of landscape renderings, the End to End (endtoend) methods are not compatible with common design processes, leading to insufficient alignment with design idealizations and limited cohesion of iterative landscape design. Informed by a formative study for comprehending design requirements, we present PlantoGraphy, an iterative design system that allows for interactive configuration of generative artificial intelligence models to accommodate human-centered design practice. A two-stage pipeline is incorporated: first, the concretization module transforms conceptual ideas into concrete scene layouts with a domain-oriented large language model; and second, the illustration module converts scene layouts into realistic landscape renderings with a layout-guided diffusion model Fine-tune (finetune)ed through Low-Rank Adaptation (LoRA) (lora). PlantoGraphy has undergone a series of performance evaluations and user studies, demonstrating its effectiveness in landscape rendering generation and the high recognition of its interactive functionality.

Image search is an essential and user-friendly method to explore vast galleries of digital images. However, existing image search methods heavily rely on proximity measurements like tag matching or image similarity, requiring precise user inputs for satisfactory results. To meet the growing demand for a contemporary image search engine that enables accurate comprehension of users' search intentions, we introduce an innovative user intent expansion framework. Our framework leverages visual-language models to parse and compose multi-modal user inputs to provide more accurate and satisfying results. It comprises two-stage processes: 1) a parsing stage that incorporates a language parsing module with large language models to enhance the comprehension of textual inputs, along with a visual parsing module that integrates an interactive segmentation module to swiftly identify detailed visual elements within images; and 2) a logic composition stage that combines multiple user search intents into a unified logic expression for more sophisticated operations in complex searching scenarios. Moreover, the intent expansion framework enables users to perform flexible contextualized interactions with the search results to further specify or adjust their detailed search intents iteratively. We implemented the framework into an image search system for NFT (non-fungible token) search and conducted a user study to evaluate its usability and novel properties. The results indicate that the proposed framework significantly improves users' image search experience. Particularly the parsing and contextualized interactions prove useful in allowing users to express their search intents more accurately and engage in a more enjoyable iterative search experience.

Inspired by Kandinsky's original postulation of specific color-shape association preferences in human beings (i.e., blue circle, red square and yellow triangle), extensive literature from diverse cultural backgrounds has re-examined this issue and achieved a consensus on the existence of such preferences, yet there is disagreement on the specific association patterns. In this study, we investigated the association between color and basic geometric shapes among Chinese college students. Correspondence analysis revealed that both circles and triangles are highly associated with yellow. Circles are also associated with red, while squares exhibit a strong association with blue. Additionally, we confirmed Kandinsky's theory that the angular characteristics of shapes affect the choice of warm and cold colors assigned to them. Interestingly, neither personality traits nor affect states affect the color-shape association preference despite their influences on either color or shape preference alone. Our findings suggest that people may share universal aesthetic preferences to a certain extent. These results have implications for future product design and generative art, where color and shape are crucial elements for visual communication.

Our participatory art project, Bitter Data, allows the audience to taste the bitterness of 100,000 trouble data via 11 teapots corresponding to 11 years of data. The bitterness and color of each pot reflect the quantity of trouble data in the corresponding year. By mapping bitterness and color to data quantity, we brew tea with varying tastes and tones. Tasting the tea with multi-sensory feelings allows participants to understand the negative emotions behind the data and how young Chinese have faced troubles in the past 11 years.

Dockless Bike-sharing systems can be treated as a typical paradigm of the sharing economy. Due to the indiscriminate use and placement of huge bikes, their scheduling rules are complicated, which poses a challenge to the macro-control of the companies To enhance the information dimension of bike Origin-Destination (OD) data, an algorithm based on grid features is proposed to reconstruct travel trajectories of OD data. This paper starts by analyzing and designing metaphors to describe the scheduling rules and trip characteristics of Dockless Bike-Sharing. The evaluation shows that the proposed metaphors are user-friendly to novice users. We argue that metaphors provide an effective way for users to understand abstract ideas in a visual design with a large dataset.

Projection mapping is a form of urban public art that uses light and video to transform buildings and structures into dynamic canvases. However, producing high-quality projection mapping content with compelling storytelling requires extensive time and resources, as it involves integrating local culture, urban spatial understanding, and animation production. To address this challenge, this paper proposes a method that combines artistic co-creation with AI, audio-visualization, and data-visualization techniques. The authors present a case study: “Urban Symphony,” an immersive public art installation that showcases our method and leverages AI and data-driven storytelling. This method fosters interdisciplinary research collaboration and explores the potential of projection mapping as a bridge between art, technology, and society. The paper describe the motivation, design, and production of the artwork, the outcomes of the performance, and the challenges and limitations of our method. The authors also suggest future directions for further improvement and exploration in this domain.

We present a machine learning-based interactive video installation powered by CLIP and diffusion models and inspired by the concept of naturality in traditional Chinese calligraphy. The artwork explores contemporary interpretations of this traditional concept through practical methods in Artificial Intelligence Generated Content (AIGC). Technically, the algorithms are based on state-of-the-art perceptual and generative models, incorporating multi-dimensional controls over text-to-image and image-to-image translation; conceptually, this real-time art installation extends the discussion brought by Xu Bing’s pieces Book from the Sky and Square Word Calligraphy. The project explores the possibility of AIGC in bridging human creativity and natural randomness, as well as a shifting creative paradigm enhanced by AI knowledge, perception, and association.

While financial forums are increasingly significant in financial analysis, current visualization tools do not properly exploit their text data. To address this, we present BiverWordle, a novel tool that reveals the relationship between market sentiment and firm trends. BiverWordle integrates candlestick chart, ThemeRiver, and Wordle with text classification and sentiment analysis techniques to decode market dynamics from textual sources, such as shareholder opinions and firm announcements. With the application of a Voting model to the manually labeled data, we achieved an accuracy of approximately 64%. BiverWordle facilitates the extraction of shareholder insights from sparse comments and provides a visual method for historical stock trend analysis, which we validated with three distinct stock trends. Resources are accessible at https://github.com/Brian-Lei-XIA/BiverWordle.

