Georeferencing of Place Markers in Digitized Early Maps by Using Similar Maps as Data Source

in Digital Humanities 2017: Conference Abstracts (2017)

Facteurs psychologiques, cognitifs et les influences contextuelles dans la douleur et la souffrance liée à la douleur

Poster (2016, November)

Proceedings International Workshop Exploring Old Maps 2016

Book published by University of Luxembourg (2016)

Many libraries own an extensive collection of historical maps. Beside their value as historical objects, these maps are an important source of information for researchers in various scientific disciplines ... [more ▼]

Many libraries own an extensive collection of historical maps. Beside their value as historical objects, these maps are an important source of information for researchers in various scientific disciplines. This ranges from the actual history of cartography and general history to the geographic and social sciences. With the progressing digitisation of libraries and archives, these maps become more easily available to a larger public. A basic level of digitisation consists of scanned bitmap images, tagged with some basic bibliographic information such as title, author and year of production. In order to make the maps more accessible, further metadata describing the contained information is desirable. This would enable more user-friendly interfaces, relevant queries of a database, and automatic analyses. The International Workshop on Exploring Old Maps provides a forum for the communication of results that may be useful to the community. Researchers and practitioners of many areas working on unlocking the content of old maps have contributed to this year’s program - humanities scholars, developers, computer and information scientists as well as librarians, archivists and curators. [less ▲]

Interaction Profiles for an Artificial Conversational Companion

Scientific Conference (2015, September)

Using Artificial Companions for tasks requiring long-term interaction like language learning or coaching can be approached by creating local computational models for particular interaction structures, and ... [more ▼]

Using Artificial Companions for tasks requiring long-term interaction like language learning or coaching can be approached by creating local computational models for particular interaction structures, and models reflecting changes in interaction over time. An Artificial Conversational Companion (ACC) that helps to practice conversation in a foreign language is expected to play the role of a language expert in conversation. We apply methods of Conversation Analysis to obtain data- driven models of interaction profiles for language experts and language novices from a corpus of instant messaging based dialogues between native speakers of German and advanced learners of German as a foreign language. We show different ways how the artificial agent can simulate ”doing being expert” in conversation and promote learning. [less ▲]

Finding Outliers in Satellite Patterns by Learning Pattern Identities

in Filipe, Joacquim; Fred, Ana (Eds.) Proceedings "6th International Conference on Agents an Artificial Intelligence" (2014, January)

Spacecrafts provide a large set of on-board components information such as their temperature, power and pressure. This information is constantly monitored by engineers, who capture the outliers and ... [more ▼]

Spacecrafts provide a large set of on-board components information such as their temperature, power and pressure. This information is constantly monitored by engineers, who capture the outliers and determine whether the situation is abnormal or not. However, due to the large quantity of information, only a small part of the data is being processed or used to perform anomaly prediction. A common accepted research concept for anomaly prediction as described in literature yields on using projections, based on probabilities, estimated on learned patterns from the past (Fujimaki et al., 2005) and data mining methods to enhance the conventional diagnosis approach (Li et al., 2010). Most of them conclude on the need to build a status vector. We propose an algorithm for efficient outlier detection that builds an identity chart of the patterns using the past data based on their curve fitting information. It detects the functional units of the patterns without apriori knowledge with the intent to learn its structure and to reconstruct the sequence of events described by the signal. On top of statistical elements, each pattern is allotted a characteristics chart. This pattern identity enables fast pattern matching across the data. The extracted features allow classification with regular clustering methods like support vector machines (SVM). The algorithm has been tested and evaluated using real satellite telemetry data. The outcome and performance show promising results for faster anomaly prediction. [less ▲]

News Representation with Multi-Word Features

in Proceedings ECDA (2013, July)

A Hybrid Classification System to find News that is relevant

in ECDA 2013 (2013)

Outlier Identification in Spacecraft Monitoring Data using Curve Fitting Information

in Proceedings ECDA (2013)

Why are you Silent? - Towards Responsiveness in Chatbots

in Avec le Temps! Time, Tempo, and Turns in Human-Computer Interaction". Workshop at CHI 2013, Paris, France (2013)

In this position paper we consider temporal phenomena in interaction with text-based conversational agents. In particular, we focus on two dimensions of time in instant messaging dialogues: responsiveness ... [more ▼]

In this position paper we consider temporal phenomena in interaction with text-based conversational agents. In particular, we focus on two dimensions of time in instant messaging dialogues: responsiveness as a measure for interaction placed in time, and interaction management performed by interaction participants caused by partner's exceeding of the maximum expected responsiveness. [less ▲]

News and stock markets: A survey on abnormal returns and prediction models

Report (2012)

Vast amount of news articles are published daily reflecting global topics. The stories represent information about events and expert opinions, which may trigger positive or negative expectations on the ... [more ▼]

Vast amount of news articles are published daily reflecting global topics. The stories represent information about events and expert opinions, which may trigger positive or negative expectations on the stock markets. The literature describes various methods for analyzing such correlations. In this paper we consider related approaches for tracking the impact of news on abnormal stock returns. In the first part we introduce studies with back- ground in Finance. Primarily by applying statistical functions the works examine unusual price volatilities and explore possible sources and market conditions, e.g. biased investors, limited attention, macro-economic variables, country development state, et cetera. In the second part we present studies with background in Computer Science, which take advan- tage of historic news and the equivalent market values. By following the common learning paradigm the projects elaborate prototypes for trend and stock price prediction. In the current survey we evaluate leading approaches regarding the objectives, assumptions, in- put, techniques, and performance. Moreover we provide a comparison framework of the recent prototypes and identify gaps for future research. [less ▲]