User-adaptive website with information palettes

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 75-76).The majority of existing websites on the Internet do not adapt to the individual user. Instead, they serve the same static content that has been created beforehand to everyone who visits the site. However, it has been shown that different people have different cognitive styles, or preferred ways in which they think, perceive information, and solve problems. Each cognitive style desires a certain type of information presented in a certain way. In this thesis, I design and implement a framework for creating user-adaptive websites that can infer a user's cognitive style from the webpages he or she visits and serve adaptive information palettes with content suited for that cognitive style.Specifically, the system first assigns ratings to each webpage, defining how each one rates along a set of cognitive style dimensions. Then it tracks a user's session on a website, compares it to sessions of past users, clusters similar sessions together, and computes the likely cognitive style of the user using a weighted average of the ratings of the webpages in the user's current session and in the cluster. I implemented this system as a customer advocacy website for General Motors. The website successfully infers users' cognitive styles and displays suitable information palettes.by Qiuyuan Jimmy Li.M.Eng

Fast Data in the Era of Big Data: Twitter's Real-Time Related Query Suggestion Architecture

We present the architecture behind Twitter's real-time related query suggestion and spelling correction service. Although these tasks have received much attention in the web search literature, the Twitter context introduces a real-time "twist": after significant breaking news events, we aim to provide relevant results within minutes. This paper provides a case study illustrating the challenges of real-time data processing in the era of "big data". We tell the story of how our system was built twice: our first implementation was built on a typical Hadoop-based analytics stack, but was later replaced because it did not meet the latency requirements necessary to generate meaningful real-time results. The second implementation, which is the system deployed in production, is a custom in-memory processing engine specifically designed for the task. This experience taught us that the current typical usage of Hadoop as a "big data" platform, while great for experimentation, is not well suited to low-latency processing, and points the way to future work on data analytics platforms that can handle "big" as well as "fast" data

Deep Multimodal Speaker Naming

Automatic speaker naming is the problem of localizing as well as identifying each speaking character in a TV/movie/live show video. This is a challenging problem mainly attributes to its multimodal nature, namely face cue alone is insufficient to achieve good performance. Previous multimodal approaches to this problem usually process the data of different modalities individually and merge them using handcrafted heuristics. Such approaches work well for simple scenes, but fail to achieve high performance for speakers with large appearance variations. In this paper, we propose a novel convolutional neural networks (CNN) based learning framework to automatically learn the fusion function of both face and audio cues. We show that without using face tracking, facial landmark localization or subtitle/transcript, our system with robust multimodal feature extraction is able to achieve state-of-the-art speaker naming performance evaluated on two diverse TV series. The dataset and implementation of our algorithm are publicly available online

(3+2)-Cycloaddition Reactions of Oxyallyl Cations

The (3+2)-cycloaddition reaction involving oxyallyl cations has proven to be a versatile and efficient approach for the construction of five-membered carbo- and heterocycles, which are prevalent frameworks in natural products and pharmaceuticals. The following article will provide a brief summary of recent disclosures on this process featuring chemo-, regio- and diastereoselective oxyallyl cycloadditions with both electron-rich and electron-deficient 2π partners

Color Filter Array Demosaicking Using High-Order Interpolation Techniques With a Weighted Median Filter for Sharp Color Edge Preservation

Demosaicking is an estimation process to determine missing color values when a single-sensor digital camera is used for color image capture. In this paper, we propose a number of new methods based on the application of Taylor series and cubic spline interpolation for color filter array demosaicking. To avoid the blurring of an edge, interpolants are first estimated in four opposite directions so that no interpolation is carried out across an edge. A weighted median filter, whose filter coefficients are determined by a classifier based on an edge orientation map, is then used to produce an output from the four interpolants to preserve edges. Using the proposed methods, the original color can be faithfully reproduced with minimal amount of color artifacts even at edges

Adaptive Colour Filter Array (CFA) Demosaicking with Mixed Order of Approximation

Adelaide, Australi