3032576
Generative models for name disambiguation
Name ambiguity is a special case of identity uncertainty where one person can be referenced by multiple name variations in different situations or evenshare the same name with other people. In this paper, we present an efficient framework by using two novel topic-based models, extended from Probabilistic Latent Semantic Analysis (PLSA) and Latent Dirichlet Allocation (LDA). Our models explicitly introduce a new variable for persons and learn the distribution of topics with regard to persons and words. Experiments indicate that our approach consistently outperforms other unsupervised methods including spectral and DBSCAN clustering. Scalability is addressed by disambiguating authors in over 750,000 papers from the entire CiteSeer dataset.
474823
Boosting the Feature Space: Text Classification for Unstructured Data on the Web
The issue of seeking efficient and effective methods for classifying unstructured text in large document corpora has received much attention in recent years. Traditional document representation like bag-of-words encodes documents as feature vectors, which usually leads to sparse feature spaces with large dimensionality, thus making it hard to achieve high classification accuracies. This paper addresses the problem of classifying unstructured documents on the Web. A classification approach is proposed that utilizes traditional feature reduction techniques along with a collaborative filtering method for augmenting document feature spaces. The method produces feature spaces with an order of magnitude less features compared with a baseline bag-of-words feature selection method. Experiments on both real-world data and benchmark corpus indicate that our approach improves classification accuracy over the traditional methods for both support vector machines and AdaBoost classifiers.
15852893
The swisshouse: an inhabitable interface for connecting nations
We present the Swisshouse, a novel type of "inhabitable interface" that supports direct and indirect communication and cultural awareness of habitants in different nations. The Swisshouse allows unsophisticated users to collaborate and be aware of each other over distance. By carefully sculpting the space and choreographing interactive elements in the space, the Swisshouse allows users to instantly separate, combine, and customize environments for specific collaborative activities. By providing visual representations of remote users and places, the Swisshouse provides awareness of the culture, presence and identities of virtual participants. And by tagging physical users with RFID tags, the Swisshouse makes participants aware of the social interaction occurring in the physical space. The combination of these primitives provides a single consistent inhabitable interface that integrates architectural and user interface design thinking, hardware and software design. To illustrate the power of this convergent approach, we describe several examples of real-life applications including a remote lecture, a brainstorming session, a business meeting, and an exhibition.
28276441
A Programmable BIST Core for Embedded DRAM
The programmable BIST design presented here supports various test modes using a simple controller. With the March C algorithm, the BIST circuit's overhead is under 1.3% for a 1-Mbit DRAM and under 0.3% for a 16-Mbit DRAM. The BIST design presented for embedded DRAM supports built-in self-diagnosis by feeding error information to the external tester. Moreover, using a specific test sequence, it can test for critical timing faults, reducing tester time for ac parametric test. The design supports wafer test, pre-burn-in test, burn-in, and final test. It is field-programmable; the user can program test algorithms using predetermined test elements (such as march elements, surround test elements, and refresh modes). The user can optimize the hardware for a specific embedded DRAM with a set of predetermined test elements. Our design is different from the microprogram-controlled BIST described by J. Dreibelbis et al. (1998) which has greater flexibility but higher overhead. Because our design begins at the register-transfer language level, test element insertion (for higher test coverage) and deletion (for lower hardware overhead are relatively easy.
400858
Toward good elimination orders for symbolic SAT solving
Fundamentally different from DPLL, a new approach to SAT has recently emerged that abandons search and enlists BDDs to symbolically represent clauses of the CNF. These BDDs are conjoined according to a schedule where some variables may be eliminated by quantification at each step to reduce the size of the intermediate BDDs. SAT solving then reduces to checking whether the final BDD is the zero constant. For this approach to be practical, finding a good quantification schedule is critical. We study the use of a variable elimination algorithm for this purpose, as well as two specific methods for the generation of good elimination orders based on CNF structure. While neither method appears to dominate, we show how one can heuristically select the better using the notion of width. We implement a symbolic SAT solver based on these techniques and evaluate its efficiency and robustness on a set of benchmarks against five other solvers, each having unique characteristics, including winners of the most recent SAT competition.
8770859
K-relevance: a spectrum of relevance for data sources impacting a query
Applications ranging from grid management to sensor nets to web-based information integration and extraction can be viewed as receiving data from some number of autonomous remote data sources and then answering queries over this collected data. In such environments it is helpful to inform users which data sources are "relevant" to their query results. It is not immediately obvious what "relevant" should mean in this context, as different users will have different requirements. In this paper, rather than proposing a single definition of relevance, we propose a spectrum of definitions, which we term "k-relevance", for k ≥ 0. We give algorithms for identifying k-relevant data sources for relational queries and explore their efficiency both analytically and experimentally. Finally, we explore the impact of integrity constraints (including dependencies) and materialized views on the problem of computing and maintaining relevant data sources.
10414560
Distributed Interactive Learning in Multi-Agent Systems
Both explanation-based and inductive learning techniques have proven successful in a variety of distributed domains. However, learning in multi-agent systems does not necessarily involve the participation of other agents directly in the inductive process itself. Instead, many systems frequently employ multiple instances of induction separately, or single-agent learning. In this paper we present a new framework, named the Multi-Agent Inductive Learning System (MAILS), that tightly integrates processes of induction between agents. The MAILS framework combines inverse entailment with an epistemic approach to reasoning about knowledge in a multi-agent setting, facilitating a systematic approach to the sharing of knowledge and invention of predicates when required. The benefits of the new approach are demonstrated for inducing declarative program fragments in a multi-agent distributed programming system.