Color is one of the fundamental elements of paintings. This paper proposes a set of measurements for color usage in a painting, including basic color elements, color harmony templates, and spatial distribution, characterizing both global and local features of color. Applying the measurements to over 3000 abstract paintings, oil paintings and Chinese ink paintings, we are able to observe the roles of color in the three genres of paintings. We report our findings in details on the effectiveness of these measurements, which may serve as tools for classification of paintings. The work is the first of this kind and points to further investigation of color usage in other forms of art and design.

Abstract Painters arrange white space in contrast with chromatic space composed of strokes. This research measures white space, color complexity and stroke density in Chinese ink paintings and examines how these attributes influence the paintings’ perceived complexity. Empirical evidence from 21 well-known modern Chinese artists’ ink paintings shows that white space decreases paintings’ complexity, while chromatic space and stroke density increase complexity. We also reveal that a large rate of white space guides the viewers’ attention on chromatic space and enhances the impacts of color complexity and stroke density on perceived complexity. An eye-tracker measures viewers’ elaboration duration on each painting, which provides consistent evidence to validate our conclusion based on subjective reported visual complexity. Our research provides insights into the rhetorical role of white space in sensory information processing.

We propose an approach to underpin interactive visual exploration of large data volumes by training Learned Visualization Index (LVI). Knowing in advance the data, the aggregation functions that are used for visualization, the visual encoding, and available interactive operations for data selection, LVI allows to avoid time-consuming data retrieval and processing of raw data in response to user’s interactions. Instead, LVI directly predicts aggregates of interest for the user’s data selection. We demonstrate the efficiency of the proposed approach in application to two use cases of spatio-temporal data at different scales.

Abstract As a two-dimensional formal method, graph grammar is widely used in defining various visual programming languages. This paper presents a new graph grammar formalism called coordinate graph grammar (CGG). CGG is extended from the edge-based graph grammar (EGG) by introducing the spatial mechanism into the theoretical framework, which consists of continuous coordinate graph grammar (cCGG) and discrete coordinate graph grammar (dCGG). By combining quantitative and qualitative spatial semantics in one framework, CGG provides strong expressiveness and flexibility for specifying various spatial graphs. This paper focuses on several important issues on the new formalism. First, the theoretical framework of CGG is given. Second, two matching algorithms for cCGG and dCGG are proposed, which use the spatial relationships between nodes to narrow down the search space during parsing. Finally, an application of CGG is demonstrated, which generates parsable flowcharts in a uniform layout.

This short article presents our preliminary work on color usage and distribution in Mani heaps, a form of Tibetan folk art and a physical means of visual communication in Tibetan Buddhism and culture. We have collected visual images of 70 Mani heaps in Ding Qing County, one of most heavily Mani heaps concentrated regions in Tibet. By quantifying the color distribution and identifying the dominant colors, we could further our studies to investigate different branches of Tibetan Buddhism and their color preferences.

Word cloud is a text visualization technique which is widely applied in helping improve semantic understanding about target materials. One of the most important features is the font size, which represents words frequencies of a document. As the result, in this paper, we explore how to set font sizes of words, and its influence on semantic understanding through people's performance with qualitative and controlled experiments. Adopting an machine learning algorithm LDA (Latent Dirichlet Allocation) topic model, we quantify semantics of the document and judge participants' accuracy performance. The experimental results show the influence of different font size on semantic understanding performance and provide insights for ways in promoting semantic understanding of word cloud.

In recent years, there has been a great interest in integrating crowdsourced API documents that are often dispersed across multiple places. Because of the complexity of natural language, however, automatically synthesized documents often fall short on quality and completeness compared to those authored by human experts. We develop a complementary “human-assensors” approach to document integration that generates API FAQs based on users' help-seeking behavior and history. We investigated the benefits and limitations of this approach in the context of programming education. This paper describes a prototype system called COFAQ and a controlled experiment with 18 novice programmers. The study confirms that the generated FAQs effectively fosters knowledge transfer between the programmers and significantly reduce the need for repeated search. It also discovers several difficulties novice programmers encountered when seeking API help as well as the strategies they used to seek and utilize API knowledge.

Spatial time series is a common type of data dealt with in many domains, such as economic statistics and environmental science. There have been many studies focusing on finding and analyzing various kinds of events in time series; the term 'event' refers to significant changes or occurrences of particular patterns formed by consecutive attribute values. We focus on a further step in event analysis: discover temporal relationship patterns between event locations, i.e., repeated cases when there is a specific temporal relationship (same time, before, or after) between events occurring at two locations. This can provide important clues for understanding the formation and spreading mechanisms of events and interdependencies among spatial locations. We propose a visual exploration framework COPE (Co-Occurrence Pattern Exploration), which allows users to extract events of interest from data and detect various co-occurrence patterns among them. Case studies and expert reviews were conducted to verify the effectiveness and scalability of COPE using two real-world datasets.

Shape grammars are powerful in specifying and generating an infinite number of designs by repeatedly applying predefined transformation rules to initial shapes. However, the intractable nature of shape grammars limits its wide use in many applications. Moreover, shape grammars lack a parsing method to automatically validate generated designs. In this paper, we present an approach capable of generating tractable designs and validating generated designs. Our approach transforms polygonal shapes and shape rules in shape grammars to graphs and then uses the spatial graph grammar (SGG) formalism to generate and parse designs. We have evaluated our approach by transforming shapes and shape rules originally specified in a shape grammar for Turkish houses to our graph representation and validating designs with the transformed graph rules on VEGGIE, a graph grammar specification and induction tool. Other shape grammars can be potentially integrated with our approach to extend their capability in generating and validating designs.

Classic geospatial network visualization tends to limit itself to 2D representation by organizing edges and nodes on a 2D map or the external surface of a traditional 3D globe model. Visual clutters and occlusions due to edge crossings and node-edge overlaps make efficient and effective exploration of geospatial networks a challenge. This paper proposes an interactive visualization approach for the intuitive exploration of geospatial networks inside a spherical virtual reality environment. To reduce visual clutter and reveal network patterns, we also propose a parameterized 5-step 3D edge-bundling algorithm and a set of techniques to avoid collision of network edges with the viewpoint. Our spherical interaction and 3D edge-bundling approach have been implemented in an Oculus Rift VR system. We demonstrate the usefulness of our approach with two case studies on real-world network data and usability experiments.

