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Since Service-Oriented Architecture (SOA) was introduced just a decade ago, SOA has become to the new generation technology for software development. With fast growing numbers of offered services, selecting the suitable services is a crucial task and challenges. Especially, selecting the service which is not only suitable in general but also suitable to the particular requester's context is big challenge because context information changes rapidly on different time, environment and task. In today's working environment runtime context-aware service selection has a quite remarkable usage and raises an interesting research direction. There are three main issues on this research: (1) context-awareness, (2) finding a suitable service selection method, (3) considering the global composition context between services when they integration. Recently, many approaches have been proposed for tackling the service selection issues. However, few approaches take the runtime context information into account. In this talk, I am going to introduce a novel context-aware automatic service selection process and demonstrate the prototype.

The process supports maximum automatic mechanism includes three main components: context-aware selection criteria, type-based Logic Scoring Preference extended selection method and composition context. Each of the components gives a solution to a research issue. The process adopted the Semantic Web technology to modelling the context information and service NFPs. The correctness and scalability of the process are evaluated against real world service selection scenarios.

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Higher dimensional category theory is a vibrant research area of fundamental significance to such different areas as theoretical physics, algebraic topology, and computer science. In this talk, we will consider a particular class of higher-dimensional categories, namely weakly globular cat^n-groups. They are sufficient to model connected n-types and have several advantages over previously considered models.

TBA.

TBA.

slides Real-Time Maude is a tool that extends the rewriting-logic-based Maude system to support the executable formal modeling and analysis of real-time systems. Real-Time Maude is characterized by its general and expressive, yet intuitive, specification formalism, and offers a spectrum of formal analysis methods, including: rewriting for simulation purposes, search for reachability analysis, and temporal logic model checking. Our tool is particularly suitable to specify real-time systems in an object-oriented style, and its flexible formalism makes it easy to model different forms of communication. This modeling flexibility, and the usefulness of Real-Time Maude for both simulation and model checking, has been demonstrated in advanced state-of-the-art applications, including scheduling and wireless sensor network algorithms, communication and cryptographic protocols, and in finding several bugs in embedded car software that were not found by standard model checking tools employed in industry. This talk gives a high-level overview of Real-Time Maude and some of its applications, and briefly discusses completeness of analysis for dense-time systems.

Broadcast Scheduling is a popular method for disseminating information in response to client requests. There is a set of pages of information, and clients request pages at different times. However, multiple clients can have their requests satisfied by a single broadcast of the requested page. We consider several related broadcast scheduling problems. One central problem asks to minimize the maximum response time (over all requests). Another related problem is the version in which every request has a release time and a deadline, and the goal is to maximize the number of requests that meet their deadlines. While approximation algorithms for both these problems were proposed several years back, it was not known if they were NP-complete. One of our main results is that both these problems are NP-complete. Furthermore, we give a proof that FIFO is a 2-competitive online algorithm for minimizing the maximum response time and that there is no better deterministic online algorithm (these results had been claimed earlier, but without complete proofs). We also give a lower bound on the integrality gap of the natural LP formulation of the problem of maximizing the number of requests that meet their deadlines.
Joint work with Jessica Chang, Renars Gailis and Samir Khuller.

Forecasting is an important activity in finance. Traditionally, forecasting has been done with in-depth knowledge in finance and the market. Advances in computational intelligence have created opportunities that were never there before. Computational finance techniques, machine learning in particular, can dramatically enhance our ability to forecast. They can help us to forecast ahead of our competitors and pick out scarce opportunities. In this talk, I shall explain some of the opportunities offered by computational intelligence and some of the achievements so far. I shall also explain the underlying technologies and explores the research horizon.

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