Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

Phd by thesis

Data Mining - Concepts and Techniques.

Data Mining Practical Machine Learning Tools and Techniques

How to Do Things With Words

Extended transaction models have drawn much interest recently in academia and industry [2]. Such models seek to address the limitations of traditional ACID transactions for supporting multisystem applications that operate in heterogeneous environments. Such applications are increasingly proving to be of strategic importance to a number of businesses and governmental agencies. Different transaction models, however, tend to be closed in that they cannot be easily combined with other such models, thus limiting their applicability to situations which exactly match one of them. We do not propose yet another transaction model. Instead, we have developed a general specification facility that enables the formalization of any transaction model that can be stated in terms of dependencies amongst significant events in different subtransactions. Such significant events include start, commit, and abort. We make no assumptions that these are the only kinds of events. Our approach is viable because most extended transaction models can be naturally formalized in terms of dependencies among different subtransactions.

Relaxed transaction processing

Commitments and requests are a hallmark of collaborative communication, especially in team settings. Identifying specific tasks being committed to or request from emails and chat messages can enable important downstream tasks, such as producing todo lists, reminders, and calendar entries. State-of-the-art approaches for task identification rely on large annotated datasets, which are not always available, especially for domain-specific tasks. Accordingly, we propose Lin, an unsupervised approach of identifying tasks that leverages dependency parsing and VerbNet. Our evaluations show that Lin yields comparable or more accurate results than supervised models on domains with large training sets, and maintains its excellent performance on unseen domains.

/pdf/lin-unsupervised-extraction-of-tasks-from-textual-1pfarcxe58.pdf

Lin: Unsupervised Extraction of Tasks from Textual Communication.

User location is crucial in understanding the dynamics of user activities, especially in relating their online and offline aspects. However, users' social media activities, such as tweets sent, do not always reveal their location. We consider the problem of estimating geo-tags for tweets and develop a comprehensive approach that incorporates textual content, the user's personalized behavior, and the user's social relationships. Our approach, Percimo, considers the two major kinds of communal attachment, which have distinct computational ramifications. We evaluate Percimo via three geo-social graphs based on the mutual-follow relationships of Twitter users, their geographical distance (computed from their geotagged tweets), and their preferences for location categories (collected from Foursquare). We find that Percimo yields a smaller prediction error than the two state-of-the-art approaches we compare with.

Percimo: a personalized community model for location estimation in social media

Crowdsourcing is the process of accomplishing a task by using a typically open call to invite members of the public (the “crowd”) to work on one's task. The authors describe a project assignment in which students received the opportunity of practicing crowdsourcing to accomplish a hummed song recognition task, yielding improved comprehension of the concept and high student satisfaction.

Teaching Crowdsourcing: An Experience Report

Intelligent user interfaces (in games, for example) provide opportunities for producing a high-quality, contextually relevant user experience. However, they also raise the specter of privacy violations. The authors review some of the ways in which user interfaces could glean a users private information; then the authors highlight the risks therein, and discuss ways of mitigating those risks.

Munindar P. Singh

Papers

Relaxed transaction processing

Lin: Unsupervised Extraction of Tasks from Textual Communication.

Percimo: a personalized community model for location estimation in social media

Teaching Crowdsourcing: An Experience Report

Privacy Risks in Intelligent User Interfaces