Summary As a two‐dimensional formal tool, graph grammars are capable of handling the layout problems of visual programming languages. Based on an edge‐based graph grammar (EGG), this paper proposes a novel layout approach that uses the unique features of EGG and overcomes the weakness of existing layout approaches. In order to make the approach rigorous yet concise, the graph grammar mechanisms with layout constraints and quantitative analysis techniques are combined together as an integrity. First, the basic notions of EGG are briefly introduced; second, the layout approach is presented that consists of two phases, ie, bottom‐up parsing and top‐down derivation. Finally, a case study is given by taking the standard flowchart as an example to demonstrate the working process of the proposed approach.

Moving sand pictures are interesting devices that can be used to generate an infinite number of unique scenes when repeatedly being flipped over. However, little work has been done on attempting to simulate the process of picture formulation. In this paper, we present an approach capable of generating images in the style of moving sand pictures. Our system defines moving sand pictures in a few steps, such as initialization, segmentation and physical simulation, so that a variety of moving sand pictures including mountain ridges, desert, clouds and even regular patterns can be generated by either automatic or semi-automatic via interaction during initialization and segmentation. Potential applications of our approach range from advertisements, posters, post cards, packaging, to digital arts.

Complexity is a key factor influencing aesthetic judgment of artworks. Using a well-known artist Wu Guanzhong's paintings as examples, we provide quantified methods to gauge three visual attributes which influence the complexity of paintings, i.e. color richness, stroke thickness and white space. By conducting regression analysis, our research validates the influences of given visual attributes on perceived complexity, and distinguishes the complexity measurements for abstract paintings and representational paintings. Specifically, all three factors influence the complexity of abstract paintings; In contrast, mere white space influences that of representational paintings.

Inspired by the recent advances of deep learning and computer graphics techniques in generating sophisticated artistic styles and architectural designs, we envision a general design generation methodology that uses various specification and generative methods and tools in an integral design cycle.

The mandala thangka, as a religious art in Tibetan Buddhism, is an invaluable cultural and artistic heritage. However, drawing a mandala is both time and effort consuming and requires mastery skills due to its intricate details. Retaining and digitizing this heritage is an unresolved research challenge to date. In this paper, we propose a computer-aided generation approach of mandala thangka patterns to address this issue. Specifically, we construct parameterized models of three stylistic patterns used in the interior mandalas of Nyingma school in Tibetan Buddhism according to their geometric features, namely the star, crescent and lotus flower patterns. Varieties of interior mandalas are successfully generated using these proposed patterns based on the hierarchical structures observed from hand drawn mandalas. The experimental results show that our approach can efficiently generate beautifully-layered colorful interior mandalas, which significantly reduces the time and efforts in manual production and, more importantly, contributes to the digitization of this great heritage.

High-quality decision making requires accurate estimation of relative values. The perceptual bias when estimating relative values displayed by a visual sign may weaken the accuracy and cause misjudgment. This research explores the heuristic estimation of relative values using visual cues, namely linear, areal, and volumetric information. We conduct experiments to empirically test the influences of dimensional information on perceptual biases. First, we investigate the conspicuity of areal information. Our experiments indicate that the responses of participants instructed to estimate rates defined by either linear or volumetric information are biased by the corresponding rates determined by areal information. Second, visual cues implying three-dimensional information (e.g., depth) can lead to overestimation. Third, we probe the influence of vividness as the boundary condition on relative value estimation. Empirical evidence on perceptual bias sheds light on the pragmatics of visual signs, helps suggest guidelines for visual persuasions, and improves decision-making quality.

Computational aesthetics has become an active research field in recent years, but there have been few attempts in computational aesthetic evaluation of logos. In this article, we restrict our study on black-and-white logos, which are professionally designed for name-brand companies with similar properties, and apply perceptual models of standard design principles in computational aesthetic evaluation of logos. We define a group of metrics to evaluate some aspects in design principles such as balance, contrast, and harmony of logos. We also collect human ratings of balance, contrast, harmony, and aesthetics of 60 logos from 60 volunteers. Statistical linear regression models are trained on this database using a supervised machine-learning method. Experimental results show that our model-evaluated balance, contrast, and harmony have highly significant correlation of over 0.87 with human evaluations on the same dimensions. Finally, we regress human-evaluated aesthetics scores on model-evaluated balance, contrast, and harmony. The resulted regression model of aesthetics can predict human judgments on perceived aesthetics with a high correlation of 0.85. Our work provides a machine-learning-based reference framework for quantitative aesthetic evaluation of graphic design patterns and also the research of exploring the relationship between aesthetic perceptions of human and computational evaluation of design principles extracted from graphic designs.

In this paper, we present a programmed experiment and the results on automatic generation of Joan Miro style of surrealism. Combining the artist's aesthetic theory with the authors' own understanding, the paper analyzes the characteristics of Miro's work and proposes a process modeling approach that consists of four steps: structured drawing, adaptive coloring, space filling and noise injection. The generation process is described in details and sample generated images styled on Miro's paintings are also demonstrated and discussed. By extracting and coding pictorial elements in paintings of Miro, different styled images could be generated under different sets of parameters.

Feature integration theory, the classic visual attention model, provides a theoretical foundation for optimizing visual communication. This paper proposes a set of glyphs named RoseShape that aim at improving users' visual information search in visualization. RoseShapes integrate rich and easy-to-catch attributes for mapping data variables, enabling automatic and unconscious information processing in the pre-attentive stage. A case study illustrating the levels of education funding in the United States in 2008 validates the feasibility of this approach.

Metaphor is the underlying mechanism of information communication. Although metaphors are ubiquitous in information visualization designs, different connotations influence users' information processing dissimilarly. However, visual metaphors imply interfering attributes caused by the source concepts, and lead to backfire effects on users' inferences. Understanding the pros and cons of metaphoric transfer effect in information visualization would help optimizing visualization designs, and improve efficiency and accuracy of information processing. This study empirically examines how metaphors influence different activities of information processing, including comprehension, inference and judgment in information visualization. Metaphors illustrating strongly implying semantic meanings in source domain can improve both the user's conceptual fluency in information comprehension, and the rate of correctness in information searching.

