Zack Zhu

Human activity recognition is a core component of context-aware, ubiquitous computing systems. Traditionally, this task is accomplished by analyzing signals of wearable motion sensors. While such signals can effectively distinguish various low-level activities (e.g. walking or standing), two issues exist: First, high-level activities (e.g. watching...

Automatic recognition of user context is essential for a variety of emerging applications, such as context-dependent content delivery, telemonitoring of medical patients, or quantified life-logging. Although not explicitly observable as, e.g., activities, an important aspect towards understanding user context lies in the affective state of mood.Whi...

Regions in urban environments often afford a mixture of different utilities. Their identification allows urban planners to leverage important insights on the emerging functional dynamics of cities. With the increasing availability of human mobility data and other forms of online digital breadcrumbs, we can now characterize urban regions with multi-...

Affordances from the urban space shape the way we interact with our environment, whether manifested as driving into the city centre for work or playing sports in designated arenas. Given today's abundance of crowd-generated digital traces on location-based social network (LBSN) platforms, an opportunity arises to grasp deeper semantic characterizat...

Today, YouTube is the largest user-driven video con-tent provider in the world; it has become a major plat-form for disseminating multimedia information. A ma-jor contribution to its success comes from the user-to-user social experience that differentiates it from tradi-tional content broadcasters. This work examines the so-cial network aspect of Y...

The preservation of geo-privacy is a critical consideration for location-based service (LBS) providers. Unfortunately, a trade-off typically exists between the quality of location-based services and revealing of private information (e.g. geo-coordinates) to obtain such services. In this work, we develop semantic obfuscation methods, which allow a t...

Human activity recognition is a core component of context-aware, ubiquitous computing systems. Traditionally, this task is accomplished by analysing signals of wearable motion sensors. While successful for low-level activities (e.g. walking or standing), high-level activities (e.g. watching movies or attending lectures) are difficult to distinguish...

Automatically recognizing people's daily activities is essential for a variety of applications, such as just-in-time content delivery or quantified self-tracking. Towards this, researchers often use customized wearable motion sensors tailored to recognize a small set of handpicked activities in controlled environments. In this paper, we design and...

We explore the feasibility of utilizing large, crowd-generated online repositories to construct prior knowledge models for high-level activity recognition. Towards this, we mine the popular location-based social network, Foursquare, for geo-tagged activity reports. Although unstructured and noisy, we are able to extract, categorize and geographical...

Freezing of gait (FoG) is a common gait deficit in advanced Parkinson's disease (PD). FoG events are associated with falls, interfere with daily life activities and impair quality of life. FoG is often resistant to pharmacologic treatment; therefore effective non-pharmacologic assistance is needed. We propose a wearable assistant, composed of a sma...

Many problems across various domains of research may be formulated as a multi-objective optimization problem. The Multi-objective Evolutionary Algorithm framework (MOEA) has been applied successfully to unconstrained multi-objective optimization problems. This work adapts the modified Hypervolume Indicator to incorporate constraints when used withi...

Experimental issues arise when scientists attempt to directly study emergent behaviour brought on by the evolutionary process. Recently, algorithms that simulate artificial evolution in robotic societies have been used to circumvent such issues. This study attempts to investigate and interpret emergent signals used by artificial agents when evolved...