Memory Networks

We describe a new class of learning models called memory networks. Memory networks reason with inference components combined with a long-term memory component; they learn how to use these jointly. The long-term memory can be read and written to, with the goal of using it for prediction. We investigate these models in the context of question answering (QA) where the long-term memory effectively acts as a (dynamic) knowledge base, and the output is a textual response. We evaluate them on a large-scale QA task, and a smaller, but more complex, toy task generated from a simulated world. In the latter, we show the reasoning power of such models by chaining multiple supporting sentences to answer questions that require understanding the intension of verbs

A system design for human factors studies of speech-enabled Web browsing

This paper describes the design of a system which will subsequently be used as the basis of a range of empirical studies aimed at discovering how best to harness speech recognition capabilities in multimodal multimedia computing. Initial work focuses on speech-enabled browsing of the World Wide Web, which was never designed for such use. System design is complete, and is being evaluated via usability testing

A Language-Based Model for Specifying and Staging Mixed-Initiative Dialogs

Specifying and implementing flexible human-computer dialogs, such as those used in kiosks, is complex because of the numerous and varied directions in which each user might steer a dialog. The objective of this research is to improve dialog specification and implementation. To do so we developed a model for specifying and staging mixed-initiative dialogs. The model involves a dialog authoring notation, based on concepts from programming languages, for specifying a variety of unsolicited reporting, mixed-initiative dialogs in a concise representation that serves as a design for dialog implementation. Guided by this foundation, we built a dialog staging engine which operationalizes dialogs specified in this notation. The model, notation, and engine help automate the engineering of mixed-initiative dialog systems. These results also provide a proof-of-concept for dialog specification and implementation from the perspective of theoretical programming languages. The ubiquity of dialogs in domains such as travel, education, and health care with the increased use of interactive voice-response systems and virtual environments provide a fertile landscape for further investigation of these results

Applying Data Mining Algorithms on Open Source Intelligence to Combat Cyber Crime

In this dissertation, we investigate the applications of data mining algorithms on online criminal information. Ever since the entry of the information era, the development of the world wide web makes the convenience of peoples\u27 lives to the next level. However, at the same time, the web is utilized by criminals for illegal activities like drug smuggling and online fraudulence. Cryptomarkets and instant message software are the most popular two online platforms for criminal activities. Here, we try to extract useful information from related open source intelligence in these two platforms with data mining algorithms. Cryptomarkets (or darknet markets) are commercial hidden-service websites that operate on The Onion Router (Tor) anonymity network, which have grown rapidly in recent years. In this dissertation, we discover interesting characteristics of Bitcoin transaction patterns in cryptomarkets. We present a method to identify vendors\u27 Bitcoin addresses by matching vendors\u27 feedback reviews with Bitcoin transactions in the public ledger. We further propose a cost-effective algorithm to accelerate both steps effectively. Comprehensive experimental results have demonstrated the effectiveness and efficiency of the proposed method. Instant message(IM) software is another base for these criminal activities. Users of IM applications can easily hide their identities while interacting with strangers online. In this dissertation, we propose an effective model to discover hidden networks of influence between members in a group chat. By transferring the whole chat history to sequential events, we can model message sequences to a multi-dimensional Hawkes process and learn the Granger Causality between different individuals. We learn the influence graph by applying an expectation–maximization(EM) algorithm on our text biased multi-dimensional Hawkes Process. Users in IM software normally maintain multiple accounts. We propose a model to cluster the accounts that belong to the same user