Geoscience observation data refers to the datasets consisting of time series of multiple parameters generated from the sensors at fixed locations. Although a few works have attempted to visualize features of these data, none of them views these data as a specific type and attempts to show the overview in all the space, time and attribute aspects. It is important for domain experts to select interested subsets from huge amounts of observation data according to the high level patterns shown in the overview. We present a novel approach to visualizing geoscience observation data in a compact radial view. Our solution consists of three visual elements. A map showing the spatial aspect is in the center of the visualization, while temporal and attribute aspects are seamlessly combined with the spatial information. Our approach is equipped with interactive mechanisms for highlighting the selected features, adjusting the display range, as well as interactively generating a fisheye view. We demonstrate the effectiveness and usability of our approach with a usability experiment. Eye tracking records and user feedbacks obtained in the experiment prove the effectiveness of our approach.

Trajectory data visualizations are usually 3-Dimensional. Due to the potential cluttering and occlusion problems, slow speed and poor interactivity as well as the influence of perspective to the effects of display, 3D visualization techniques have been controversial in the field of information visualization. To address these problems, this paper proposes a novel 3D trajectory visualization method employing the immersive virtual reality technology. Compared with the traditional 3D trajectory visualization methods, our method breaks the limitations of the traditional 2D display of 3D visualization, and engages users in a virtual world with optional perspective to observe 3D visualization without over-plotting. Through practical experiments, our work can reduce the over-plotting of 3D visualization results, and provide smooth transition between visualization views and interactions.

To foster innovation and competition, an increasing number of software teams are becoming distributed. Such a distribution makes continuous collaboration and continuous awareness support a necessity and also a great challenge. Traditional desktop-based approaches are insufficient for the requirements of continuous awareness. In software development practice, an awareness tool on mobile devices is also desirable for team members to obtain the awareness information continuously. This paper addresses how to effectively present collaborative development activities using aesthetic visualization on mobile screens. Our approach supports multiple views suitable for software developers as well as team leaders. A baseline usability experiment and an eye-tracking experiment have evaluated the effectiveness and usability of the visualization method.

Interactive visual analysis can support displaying complex multidimensional data. We developed a novel system, which can incorporate domain knowledge and visual guidelines, to present complex logistical data. This paper presents the details of the development process of that system. Diagrammatic visualization approach was adopted for presenting multidimensional port data, in relation to events. The paper discusses case studies with land-side data and wharf-side data obtained from Port Botany in Sydney, Australia. We conducted a usability study with 20 students that compared performance with our visualization against a traditional bar chart. Task completion was significantly shorter with the diagrammatic visualization, and the visualization was rated higher in overall user preference.

This paper presents an approach of automatically generating abstract paintings of Malevich Style. Through parameterized program and flexible production processes, we successfully generated images mimicking the styles of the abstract artist Kazimir Malevich, having classified different styles of his paintings. The generated results using the basic components in Malevich's style based on his art theory are shown and the reason why some colors are chosen are also discussed.

Hierarchical structures are a common organization of data in the real world. In this paper, we propose a new type of tree structures called diamond tree to present the organization and relationship of hierarchical data. We design a spatial layout using the geometric theory, starting with a simple design and continually improving until the design that makes the best use of the screen estate. Finally, we use the diamond tree method to analyze the structure of Tianjin University website (www.tju.edu.cn).

Awareness, the understanding of others' activities, has been widely accepted as a remedy to many collaboration difficulties in distributed organizations. Continuous Awareness (CA), a continuous support for integrated awareness information across space and time, aims to address the need of continuous awareness information in globally distributed software teams. This paper presents an comprehensive evaluation of a CA system, Team Radar, across multiple platforms. The result shows that Team Radar enables developers to receive continuous awareness information effectively and efficiently without the limitations of space and time. Our work provides insights into further exploration of awareness for distributed software development and the design and evaluation of similar tools.

To foster innovation and competition, an increasing number of software teams are becoming distributed. Such distribution makes continuous collaboration and continuous awareness support a necessity and also a great challenge. Traditional desktop-based approaches are insufficient for the requirements of continuous awareness. In practical process of software development, an awareness tool on mobile devices is also desirable for team members to obtain the awareness information continuously. This paper addresses how to effectively present collaborative development activities using aesthetic visualization on mobile screens. Our approach supports multiple views suitable for software developers as well as team leaders. A small scale usability experiment has been conducted and reported.

This paper presents ERELT (Enhanced Radial Edgeless Tree), a tree visualization approach on modern mobile devices. ERELT is designed to offer a clear visualization of any tree structure with intuitive interaction. We are interested in both the observation and navigation of such structures. Such visualization can assist users in interacting with a hierarchical structure such as a media collection, file system, etc.

There would be value to several domains in discovering and visualizing sentiments in online posts. This paper presents SentiView, an interactive visualization system that aims to analyze public sentiments for popular topics on the Internet. SentiView combines uncertainty modeling and model-driven adjustment. By searching and correlating frequent words in text data, it mines and models the changes of the sentiment on public topics. In addition, using a time-varying helix together with an attribute astrolabe to represent sentiments, it can visualize the changes of multiple attributes and relationships among demographics of interest and the sentiments of participants on popular topics. The relationships of interest among different participants are presented in a relationship map. Using a new evolution model that is based on cellular automata, it is able to compare the time-varying features for sentiment-driven forums on both simulated and real data. Adaptable for different social networking platforms, such as Twitter, blog and forum, the methods demonstrate the effectiveness of SentiView in analyzing and visualizing public sentiments on the Web.

While cooking is a daily activity in our life, many family cooks feel bothered by deciding the menu each and every time because of being required to cook both tasty as well as well-balanced meals for their families. We have been developing a meal recording system, MEAL (Menu Evaluation and Attribute Log), which supports those cooks by easily managing and retrieving meal records on mobile platforms. MEAL displays automatically generated icons as composed representations of information based on food data, so users can easily identify each data item on a small screen. The interface has two main views, a retrieval view and an analyzing view for food records in the user's own food database. The retrieval view displays retrieval results with photos of food and symbolic icons. The analysis view supports visualizing all sets of food data represented by icons. It allows users to easily understand their cooking habits for their family. Since the information gets optimized, the symbolic icons lead to higher visibility on small screens.

