Vesa Kyllönen

Unobtrusive Dynamic Modelling of TV Program Preferences in a Household

Majority of recommender systems require explicit user interaction (ranking of movies and TV programs and/or their metadata, such as genres, actors etc), which requires user time and effort. Furthermore, often such ranking is done separately by each person, while merging these manually acquired preferences in multi-user environments remains largely unsolved problem. This work presents a method to learn a model of multi-user environment in intelligent home from implicit interactions: the choices which family members make together and separately. In tests on TV viewing histories of twenty families, acquired during two months, the method has achieved prediction accuracy comparable with the accuracy of systems which require explicit user ratings: a set of TV programs, actually viewed during each test session (average set size was 2.2 programs per viewing session), was recommended among five top choices in 60% of cases on average, despite training on small data sets.

Increasing Security of Mobile Devices by Decreasing User Effort in Verification

Reliable user verification is important for security of computers and personal devices; however, most of well-performing verification methods require explicit user effort. As a consequence, an access is granted for a long time after the only successful verification, which allows replacing the authorized user to the advantage of an impostor, as it is often the case with mobile phones. This work proposes a method of frequent user verification, based on cascading of unobtrusive biometrics with more reliable biometrics, provided explicitly, in such a way that explicit effort is required only if unobtrusive verification fails. Experiments with voice, gait and fingerprint data have shown that in most of noise conditions cascade was able to satisfy security requirements of False Accept Rate 1% and to achieve overall False Reject Rate 3% or less, while requiring explicit effort in 10 - 60% of cases.

Context-Dependent User Modelling for Smart Homes

This works presents a user modelling service for a Smart Home --- intelligent context-aware environment, providing personalized proactive support to its inhabitants. Diversity of Smart Home applications imposes various technical and implementation requirements, such as the need to model dependency of user preferences on context in a unified and convenient way, both for users and for application developers. This paper introduces the service architecture and currently implemented functionalities: stereotypes-based profiles initialisation; a GUI for acquisition of context-dependent and context-independent preferences, which provides an easy way to create own concepts of context ontology and to map them into already existing concepts; and a method to learn context-dependent user preferences from interaction history.

Mobility Trigger Management: Implementation and Evaluation

Modern mobile devices have several network interfaces and can run various network applications. In order to remain always best connected, events need to be communicated through the entire protocol stack in an efficient manner. Current implementations can handle only a handful of low level events that may trigger actions for mobility management, such as signal strength indicators and cell load. In this paper, we present a framework for managing mobility triggers that can deal with a greater variety of triggering events, which may originate from any component of the node’s protocol stack as well as mobility management entities within the network. We explain the main concepts that govern our trigger management framework and discuss its architecture which aims at operating in a richer mobility management framework, enabling the deployment of new applications and services. We address several implementation issues, such as, event collection and processing, storage, and trigger dissemination, and introduce a real implementation for commodity mobile devices. We review our testbed environment and provide experimental results showcasing a lossless streaming video session handover between a laptop and a PDA using mobility and sensor-driven orientation triggers. Moreover, we empirically evaluate and analyze the performance of our prototype. We position our work and implementation within the Ambient Networks architecture and common prototype, centring in particular on the use of policies to steer operation. Finally, we outline current and future work items.

Paving the way for future mobility mechanisms: a testbed for triggering & moving network support

Forthcoming wireless communication systems, well represented by the term beyond 3G, are likely to impose some new requirements that go beyond the traditional view on todays networking paradigm. In particular, mobility procedures will no longer be restricted to the change of the point of attachment to the network. The work presented in this paper aims at proving, following a fully experimental approach, the feasibility of some architectural components of a mobility control space, which has been designed so the context of the ambient networks project. Especially, in this study we focused on and successfully realized two concepts, a facility for triggering mobility events and support for moving networks

Wearable Context Aware Terminal for Maintenance Personnel

A wearable context aware terminal with net connection and spoken command input is presented. The context aware terminal can be used, for example, by janitors or other maintenance personnel for retrieving and logging information related to a location, such as a room. The main context cues used are user’s identity and location. The user is identified by biometrics which is also used for preventing unauthorized usage of the terminal and information accessible through the terminal. Location information is acquired by using signal strength information of existing Wireless Local Area Network (WLAN) infrastructure. Since the wearable terminal is envisaged to be used by maintenance personnel it was seen important to offer possibility to hands-free operation by using spoken commands. Tentative experiments show that this approach might be useful for maintenance personnel.

Prediction of interface preferences with a classifier selection approach

Interaction in smart environments should be adapted to the users’ preferences, e.g., utilising modalities appropriate for the situation. While manual customisation of a single application could be feasible, this approach would require too much user effort in the future, when a user interacts with numerous applications with different interfaces, such as e.g. a smart car, a smart fridge, a smart shopping assistant etc. Supporting user groups, jointly interacting with the same application, poses additional challenges: humans tend to respect the preferences of their friends and family members, and thus the preferred interface settings may depend on all group members. This work proposes to decrease the manual customisation effort by addressing the cold-start adaptation problem, i.e., predicting interface preferences of individuals and groups for new (unseen) combinations of applications, tasks and devices, based on knowledge regarding preferences of other users. For predictions we suggest several reasoning strategies and employ a classifier selection approach for automatically choosing the most appropriate strategy for each interface feature in each new situation. The proposed approach is suitable for cases where long interaction histories are not yet available, and it is not restricted to similar interfaces and application domains, as we demonstrate by experiments on predicting preferences of individuals and groups for three different application prototypes: recipe recommender, cooking assistant and car servicing assistant. The results show that the proposed method handles the cold-start problem in various types of unseen situations fairly well: it achieved an average prediction accuracy of

Unsupervised Stress Detection Algorithm and Experiments with Real Life Data

72 \pm 1\,\%