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

How to Do Things With Words

Sentiment analysis and opinion mining is the field of study that analyzes people's opinions, sentiments, evaluations, attitudes, and emotions from written language. It is one of the most active research areas in natural language processing and is also widely studied in data mining, Web mining, and text mining. In fact, this research has spread outside of computer science to the management sciences and social sciences due to its importance to business and society as a whole. The growing importance of sentiment analysis coincides with the growth of social media such as reviews, forum discussions, blogs, micro-blogs, Twitter, and social networks. For the first time in human history, we now have a huge volume of opinionated data recorded in digital form for analysis. Sentiment analysis systems are being applied in almost every business and social domain because opinions are central to almost all human activities and are key influencers of our behaviors. Our beliefs and perceptions of reality, and the choices we make, are largely conditioned on how others see and evaluate the world. For this reason, when we need to make a decision we often seek out the opinions of others. This is true not only for individuals but also for organizations. This book is a comprehensive introductory and survey text. It covers all important topics and the latest developments in the field with over 400 references. It is suitable for students, researchers and practitioners who are interested in social media analysis in general and sentiment analysis in particular. Lecturers can readily use it in class for courses on natural language processing, social media analysis, text mining, and data mining. Lecture slides are also available online.

/pdf/sentiment-analysis-and-opinion-mining-8314pykwrf.pdf

Sentiment Analysis and Opinion Mining

An Introduction to MultiAgent Systems.

ChatGPT for good? On opportunities and challenges of large language models for education

We investigate how social networks can be used in recommendation generation in taste related domains. Social Filtering (using social networks for neighborhood generation) is compared to Collaborative Filtering with respect to prediction accuracy in the domain of rating clubs. After reviewing background and related work, we present an extensive empirical study where over thousand participants from a social networking community where asked to provide ratings for clubs in Munich. We then compare a typical traditional CF-approach to a social recommender / social filtering approach where friends from the underlying social network are used as rating neighborhood and analyze the experiments statistically. Surprisingly, the social filtering approach outperforms the CF approach in all variants of the experiment. The implications of the experiment for professional and private-life collaborative environments and services where recommendations play a role are discussed. We conclude with future perspectives on social recommender systems, especially in upcoming mobile environments.

Recommendations in taste related domains: collaborative filtering vs. social filtering

In this paper, we give an overview of a system (CAIMAN) that can facilitate the exchange of relevant documents between geographically dispersed people in Communities of Interest. The nature of Communities of Interest prevents the creation and enforcement of a common organizational scheme for documents, to which all community members adhere. Each community member organizes her documents according to her own categorization scheme (ontology). CAIMAN exploits this personal ontology, which is essentially the perspective of a user on a domain, for information retrieval. Related documents are retrieved on a concept granularity level from a central community document repository. To find the related concepts in the queried ontology, CAIMAN performs an ontology mapping. The ontology mapping in CAIMAN is based on a novel approach, which considers the concepts in an ontology implicitly represented by the documents assigned to each concept. Using machine learning techniques for text classification, a concept in a personal ontology is mapped to a concept in a community ontology. The CAIMAN system uses this mapping to provide document publishing and retrieval services both for the community and the user. First results of the prototype system showed that this approach can be a valid alternative to existing techniques for information retrieval.

/pdf/facilitating-the-exchange-of-explicit-knowledge-through-4nvjz0ypih.pdf

Facilitating the Exchange of Explicit Knowledge through Ontology Mappings

Context has rarely been incorporated into recommender systems so far, but physical (e.g. a user's location) or social (e.g. the social network of a user) context can be useful sources for improving recommender systems. In this paper, we first discuss some principles for context-awareness in recommender systems. Then we present our hybrid recommender system for recommending applications to users of mobile devices. Finally, we describe our approach to utilize social networks to enhance collaborative filtering. Our evaluation shows that the social recommender outperforms traditional collaborative filtering algorithms in our scenario.

Utilizing Physical and Social Context to Improve Recommender Systems

A novel method for quantitatively measuring social interactions on small temporal and spatial scales on the basis of interaction geometry (reduced to the parameters interpersonal distance and relative body orientation) with the help of infrared (IR) tracking is introduced. The method is intended to be used to establish a probabilistic classifier to identify existing social situations on the basis of measuring the two parameters for pairs of persons through a series of experiments. The classifier can then be used for characterizing the social context (as an evidence for or against established social situations) of users using sensors in mobile devices in view of useful future Mobile Social Networking services. A first experiment is conducted with the method, a number of standard classifiers including a Gaussian Mixture Model are trained and evaluated and the results are discussed.

Georg Groh

Papers

ChatGPT for good? On opportunities and challenges of large language models for education

Recommendations in taste related domains: collaborative filtering vs. social filtering

Facilitating the Exchange of Explicit Knowledge through Ontology Mappings

Utilizing Physical and Social Context to Improve Recommender Systems

Detecting Social Situations from Interaction Geometry