Context awareness is the key to any active assistance system. The Human Behavior Monitoring and Support project (HBMS) applies a multilevel context modeling approach, aiming to achieve context readability, reuse, adaptability and interoperability. The HBMS-System is the resulting active assistance system, which is multiply deployable in different domains to support the behavior of users in situ...

This paper focuses on modeling users’ cognitive style based on a set of Web usage mining techniques on navigation patterns and clickstream data. Main aim is to investigate whether k-means clustering can group users of particular cognitive style using measures obtained from a series of psychometric tests and content navigation behavior. Three navigation metrics are proposed and used to find iden...

Analogical modeling is a supervised exemplar-based approach that has been widely applied to predict linguistic behavior. The paradigm has been well documented in the linguistics and cognition literature, but is less well known to the machine learning community. This paper sets out some of the basics of the approach, including a simplified example of the fundamental algorithm’s operation. It the...

the present study seeks to investigate users’ perception and evaluation of the function of designed environments and highlights a deeper understanding of man-environment relationship. the study tries to answer the question: “is there any relationship between the physical features of environment and users’ perception and evaluation of the environment and their spatial behaviors?” such investigat...

Existing context-aware adaptation techniques are limited in their support for user personalization. There is relatively less developed research involving adaptive user modeling for user applications in the emerging areas of mobile and pervasive computing. This paper describes the creation of a User Profile Ontology for context-aware application personalization within mobile environments. We ana...

A lot of recent research has focused on methods of modeling Web users and on efficient ways to initialize and manage user models. In this paper, we present a new user modeling technique relying on a temporal, graph-based data model for semistructured information. Our technique can be used to filter WWW information and to make the Web experience personalized for the individual user. Moreover, su...

This paper describes an algorithm to cluster and segment sequences of low-level user actions into sequences of distinct high-level user tasks. The algorithm uses text contained in interface windows as evidence of the state of user–computer interaction. Window text is summarized using latent semantic indexing (LSI). Hierarchical models are built using expectation–maximization to represent users ...

Existing works on user behavior analysis mainly focus on modeling a single behavior and predicting whether a user will take an action or not. However, users’ behaviors do not always happen in isolation, sometimes, different behaviors may happen simultaneously. Therefore, in this paper, we try to analyze the combination of basic behaviors, called behavioral state here, which can describes users’...

As massive MIMO is currently considered a leading 5G technology candidate, channel models that capture important massive MIMO channel characteristics are urgently needed. In this paper we present an attempt for massive MIMO channel modeling based on measurement campaigns at 2.6 GHz in both outdoor and indoor environments, using physically-large arrays and with closelyspaced users. The COST 2100...