UbiOpticon : participatory sousveillance with urban screens and mobile phone cameras

In many cities around the world, surveillance by a pervasive net of CCTV cameras is a common phenomenon in an attempt to uphold safety and security across the urban environment. Video footage is being recorded and stored, sometimes live feeds are being watched in control rooms hidden from public access and view. In this study, we were inspired by Steve Mann’s original work on sousveillance (surveillance from below) to examine how a network of camera equipped urban screens could allow the residents of Oulu in Finland to collaborate on the safekeeping of their city. An agile, rapid prototyping process led to the design, implementation and ‘in the wild’ deployment of the UbiOpticon screen application. Live video streams captured by web cams integrated at the top of 12 distributed urban screens were broadcast and displayed in a matrix arrangement on all screens. The matrix also included live video streams of two roaming mobile phone cameras. In our field study we explored the reactions of passers-by and users of this screen application that seeks to inverse Bentham’s original panopticon by allowing the watched to be watchers at the same time. In addition to the original goal of participatory sousveillance, the system’s live video feature sparked fun and novel user-led apprlopriations.

Vote with your feet : local community polling on urban screens

Falling prices have led to an ongoing spread of public displays in urban areas. Still, they mostly show passive content such as commercials and digital signage. At the same time, technological advances have enabled the creation of interactive displays potentially increasing their attractiveness for the audience, e.g. through providing a platform for civic discourse. This poses considerable challenges, since displays need to communicate the opportunity to engage, motivate the audience to do so, and be easy to use. In this paper we present Vote With Your Feet, a hyperlocal public polling tool for urban screens allowing users to express their opinions. Similar to vox populi interviews on TV or polls on news websites, the tool is meant to reflect the mindset of the community on topics such as current affairs, cultural identity and local matters. It is novel in that it focuses on a situated civic discourse and provides a tangible user interface, tackling the mentioned challenges. It shows one Yes/No question at a time and enables users to vote by stepping on one of two tangible buttons on the ground. This user interface was introduced to attract people’s attention and to lower participation barriers. Our field study showed that Vote With Your Feet is perceived as inviting and that it can spark discussions among co-located people.

CoRE : a context-aware relation extraction method for relation completion

We identify relation completion (RC) as one recurring problem that is central to the success of novel big data applications such as Entity Reconstruction and Data Enrichment. Given a semantic relation, RC attempts at linking entity pairs between two entity lists under the relation. To accomplish the RC goals, we propose to formulate search queries for each query entity α based on some auxiliary information, so that to detect its target entity β from the set of retrieved documents. For instance, a pattern-based method (PaRE) uses extracted patterns as the auxiliary information in formulating search queries. However, high-quality patterns may decrease the probability of finding suitable target entities. As an alternative, we propose CoRE method that uses context terms learned surrounding the expression of a relation as the auxiliary information in formulating queries. The experimental results based on several real-world web data collections demonstrate that CoRE reaches a much higher accuracy than PaRE for the purpose of RC.

Dictionary learning and sparse coding on Grassmann manifolds : an extrinsic solution

Recent advances in computer vision and machine learning suggest that a wide range of problems can be addressed more appropriately by considering non-Euclidean geometry. In this paper we explore sparse dictionary learning over the space of linear subspaces, which form Riemannian structures known as Grassmann manifolds. To this end, we propose to embed Grassmann manifolds into the space of symmetric matrices by an isometric mapping, which enables us to devise a closed-form solution for updating a Grassmann dictionary, atom by atom. Furthermore, to handle non-linearity in data, we propose a kernelised version of the dictionary learning algorithm. Experiments on several classification tasks (face recognition, action recognition, dynamic texture classification) show that the proposed approach achieves considerable improvements in discrimination accuracy, in comparison to state-of-the-art methods such as kernelised Affine Hull Method and graph-embedding Grassmann discriminant analysis.

A new distributed control strategy to coordinate multiple dstatcoms in LV network

Voltage rise is the main issue which limits the capacity of Low Voltage (LV) network to accommodate more Renewable Energy (RE) sources. In addition, voltage drop at peak load period is a significant power quality concern. This paper proposes a new robust voltage support strategy based on distributed coordination of multiple distribution static synchronous compensators (DSTATCOMs). The study focuses on LV networks with PV as the RE source for customers. The proposed approach applied to a typical LV network and its advantages are shown comparing with other voltage control strategies.

