The purpose of this article is to improve understanding of internationalization as a strategic response to the catalysts of globalization and the knowledge society. The paper will attempt to critically identify and interpret how the aforementioned elements are being recontextualized and translated into responsive internationalization policies and systemic institutional change. The article takes a critical analysis approach on current internationalization efforts and provides a conceptual framework for developing a performance indicator set through a combination of institutional change theory (North 1990) and the Delta cycle for internationalization (Rumbley 2010). Recommendations on future research areas are made at the conclusion of the article.
Since 2014, Carleton University Library has been adding to the ways it practices collection development. In addition to the subject liaison firm order model, we have added 3 successful user-centred ways to acquire material. We ended our approval plan and used its selection framework to create a DDA plan. We started a textbook purchasing program in Reserves, and we instituted print purchase on demand procedures in ILL. This poster provides an overview and key takeaways for each initiative.
Libraries are quickly becoming spaces for more than just books and journals. At Carleton University MacOdrum Library, we used Minecraft to introduce elementary and high school students to the power of gaming as a tool to foster education, research and collaboration. In May 2015, we encouraged students to take part in a project that engaged them with a local project called the LeBreton Flats Redevelopment Project. The redevelopment project led by the National Capital Commission (NCC), shortlisted four developers and published their proposals for the community to see. Using the criteria presented by the four pre-qualified proponents, the students were asked to research and propose their own ideas for the space. Using a scale version of the space in Minecraft, the students built their proposed plan for the space in a 1:1 scale replica of LeBreton Flats.
Energy modeling and optimization studies can facilitate the design of cost-effective, low-energy buildings. However, this process inevitably involves uncertainties such as predicting occupant behavior, future climate, and econometric parameters. As presently practiced, energy modelers typically do not quantify the implications of these unknowns into performance outcomes. This paper describes an energy modeling approach to quantify economic risk and better inform decision makers of the economic feasibility of a project. The proposed methodology suggests how economic uncertainty can be quantified within an optimization framework. This approach improves modeling outcomes by factoring in the effect of variability in assumptions and improves confidence in simulation results. The methodology is demonstrated using a net zero energy commercial office building case study located in London, ON, Canada.
The use of open linked data in libraries is quickly developing as means of connecting digital content from the web to local library collections. In the world of cataloguing, metadata, and authority control, using controlled vocabularies through open linked data presents the possibility of providing library patrons with access to a seemingly unlimited expanse of digital resources. Encouraged by this potential, the Carleton University Library is currently implementing open linked data models within its institutional repository in order to connect users to digital content within our repository, our ILS, and beyond. This poster presents the ideas and processes behind this innovative project, and hopes to inspire other libraries to implement open linked data concepts in order to enhance the discoverability of their own digital collections.
• Clear explanation of open linked data concepts using diagrams to illustrate key points
• How libraries of all sizes can utilize linked data for authority control to expand access to digital collections
• How libraries can use linked data to promote and expand access to OA publications
The field of game playing is a particularly well-studied area within the context of AI, leading to the development of powerful techniques, such as the alpha-beta search, capable of achieving competitive game play against an intelligent opponent. It is well known that tree pruning strategies, such as alpha-beta, benefit strongly from proper move ordering, that is, searching the best element first. Inspired by the formerly unrelated field of Adaptive Data Structures (ADSs), we have previously introduced the History-ADS technique, which employs an adaptive list to achieve effective and dynamic move ordering, in a domain independent fashion, and found that it performs well in a wide range of cases. However, previous work did not compare the performance of the History-ADS heuristic to any established move ordering strategy. In an attempt to address this problem, we present here a comparison to two well-known, acclaimed strategies, which operate on a similar philosophy to the History-ADS, the History Heuristic, and the Killer Moves technique. We find that, in a wide range of two-player and multi-player games, at various points in the game’s progression, the History-ADS performs at least as well as these strategies, and, in fact, outperforms them in the majority of cases.
In this paper, we propose a novel online classifier for complex data streams which are generated from non-stationary stochastic properties. Instead of using a single training model and counters to keep important data statistics, the introduced online classifier scheme provides a real-time self-adjusting learning model. The learning model utilizes the multiplication-based update algorithm of the Stochastic Learning Weak Estimator (SLWE) at each time instant as a new labeled instance arrives. In this way, the data statistics are updated every time a new element is inserted, without requiring that we have to rebuild its model when changes occur in the data distributions. Finally, and most importantly, the model operates with the understanding that the correct classes of previously-classified patterns become available at a later juncture subsequent to some time instances, thus requiring us to update the training set and the training model. The results obtained from rigorous empirical analysis on multinomial distributions, is remarkable. Indeed, it demonstrates the applicability of our method on synthetic datasets, and proves the advantages of the introduced scheme.