This paper presents a new interactive scatter plot visualization for multi-dimensional data analysis. We apply RST to reduce the visual complexity through dimensionality reduction. We use an innovative point-to-region mouse click concept to enable direct interactions with scatter points that are theoretically impossible. To show the decision trend we use a virtual Z dimension to display a set of linear flows showing approximation of the decision trend. We have conducted a case study to demonstrate the effectiveness and usefulness of our new technique for identifying the impact sources of wine quality through the visual analytics of a wine dataset consisting of 12 attributes with 4898 samples.

With the proliferation of globally distributed projects, there has been an urgent need for continuous coordination and continuous awareness support. Traditional desktop-based approaches are insufficient for the requirements of continuous awareness. Team Radar Mobile takes a visual mobile approach to continuous awareness by visualizing awareness information on mobile platforms. The concept of continuous awareness and its implementation on multiple platforms are discussed. An experiment has evaluated the visual mobile approach to continuous awareness, and validated the design principles of mobile visualization.

With the advent of the Internet, it is desirable to interpret and extract useful information from the Web. One major challenge in Web interface interpretation is to discover the semantic structure underlying a Web interface. Many heuristic approaches have been developed to discover and group semantically related interface objects. However, those approaches cannot solve the problem of nonuniformity satisfactorily and are not able to tag the semantic role of each object. Distinct from existing approaches, this paper develops a robust and formal approach to recovering interface semantics using graph grammars. Because of the distinct capability of spatial specifications in the abstract syntax, the spatial graph grammar (SGG) is selected to perform the semantic grouping and interpretation of segmented screen objects. Instead of analyzing HTML source codes, we apply an efficient image-processing technology to recognize atomic interface objects from the screenshot of an interface and produce a spatial graph, which records significant spatial relations among recognized objects. A spatial graph is more concise than its corresponding document object model structure and, thus, facilitates interface analysis and interpretation. Based on the spatial graph, the SGG parser recovers the hierarchical relations among interface objects.

Multidimensional visualization, which creates visual presentations for datasets of multiple variables, helps users to gain an insight of represented information from different perspectives. This paper presents a multidimensional information visualization technique called InfoShape that represents multidimensional information as a 3D sphere whose appearance denotes how much the provided information satisfies a pre-defined set of criteria. By comparing spheres representing different multiple information sets, overall content similarities and differences can be quickly captured at a glance by viewers. The InfoShape concept has been evaluated with a case analysis and an empirical study.

Information visualization techniques have been applied to many research fields in recent years. This paper introduces the concept of visual signature for financial time series. Unlike other information visualization, visual signatures are designed to more proactively reveal visual patterns of market data. Previous research on visualizing financial time series is discussed and compared. This paper shows how the concept of visual signature helps professional analysts obtain instant and useful visual messages from it.

Analyzing financial time series data is challenging due to the volatility of stock market. We advocate using visual approaches to market data analysis. We present a visual analytics system that enables individual investors to analyze stock market by giving them a holistic view on the market data. This paper discusses the drawbacks of current visual charts and describes the core visual techniques used in our system.

Discovering program behaviors and functionalities can ease program comprehension and verification. Existing program analysis approaches have used text mining algorithms to infer behavior patterns or formal models from program execution. When one tries to identify the hierarchical composition of a program behavior at different abstraction levels, textual descriptions are not informative and expressive enough. To address this, we present a semi-automatic graph grammar approach to retrieving the hierarchical structure of the program behavior. The hierarchical structure is built on recurring substructures in a bottom-up fashion. We formulate the behavior discovery and verification problem as a graph grammar induction and parsing problem, i.e., automatically iteratively mining qualified patterns and then constructing graph rewriting rules. Furthermore, using the induced grammar to parse the behavioral structure of a new program could verify if the program has the same behavioral properties specified by the grammar.

A bibliographic database houses vital information pertinent to the research community. By extracting implicit hidden information in such data collections, social networks can be used to represent the various data inter-relationships.In this paper, we propose a new method for the discovery and visualization of research communities from bibliography information. We utilize the hierarchical overlapping communities (HOC) algorithm to identify the hierarchical overlapping communities based on the common neighborhood, and e-spring algorithm to visualize the derived communities.This method has been validated by visualizing synthetic datasets, and research communities obtained from the DBLP bibliography data.

This paper presents a grammar-induction based approach to partitioning a Web page into several small pages while each small page fits not only spatially but also logically for mobile browsing. Our approach proceeds in three steps: (1) using the grammar induction technique to generate a graph grammar, which formalizes design policies for presenting information in a clear and logic structure; (2) based on the graph grammar, a graph parser parses a Web page to recover the hierarchical logic structure underlying that Web page; (3) the extracted logic structure models the content organization in the Web page, and is used to partition the Web page into several small pages for mobile displays.

Frequent patterns in program executions represent recurring sequences of events. These patterns can be used to reveal the hidden structures of a program, and ease the comprehension of legacy systems. Existing grammar-induction approaches generally use sequential algorithms to infer formal models from program executions, in which program executions are represented as strings. Software developers, however, often use graphs to illustrate the process of program executions, such as UML diagrams, flowcharts and call graphs. Taking advantage of graphs' expressiveness and intuitiveness for human cognition, we present a graph-grammar induction approach to discovering program's behavioral patterns by analyzing execution traces represented in graphs. Moreover, to improve the efficiency, execution traces are abstracted to filter redundant or unrelated traces. A grammar induction environment called VEGGIE is adopted to facilitate the induction. Evaluation is conducted on an open source project JHotDraw. Experimental results show the applicability of the proposed approach.

Conventional force directed graph drawing methods aimto produce aesthetically pleasing visualization of graphs.The edges of the graph have more or less equal length,and there are as few crossing edges as possible. Thispaper proposes a new E-Spring algorithm for visualizingclustered directed acyclic graphs (DAGs) without nodeoverlapping, extended from the popular spring embeddermodel. In our framework, nodes are modeled as nonuniformcharged particles with weights, and a finaldrawing is derived by adjusting the positions of thenodes according to a combination of spring forces andrepulsive forces derived from electrostatic forcesbetween the nodes. Experimental results andvisualization/analysis of this method are reported.

