12664404
Size Does Matter in Computer Collaboration: Heterogeneous Platform Effects on Human-Human Interaction
Because today's workforce is highly mobile, small wireless devices are being used to support mobile work collaboration. However, do computer platform differences affect such collaborations? This question is investigated through a controlled experiment that examines collaborative problem solving on different combinations of small and large computers. Experiment participants in the study work together on solving 2- and 3-dimensional variations of the popular Tetris™ game. Gender is used as a moderating variable to ascertain if prior observed effects on groups of males would be found among females. The findings indicate that platform differences affect communication and social behavior among both groups. Unexpectedly, collaboration amongst partners using small handhelds was the most cooperative and friendly because of the difficulty of solving the problem with the small device.
5599051
Constructivist learning with participatory examinations
Can we improve the examination process, making it more enjoyable and increasing perceived learning? The participatory exam process provides opportunities for students to learn from creating, reading, answering and grading exam questions. An online asynchronous learning network system facilitates the process. This paper presents the process framework, our research model and experimental results. A majority of students preferred the participatory exam and believed that it increased their learning.
6123650
Analyzing Multiple File Downloading in BitTorrent
Previous studies show that more than 85% of the peers have joined multiple torrents in BitTorrent, but theoretical work on multiple files BitTorrent downloading is rare. In this paper, we first consider the scenario of multi-torrent downloading. We present a fluid-model based analysis on the multi-torrent concurrent downloading scheme, which is implicitly adopted in practical applications, and quantitatively compare its performance with an alternative scheme of multi-torrent sequential downloading. We also consider the scenario of multi-file torrent downloading (e.g. multiple files shared within a single torrent), and find that the scheme of multi-file torrent concurrent downloading, which is explicitly engaged in practical applications, is inefficient. A new scheme named collaborative multi-file torrent sequential downloading is proposed, and we show via numerical analysis that the download performance could be improved by collaboration among the peers in different subtorrents. Finally, we propose a self-adaptive mechanism for practically deploying our multi-file torrent downloading scheme in a distributed fashion under situations when correlation among the files and majority peers' behaviors are unknown
1529609
Roogle: Supporting Efficient High-Dimensional Range Queries in P2P Systems
Multi-dimensional range query is an important query type and especially useful when the user doesn't know exactly what he is looking for. However, due to improper indexing method and high routing latency, existing schemes cannot perform well under high-dimensional situations. In this paper, we propose Roogle, a decentralized non-flooding P2P search engine that can efficiently support high-dimensional range queries in P2P systems. Roogle makes improvements on both indexing and routing. The high-dimensional data is indexed based on the maximum or minimum value among all dimensions. This simple indexing method performs rather well under high-dimensional situations and tolerates data points with missing values or different dimensionality. To speed query routing, Roogle is built on top of our proposed structured overlay – Aurelia, which has better routing performance by exploiting node heterogeneity. Aurelia also guarantees the data locality and efficiently support range queries. Experimental results from simulation validate the scalability and efficiency of Roogle.
61042466
The berkeley UNIX consultant project
UC (UNIX Consultant) is an intelligent, natural language interface that allows naive users to learn about the UNIX2 operating system. UC was undertaken because the task was thought to be both a fertile domain for artificial intelligence (AI) research and a useful application of AI work in planning, reasoning, natural language processing, and knowledge representation.The current implementation of UC comprises the following components: a language analyzer, called ALANA, produces a representation of the content contained in an utterance; an inference component, called a concretion mechanism, that further refines this content; a goal analyzer, PAGAN, that hypothesizes the plans and goals under which the user is operating; an agent, called UCEgo, that decides on UC's goals and proposes plans for them; a domain planner, called KIP, that computes a plan to address the user's request; an expression mechanism, UCExpress, that determines the content to be communicated to the user, and a language production mechanism, UCGen, that expresses UC's response in English.UC also contains a component, called KNOME, that builds a model of the user's knowledge state with respect to UNIX. Another mechanism, UCTeacher, allows a user to add knowledge of both English vocabulary and facts about UNIX to UC's knowledge base. This is done by interacting with the user in natural language.All these aspects of UC make use of knowledge represented in a knowledge representation system called KODIAK. KODIAK is a relation-oriented system that is intended to have wide representational range and a clear semantics, while maintaining a cognitive appeal. All of UC's knowledge, ranging from its most general concepts to the content of a particular utterance, is represented in KODIAK.
37163274
Concretion Inferences in NAtural Language Understanding
This paper introduces the architecture of FRESCO, an integrated inference mechanism and knowledge representation suited to a cognitive model of natural language understanding processes. A particular type of disambiguation inference known as concretion which occurs in language interpretation is examined within this framework. I put forth some principles resolving several outstanding issues surrounding concretion inferences, and describe a technique called probabilistic map passing which is employed within FRESCO to implement these principles.
13137405
Estimating Probability Distributions over Hypotheses with Variable Unification
We analyze the difficulties in applying Bayesian belief networks to language interpretation domains, which typically involve many unification hypotheses that posit variable bindings. As an alternative, we observe that the structure of the underlying hypothesis space permits an approximate encoding of the joint distribution based on marginal rather than conditional probabilities. This suggests an implicit binding approach that circumvents the problems with explicit unification hypotheses, while still allowing hypotheses with alternative unifications to interact probabilistically. The proposed method accepts arbitrary subsets of hypotheses and marginal probability constraints, is robust, and is readily incorporated into standard unification-based and frame-based models.