18173014
A Fast Correlation Attack on the Shrinking Generator
In this paper we demonstrate a fast correlation attack on the shrinking generator with known connections. Our attack is applicable to arbitrary weight feedback polynomial of the generating LFSR and comparisons with other known attacks show that our attack offers good trade-offs between required keystream length, success probability and complexity. Our result confirms Golic's conjecture that the shrinking generator may be vulnerable to fast correlation attacks without exhaustively searching through all possible initial states of some LFSR is correct.
12677295
Security Analysis of the Generalized Self-shrinking Generator
In this paper, we analyze the generalized self-shrinking generator newly proposed in [8]. Some properties of this generator are described and an equivalent definition is derived, after which two attacks are developed to evaluate its security. The first attack is an improved clock-guessing attack using short keystream with the filter function (vector G) known. The complexity of this attack is O(20.694n), where n is the length of the LFSR used in the generator. This attack shows that the generalized self-shrinking generator can not be more secure than the self-shrinking generator, although much more computations may be required by it. Our second attack is a fast correlation attack with the filter function (vector G) unknown. We can restore both the initial state of the LFSR with arbitrary weight feedback polynomial and the filter function (vector G) with complexity much lower than the exhaustive search. For example, for a generator with 61-stage LFSR, given a keystream segment of 217.1 bits, the complexity is around 256, which is much lower than 2122, the complexity of the exhaustive search.
246827
Handling Biographical Questions with Implicature
Traditional question answering systems adopt the following framework: parsing questions, searching for relevant documents, and identifying/generating answers. However, this framework does not work well for questions with hidden assumptions and implicatures. In this paper, we describe a novel idea, a cascading guidance strategy, which can not only identify potential traps in questions but further guide the answer extraction procedure by recognizing whether there are multiple answers for a question. This is the first attempt to solve implicature problem for complex QA in a cascading fashion using N-gram language models as features. We here investigate questions with implicatures related to biography facts in a web-based QA system, Power-Bio. We compare the performances of Decision Tree, Naive Bayes, SVM (Support Vector Machine), and ME (Maximum Entropy) classification methods. The integration of the cascading guidance strategy can help extract answers for questions with implicatures and produce satisfactory results in our experiments.
30786575
Content-Based Retrieval of Multimedia Information
The recent information explosion has led to massively increased demand for multimedia data storage and retrieval techniques. Content-based retrieval is an important alternative and complement to the traditional keyword-based searching for multimedia data and can greatly enhance information management. For the last ten years, the Biomedical and Multimedia Information Technology (BMIT) Group and recently the Center for Multimedia Signal Processing (CMSP) have conducted systematic studies and research activities on this topic. Some of the works relating to content-based image/video retrieval and their applications are briefly presented in this paper.
4695730
MRE: A Study on Evolutionary Language Understanding
The lack of well-annotated data is always one of the biggest problems for most training-based dialogue systems. Without enough training data, it’s almost impossible for a trainable system to work. In this paper, we explore the evolutionary language understanding approach to build a natural language understanding machine in a virtual human training project. We build the initial training data with a finite state machine. The language understanding system is trained based on the automated data first and is improved as more and more real data come in, which is proved by the experimental results.
1310259
Mining and Re-ranking for Answering Biographical Queries on the Web
The rapid growth of the Web has made itself a huge and valuable knowledge base. Among them, biographical information is of great interest to society. However, there has not been an efficient and complete approach to automated biography creation by querying the web. This paper describes an automatic web-based question answering system for biographical queries. Ad-hoc improvements on pattern learning approaches are proposed for mining biographical knowledge. Using bootstrapping, our approach learns surface text patterns from the web, and applies the learned patterns to extract relevant information. To reduce human labeling cost, we propose a new IDF-inspired reranking approach and compare it with pattern's precision-based re-ranking approach. A comparative study of the two re-ranking models is conducted. The tested system produces promising results for answering biographical queries.
6618252
Learning to Detect Conversation Focus of Threaded Discussions
In this paper we present a novel feature-enriched approach that learns to detect the conversation focus of threaded discussions by combining NLP analysis and IR techniques. Using the graph-based algorithm HITS, we integrate different features such as lexical similarity, poster trustworthiness, and speech act analysis of human conversations with feature-oriented link generation functions. It is the first quantitative study to analyze human conversation focus in the context of online discussions that takes into account heterogeneous sources of evidence. Experimental results using a threaded discussion corpus from an undergraduate class show that it achieves significant performance improvements compared with the baseline system.