Visualization can be very powerful in revealing cluster structures. However, directly using visualization techniques to verify the validity of clustering results is still a challenge. This is due to the fact that visual representation lacks precision in contrasting clustering results. To remedy this problem, in this paper we propose a novel approach, which employs a visualization technique called HOV (hypothesis oriented verification and validation by visualization) which offers a tunable measure mechanism to project clustered subsets and non-clustered subsets from a multidimensional space to a 2D plane. By comparing the data distributions of the subsets, users not only have an intuitive visual evaluation but also have a precise evaluation on the consistency of cluster structure by calculating geometrical information of their data distributions

The model driven architecture (MDA) has been widely used as a paradigm in software development. This paper presents an overview on the current research in the model driven architecture. We analyze the key concepts of the MDA by illustrative examples, explore the existing approaches and tools that support model transformation - the essential part of the MDA, and classify these methods based on a multidimensional scheme. Furthermore, this paper summarizes the current technical achievements of model transformation techniques in software development at different abstraction levels of a system.

Design patterns are generic design solutions that can be applied and composed in different applications where pattern-related information is generally implicit in the Unified Modeling Language (UML) diagrams of the applications. It is unclear in which pattern instances each modeling element, such as class, attribute, and operation, participates. It is hard for a designer to find the design patterns used in an application design. Consequently, the benefits of design patterns are compromised because designers cannot communicate with each other in terms of the design patterns they used and their design decisions and trade-offs. In this paper, we present a UML profile that defines new stereotypes, tagged values, and constraints for tracing design patterns in UML diagrams. These new stereotypes and tagged values are attached to a modeling element to explicitly represent the role the modeling element plays in a design pattern so that the user can identify the pattern in a UML diagram. Based on this profile, we also develop a Web service (tool) for explicitly visualizing design patterns in UML diagrams. With this service, users are able to visualize design patterns in their applications and compositions because pattern-related information can be dynamically displayed. A real-world case study and a comparative experiment with existing approaches are conducted to evaluate our approach.

Cluster analysis is an important technique that has been used in data mining. However, cluster analysis provides numerical feedback making it hard for users to understand the results better; and also most of the clustering algorithms are not suitable for dealing with arbitrarily shaped data distributions of datasets. While visualization techniques have been proven to be effective in data mining, their use in cluster analysis is still a major challenge, especially in data mining applications with high-dimensional and huge datasets. This paper introduces a novel approach, Hypothesis Oriented Verification and Validation by Visualization, named HOV3, which projects datasets based on given hypotheses by visualization in 2D space. Since HOV3 approach is more goal-oriented, it can assist the user in discovering more precise cluster information from high-dimensional datasets efficiently and effectively.

More and more ontologies are emerging across bioinformatics domains to represent and define domain knowledge, such as gene ontology, anatomy ontology and disease ontology. To integrate these heterogeneous ontologies is becoming critically important for applications utilizing multiple ontologies. Because the entities described in the ontology often overlap with other entities in other ontologies, a mapping between two corresponding terms is required and becoming the key to the integration of heterogeneous ontologies. Based on a previously developed schema matching algorithm, this paper presents a framework for mapping and integration of heterogeneous biomedical ontologies. The framework detects possible false mappings intelligently and provides intuitive interfaces for users to customize mappings and for applications to integrate and access biomedical ontologies

This paper presents a framework supporting collaborative efforts between the designer and developer of software-intensive systems. The framework realizes a two-layered meta-tool concept: a powerful specification language in the form of a grammar for the designer at the upper layer; and a generative mechanism for generating domain-specific design languages at the lower layer. The paper introduces the spatial graph grammar formalism as the specification language and the generation mechanism. We also describe successful and potential application domains of the two-layered framework

The development of visual query languages can ease the retrieval of human motion data. This paper describes a process that allows users to specify queries for human motions describing disabilities, medical conditions, testing criteria, or other domain requirements as an encoding of grammar rules

In this paper, the sensitivity of Adalines to weight perturbation is discussed. According to the discrete feature of Adalines' input and output, the sensitivity is defined as the probability of an Adaline's erroneous outputs due to weight perturbation with respect to all possible inputs. By means of hypercube model and analytical geometry method, a heuristic algorithm is given to accurately compute the sensitivity. The accuracy of the algorithm is verified by computer simulations.

In a graphical user interface, physical layout and abstract structure are two important aspects of a graph. This article proposes a new graph grammar formalism which integrates both the spatial and structural specification mechanisms in a single framework. This formalism is equipped with a parser that performs in polynomial time with an improved parsing complexity over its nonspatial predecessor, that is, the Reserved Graph Grammar. With the extended expressive power, the formalism is suitable for many user interface applications. The article presents its application in adaptive Web design and presentation.

Instead of UML diagrams, an alternative approach is using graph grammars according to G. Rozenberg (1997) to specify meta-models. With the capability of formally specifying syntax in a visual fashion, graph grammars have gained much attention in recent years. Based on graph grammar formalism, called the reserved graph grammar (RGG) as stated in D.Q. Zhang et al. (2001), this paper proposes an intuitive yet formal approach to specifying models. More specially, given a graph grammar defining a meta-model, a visual language generator can automatically generate a graphical environment, which allows users to directly manipulate models by drawing box-line diagrams. A graph transformation engine underlying the environment can validate the integrity of user-designed models.

Compared with texts, graphs are more intuitive to express comparative and structural information. Many graphical approaches, however, lack a formal basis for precise specifications in the design. This paper proposes a generative style-driven framework for software architecture specification based on a visual language formalism. In this framework, the designer uses graphical notations to define architectural styles. Given the graphical specifications, a visual language generator, i.e. a meta-tool, can automatically generate a specific design environment, which is suitable for the users without any knowledge of formal methods to directly manipulate software architectures by drawing box-line graphs. The graph transformation engine underlying the design environment can automatically validate structural integrity and reveal the hierarchical structure of a user-defined software architecture.

