In this chapter we discuss the role of context in organizing episodic memories. We define context as slowly drifting information (i.e. information that persists over a relatively long time scale in the person's brain; for example, a representation of the person's location). First, we use the Temporal Context Model of memory search (Howard & Kahana, 2002) to illustrate how binding slowly-driftin...

Methods for generating context-aware recommendations were classified into the pre-filtering, post-filtering and contextual modeling approaches. This paper proposes a novel type of contextual modeling (CM) based on the contextual neighbors approach and introduces four specific contextual neighbors methods. It compares these four types of contextual neighbors techniques to determine the best-perf...

Search Engine Advertising has grown strongly in recent years and amounted to about USD 60 billion in 2014. Based on real-world data of online campaigns of 28 companies, we analyse the incident of a hacked campaignaccount. We describe the occurred damage, i.e. (1) follow-up consequences of unauthorized access to the account of the advertiser, and (2) limited availability of short-term online cam...

News is increasingly being accessed on smartphones and tablets, establishing mobile news reading as one of the most popular activities on mobile devices. News reading is also a very individual activity with marked differences in the way people read and access the news, however, news apps have limited personalization. In this paper, we approach news personalization as a two-dimensional problem. ...

Affinity diagramming is a commonly used contextual design practice in the development of interactive systems. Currently many tools exist that attempt to support the process of designing. However, experts and novices alike eschew tool use, instead preferring traditional paper and whiteboard methods. In this paper, we explore the potential of using Multiple Display Environments (MDEs) for the pro...

A precondition of existing ensemble-based distributed data mining techniques is the assumption that contributing data are identically and independently distributed. However, this assumption is not valid in many virtual organization contexts because contextual heterogeneity exists. Focusing on regression tasks, this paper proposes a contextbased meta-learning technique for horizontally partition...

Although learning with Intelligent Tutoring Systems (ITS) has been well studied, little research has investigated what role teachers can play, if empowered with data. Many ITSs provide student performance reports, but they may not be designed to serve teachers’ needs well, which is important for a well-designed dashboard. We investigated what student data is most helpful to teachers and how the...

In this work, we compared emotions induced by the same performance of Schubert Lieder during a live concert and in a laboratory viewing/listening setting to determine the extent to which laboratory research on affective reactions to music approximates real listening conditions in dedicated performances. We measured emotions experienced by volunteer members of an audience that attended a Lieder ...

This paper reports on the results of a series of experiments that put Escudero’s theoretical model[4] to a practical test. This model was developed to explain how Dutch learners of Spanish vowels solve the SUBSET problem. Escuderos model uses Stochastic Optimality Theory and, in this paper, we use this same framework with the Gradual Learning Algorithm to simulate learning on the basis of Escud...

Defense and security surveillance scenarios typically involve the detection and classification of targets in complex and dynamic backgrounds. Imaging systems deployed for this purpose should therefore provide imagery that enables optimal simultaneous recognition of both targets and their context. Here we investigate the recognition of semi-hidden targets, which are targets that are embedded in ...