Aspect mining from a modelling perspective
Aspect mining aims at identifying, analysing, and refactoring crosscutting concerns throughout a legacy system for the purpose of improving software modularisation. This paper presents our investigation into raising the benefits of aspect mining to high level domain-specific models. A key contribution is the capability to identify crosscutting concerns early in development, which will assist in the modularisation of a design through aspects, before proceeding to the implementation level. Furthermore, our experience has led us to believe that aspects are easier to identify at the modelling level, because much of the accidental complexities of implementation concerns are not present in the corresponding modelling abstractions.

title: C-SAW and genAWeave: a two-level aspect weaving toolsuite
abstract: This demonstration will feature overviews of the C-SAW and GenAWeave projects. The first half of the presentation will introduce the concept of two-level aspect weaving, which unites a <i>model transformation</i> tool with a <i>program transformation</i> engine. From models representing an avionics application, it will be shown how changes to model properties trigger corresponding adaptations to the related source code. The second half of the demonstration is focused on using a program transformation engine to perform the task of aspect weaving. In particular, several crosscutting concerns will be woven into an Object Pascal application.

title: Metamodel-driven model interpreter evolution
abstract: Domain-specific modeling (DSM) raises the level of abstraction by specifying a metamodel that is aligned to a particular problem domain. A key feature of DSM is the construction of model interpreters that synthesize the domain models into software artifacts. In the presence of new stakeholder requirements, it is possible that a metamodel undergoes numerous changes during periods of evolution (a new instance of the classic schema evolution problem). Consequently, there is a fundamental problem in keeping the model interpreters up to date with metamodel changes. This research abstract outlines the technical challenges in formalizing model interpreter implementation with the intent to facilitate interpreter evolution in terms of metamodel migration.

title: Model replication: transformations to address model scalability
abstract: 2 SUMMARY In Model-Driven Engineering, it is often desirable to evaluate different design alternatives as they relate to scalability issues of the modeled system. A typical approach to address scalability is model replication, which starts by creating base models that capture the key entities as model elements and their relationships as model connections. A collection of base models can be adorned with necessary information to characterize a specific scalability concern as it relates to how the base modeling elements are replicated and connected together. In current modeling practice, such model replication is usually accomplished by scaling the base model manually. This is a time-consuming process that represents a source of error, especially when there are deep interactions between model components. As an alternative to the manual process, this paper presents the idea of automated model replication through a model transformation process that expands the number of elements from the base model and makes the correct connections among the generated modeling elements. The paper motivates the need for model replication through case studies taken from models supporting different domains.

title: An Information-Driven Framework for Image Mining
abstract: Image mining systems that can automatically extract semantically meaningful information (knowledge) from image data are increasingly in demand. The fundamental challenge in image mining is to determine how lowlevel, pixel representation contained in a raw image or image sequence can be processed to identify high-level spatial objects and relationships. To meet this challenge, we propose an efficient information-driven framework for image mining. We distinguish four levels of information: the Pixel Level, the Object Level, the Semantic Concept Level, and the Pattern and Knowledge Level. High-dimensional indexing schemes and retrieval techniques are also included in the framework to support the flow of information among the levels. We believe this framework represents the first step towards capturing the different levels of information present in image data and addressing the issues and challenges of discovering useful patterns/knowledge from each level.

title: Using temporal logic to specify adaptive program semantics
abstract: Computer software must dynamically adapt to changing conditions. In order to fully realize the benefit of dynamic adaptation, it must be performed correctly. The correctness of adaptation cannot be properly addressed without precisely specifying the requirements for adaptation. This paper introduces an approach to formally specifying adaptation requirements in temporal logic. We introduce A-LTL, an adaptation-based extension to linear temporal logic, and use this logic to specify three commonly used adaptation semantics. Composition techniques are developed and applied to A-LTL to construct the specification of an adaptive program. We introduce adaptation semantics graphs to visually represent the adaptation semantics, which can also be used to automatically generate specification for adaptive programs.

title: A Hand-Eye Robotic Model for Total Knee Replacement Surgery
abstract: This paper presents a hand-eye robotic model for total knee replacement (TKR) surgery. Unlike existent robot assisted TKR surgery, the proposed model is a surgical robot that combines with a movable hand-eye navigation system, which would use the full potential of both computer-assisted systems. Without using CT images and landmark pins in the patient's bones, it can directly measure the mechanical axis with high precision. This system provides a new approach of the minimally invasive surgery. Experiment results show that the proposed model is promising in the future application.

title: Itanium processor clock design
abstract: The Itanium processor is Intel's first 64-bit microprocessor  and features a highly parallel architecture fabricated using the 0.18um process. This higher integration of features requires a significant silicon real estate and high clock loading. These factors, coupled with more prominent on-die variations because of reduced device geometries, call for special techniques to manage the clock design. The Itanium processor employs very well balanced clock routing along with distributed deskew buffers (DSK) to achieve low skew. The ItaniumTM processor also includes additional features to aid performance tuning and timing debug. This paper highlights the salient features of the Itanium processor clock design and presents clock characterization data from initial silicon.

title: ArnetMiner: extraction and mining of academic social networks
abstract: This paper addresses several key issues in the ArnetMiner system, which aims at extracting and mining academic social networks. Specifically, the system focuses on: 1) Extracting researcher profiles automatically from the Web; 2) Integrating the publication data into the network from existing digital libraries; 3) Modeling the entire academic network; and 4) Providing search services for the academic network. So far, 448,470 researcher profiles have been extracted using a unified tagging approach. We integrate publications from online Web databases and propose a probabilistic framework to deal with the name ambiguity problem. Furthermore, we propose a unified modeling approach to simultaneously model topical aspects of papers, authors, and publication venues. Search services such as expertise search and people association search have been provided based on the modeling results. In this paper, we describe the architecture and main features of the system. We also present the empirical evaluation of the proposed methods.