Enhancing the reserved graph grammar (RGG) formalism, this paper introduces a size-increasing condition on the structure of graph grammars' productions to simplify the definition of graph grammars, and a general parsing algorithm to extend the power of the RGG parsing algorithm.

GOP is a graph-oriented programming model which aims at providing high-level abstractions for configuring and programming cooperative parallel processes. With GOP, the programmer can configure the logical structure of a parallel/distributed program by constructing a logical graph to represent the communication and synchronization between the local programs in a distributed processing environment. This paper describes a visual programming environment, called VisualGOP, for the design, coding, and execution of GOP programs. VisualGOP applies visual techniques to provide the programmer with automated and intelligent assistance throughout the program design and construction process. It provides a graphical interface with support for interactive graph drawing and editing, visual programming functions and automation facilities for program mapping and execution. VisualGOP is a generic programming environment independent of programming languages and platforms. GOP programs constructed under VisualGOP can run in heterogeneous parallel/distributed systems. Copyright © 2005 John Wiley & Sons, Ltd.

FAÇADE (Fast and Automatic Clustering Approach to Data Engineering) is a spatial clustering tool that can discover clusters of different sizes, shapes, and densities in noisy spatial data. Compared with the existing clustering methods, FAÇADE has several advantages: first, it separates true data and noise more effectively. Second, most steps of FAÇADE are automatic. Third, it requires only O(nlogn) time. 2D and 3D visualizations are used in FAÇADE to assist parameter selection and result evaluation. More information on FAÇADE is available at: http://viscomp.utdallas.edu/FACADE.

This paper presents a parser for a spatial graph grammar formalism, which integrates both the spatial and structural specification mechanisms in a single framework. By taking advantage of the spatial information, the parser performs in polynomial time with an improved parsing complexity over its non-spatial predecessor, i.e. the Reserved Graph Grammar.

This paper presents a new graph grammar formalism, which integrates both the spatial and structural specification mechanisms in a single framework. In addition to nodes and edges, this formalism treats spatial constraints as a type of language constructs in the abstract syntax. With the extended expressive power, semantic and structural requirements can be intuitively specified through spatial notations.

Model management offers a higher level interface than current techniques for metadata management, and generic operators drastically reduce amount of programming for metadata applications. The interactive nature of generic model management operators inevitably demands an intuitive representation. This paper proposes a visual representation for model management operators based on graph transformation. Graph transformation formalisms, as the theoretic foundation of many visual programming languages, can formally represent model management operators by visual and intuitive expressions. By using graphical representations users can easily comprehend and manipulate the operators and desired outputs.

This paper reports an empirical study that explores the problem of finding a highly-efficient, user-friendly interface design method on small display devices. We compared three models using our PDA interface simulator: presentation optimization method, semantic conversion method, and zooming method. A controlled experiment has been carried out to identify the pros and cons of each method. The results show that of the three interface methods, the zooming method is slightly better than the semantic conversion method, while they both outperform the optimizing presentation method.

Based on the Reserved Graph Grammar (RGG), this paper presents a unified framework to manage model-based information on the Web in a hierarchical structure. The framework allows models, schemas, and data instances to be represented explicitly and uniformly. The uniform representation of the framework also enables simple user-defined graph transformation rules for different Web data models to translate schemas and data instances between different formats. In addition, the framework implements a set of prototype tools for users to identify meta-primitives at the meta-model level, to define a model or schema by specifying a set of graph grammar rules and to draw the structure of data instances. These features promote a wide scope of Web-related applications, such as information exchange between different organizations, and integration of data coming from heterogeneous information sources.

The concepts and ideas of domain-speci c languages (DSLs) have b e e n w ell studied over the past decades.Most studies show signi cant bene ts of the use of DSLs over traditional programming techniques, e.g.increased reliability and maintainability of the produced software and better support of reuse of code.The use of DSLs and their bene ts have b e e n w ell documented.However, there is only little literature available on the relation between software engineering methods and models on the one hand and DSLs on the other hand.Second to that, the development o f a D S L i s often an ad-hoc process, for which there seems to be no default methodology.Our ambition is to eventually come to a methodology for the construction of DSLs and their use in a software engineering process.We elaborate on the role a domain-speci c language plays, or should play, in the software engineering process.We g i v e an outline of an approach to the software engineering process that is in line with customary software engineering methods, models and best practices.We refer to this approach a s t h e language-driven approach.The language-driven approach s e r v es as a rst step in the development of a real methodology.The language-driven approach c o m bines several areas of expertise.First, it relies heavily on techniques used for domain analysis.Second, it combines techniques that are used in software engineering.A domain analysis must be conducted to obtain the basic concepts and notions that are needed to describe a set of problems.The design of a language (and the possible co-design of a software product) rely heavily on techniques that are de ned and used for software engineering, such as formal methods and programming paradigms.This is motivated by the fact that a language consists not only of a syntax, but also of a semantics and its pragmatics.We focus on the language-driven approach and we illustrate the approach w i t h a s m a l l and instructive case study.

Software architecture and design are usually modeled and represented by informal diagrams, such as architecture diagrams and UML diagrams. While these graphic notations are easy to understand and are convenient to use, they are not amendable to automated verification and transformation. This paper provides graph grammars for architecture and UML class diagrams. These grammars enable a high level of abstraction for the general organization of a class of software architectures, and form a basis for various analysis and transformations. In this approach, software verification is performed through a syntax analyzer. Architecture transformation is achieved by applying predefined transformation rules.

The diversity of multimedia devices is an important feature on the Internet, which demands an executable mechanism to intelligently adjust the appearance of a document according to different viewing contexts. In order to satisfy this requirement, a graph-grammar-based approach for multimedia authoring is presented by extending context-sensitive graph grammar formalism, reserved graph grammar (RGG), with the capability of spatial specifications. Based on a parser for the RGG, a graph grammar functions as a mapping from the structure of a document to a desirable layout, and graph transformations arc automatically performed when contexts arc changed.

This paper investigates the multi-version approach to consistency maintenance in Internet-based real-time collaborative editing systems. It proposes a new multi-versioning scheme that is able to preserve individual users' intentions while guaranteeing convergent document states. The scheme has been implemented in Java in a prototype called POLO.

This paper presents Visper, a novel object-oriented framework that identifies and enhances common services and programming primitives, and implements a generic set of classes applicable to multiple programming models in a distributed environment. Groups of objects, which can be programmed in a uniform and transparent manner, and agent-based distributed system management, are also featured in Visper. A prototype system is designed and implemented in Java, with a number of visual utilities that facilitate program development and portability. As a use case, Visper integrates parallel programming in an MPI-like message-passing paradigm at a high level with services such as checkpointing and fault tolerance at a lower level. The paper reports a range of performance evaluation on the prototype and compares it to related works.

This paper presents a visual approach to the representation, validation of XML document structures and transformation of one XML structure to another. The underlying theory of our approach is a context-sensitive graph grammar formalism. The paper demonstrates the conciseness and expressiveness of the graph grammar formalism. An example XML structure is provided and its graph grammar representation, validation and transformation to another structure are presented.

Intention preservation is an important aspect of consistency maintenance in distributed group editors. In this paper, we first introduce a classification framework for various kinds of intentions, then propose LOVOT (post-LOcking, multi-Versioning and Operational Transformation), an integrated scheme for intention preservation. With this scheme, individual users' compatible intentions are preserved by operational transformation, individuals' conflicting intentions are preserved by multi-versioning, and the preservation of a syntactically and semantically consistent group intention is facilitated by post-locking. An algorithm for preserving individual users' intentions is discussed in detail. The integrated scheme is fully distributed, highly responsive, non-blocking and capable of avoiding any locking overhead.

Graph grammars may be used as natural and powerful syntax-definition formalisms for visual programming languages. Yet most graph-grammar parsing algorithms presented so far are either unable to recognize interesting visual languages or tend to be inefficient (with exponential time complexity) when applied to graphs with a large number of nodes and edges. This paper presents a context-sensitive graph grammar called reserved graph grammar, which can explicitly and completely describe the syntax of a wide range of diagrams using labeled graphs. The parsing algorithm of a reserved graph grammar uses a marking mechanism to avoid ambiguity in parsing and has polynomial time complexity in most cases. The paper defines a constraint condition under which a graph defined in a reserved graph grammar can be parsed in polynomial time. An algorithm for checking the condition is also provided.

The implementation of visual programming languages (VPLs) and their supporting environments is time-consuming and tedious. To ease the task, researchers have developed some high-level tools to reduce the development effort. None of these tools, however, can be easily used to create a complete visual language in a seamless way as the lex/yacc tools do for textual language constructions. This paper presents the design, construction and application of a generic visual language generation environment, called VisPro. The VisPro design model improves the conventional model-view-controller framework in that its functional modules are decoupled to allow independent development and integration. The VisPro environment consists of a set of visual programming tools. Using VisPro, the process of VPL construction can be divided into two steps: lexicon definition and grammar specification. The former step defines visual objects and a visual editor, and the latter step provides language grammars with graph rewriting rules. The compiler for the VPL is automatically created according to the grammar specification. A target VPL is generated as a programming environment which contains the compiler and the visual editor. The paper demonstrates how VisPro is used by building a simple visual language and a more complex visual modeling language for distributed programming.

Communication latency presents a major challenge to achieving high responsiveness for Internet based cooperative editing systems. The authors propose conflict control locking (post-locking) schemes for conflict resolution in real time object based cooperative graphics editors. With these schemes, no locking request is needed before applying an editing operation to an object, instead, a lock will automatically be generated by the system if a conflict occurs. Lock ownership assigning rules are devised for the schemes. With a specific post-locking approach, algorithms and implementation techniques such as lock synchronisation detection, voting, and group intention realisation, are discussed in detail.

As an objected-oriented programming language and a platform-independent environment, Java has been attracting much attention. However, the trade-off between portability and performance has not spared Java. The initial performance of Java programs has been poor, due to the interpretive nature of the environment. In this paper we present the communication performance results of three different types of message-passing programs: native, Java and native communications, and pure Java. Despite concerns about performance and numerical issues, we believe the obtained results confirm that high-performance parallel computing in Java is possible, as the technology matures and the approach is pragmatic.Copyright © 2000 John Wiley & Sons, Ltd.

The attractiveness of visual programming stems in large part from the direct interaction with program elements as if they were real objects, since people deal better with concrete objects than with the abstract. This paper describes a new graph based software visualization tool for parallel message-passing programming named Visper that combines the levels of abstraction at which message-passing parallel programs are expressed and makes use of compositional programming. Central to the tool is the Process Communication Graph that correlates both the control and data flow graphs into a single graph formalism, without a need for complex textual annotation. The graph can express static and runtime communication and replication structures, as found in Message Passing Interface (MPI) and Parallel Virtual Machine (PVM). It also forms the basis for visualizing parallel debugging and performance.

Analysing and visualising the run time behaviour of a distributed program requires collecting performance data during its execution. This is usually done through program instrumentation that inserts trace recording instructions into the program. The intrusiveness of inserted extra code to the original program may make the existing errors vanish, or can cause new errors to appear. It will not only change the real time execution speed, but also change the probability of making particular non deterministic choices. W. Cai and S.J. Turner (1994) proposed a logical clock approach (LCA) to control the inter process communication. It uses a logical clock in each process to reflect the real time execution of the process when running without monitoring. Not relying on any special hardware, this approach achieves minimum intrusiveness and makes the monitoring effect on the original program completely independent from the amount of time spent on monitoring activities. A main problem with LCA is that in an event of non deterministic communication and when several processes wait on each other's logical clock to advance, a deadlock may occur. LCA uses a carrier null message algorithm to break the deadlock situation. But sending an extra null message will distort the event timing information. Therefore, LCA is only suitable for debugging rather than for performance tuning. We present a major improvement to LCA, which preserves the timing information without introducing any deadlock. Our improved approach can support both debugging and performance tuning. We call our clocks virtual clocks to avoid confusion.

Designing a parallel program to fully utilise the processing power of a multiprocessor machine requires a series of performance analysis and tuning. The paper describes a performance tuning tool for message passing parallel programs. The tool combines the advantages of relational databases and spreadsheets to organise the performance data and analyse the program performance through visualisation. Various graphical displays which assist the user to fine tune the performance of message passing programs are discussed.