Invisible connections : investigating older people's emotions and social relations around objects

The advent of the Internet of Things creates an interest in how people might interrelate through and with networks of internet enabled objects. With an emphasis on fostering social connection and physical activity among older people, this preliminary study investigated objects that people over the age of 65 years viewed as significant to them. We conducted contextual interviews in people's homes about their significant objects in order to understand the role of the objects in their lives, the extent to which they fostered emotional and social connections and physical activity, and how they might be augmented through internet connection. Discussion of significant objects generated considerable emotion in the participants. We identified objects of comfort and routine, objects that exhibited status, those that fostered independence and connection, and those that symbolized relationships with loved ones. These findings lead us to consider implications for the design of interconnected objects.

Towards the development of 1-to-n human machine interfaces for unmanned aerial vehicles

Multi-touch interfaces across a wide range of hardware platforms are becoming pervasive. This is due to the adoption of smart phones and tablets in both the consumer and corporate market place. This paper proposes a human-machine interface to interact with unmanned aerial systems based on the philosophy of multi-touch hardware-independent high-level interaction with multiple systems simultaneously. Our approach incorporates emerging development methods for multi-touch interfaces on mobile platforms. A framework is defined for supporting multiple protocols. An open source solution is presented that demonstrates: architecture supporting different communications hardware; an extensible approach for supporting multiple protocols; and the ability to monitor and interact with multiple UAVs from multiple clients simultaneously. Validation tests were conducted to assess the performance, scalability and impact on packet latency under different client configurations.

Classifying natural aerial scenery for autonomous aircraft emergency landing

In this paper, we present an approach for image-based surface classification using multi-class Support Vector Machine (SVM). Classifying surfaces in aerial images is an important step towards an increased aircraft autonomy in emergency landing situations. We design a one-vs-all SVM classifier and conduct experiments on five data sets. Results demonstrate consistent overall performance figures over 88% and approximately 8% more accurate to those published on multi-class SVM on the KTH TIPS data set. We also show per-class performance values by using normalised confusion matrices. Our approach is designed to be executed online using a minimum set of feature attributes representing a feasible and ready-to-deploy system for onboard execution.

Enabling safe autonomous driving in real-world city traffic using multiple criteria decision making

This paper addresses the topic of real-time decision making for autonomous city vehicles, i.e., the autonomous vehicles' ability to make appropriate driving decisions in city road traffic situations. The paper explains the overall controls system architecture, the decision making task decomposition, and focuses on how Multiple Criteria Decision Making (MCDM) is used in the process of selecting the most appropriate driving maneuver from the set of feasible ones. Experimental tests show that MCDM is suitable for this new application area.

Bluetooth vehicle trajectory by fusing Bluetooth and loops : motorway travel time statistics

Loop detectors are widely used on the motorway networks where they provide point speed and traffic volumes. Models have been proposed for temporal and spatial generalization of speed for average travel time estimation. Advancement in technology provides complementary data sources such as Bluetooth MAC Scanner (BMS), detecting the MAC ID of the Bluetooth devices transported by the traveller. Matching the data from two BMS stations provides individual vehicle travel time. Generally, on the motorways loops are closely spaced, whereas BMS are placed few kilometres apart. In this research, we fuse BMSs and loops data to define the trajectories of the Bluetooth vehicles. The trajectories are utilised to estimate the travel time statistics between any two points along the motorway. The proposed model is tested using simulation and validated with real data from Pacific motorway, Brisbane. Comparing the model with the linear interpolation based trajectory provides significant improvements.

An object-oriented design of a World Model for autonomous city vehicles

This paper presents an object-oriented world model for the road traffic environment of autonomous (driver-less) city vehicles. The developed World Model is a software component of the autonomous vehicle's control system, which represents the vehicle's view of its road environment. Regardless whether the information is a priori known, obtained through on-board sensors, or through communication, the World Model stores and updates information in real-time, notifies the decision making subsystem about relevant events, and provides access to its stored information. The design is based on software design patterns, and its application programming interface provides both asynchronous and synchronous access to its information. Experimental results of both a 3D simulation and real-world experiments show that the approach is applicable and real-time capable.

eHealth-as-a-Service (eHaaS) : towards universal stakeholder engagement

With the introduction of the Personally Controlled Health Record (PCEHR), the Australian public is being asked to accept greater responsibility for their healthcare by taking an active role in the management of personal health information. Although well designed, constructed and intentioned, policy and privacy concerns have resulted in an eHealth model that may impact future health sharing requirements. Hence, as a case study for a consumer eHealth initative in the Australian context, eHealth-as-a-Service (eHaaS) serves as a disruptive step in in the aggregation and transformation of health information for use as real-world knowledge. The strategic value of extending the community Health Record Bank (HRB) model lies in the ability to automatically draw on a multitude of relevant data repositories and sources to create a single source of the truth and to engage market forces to create financial sustainability. The opportunity to transform the beleaguered Australian PCEHR into a realisable and sustainable technology consumption model for patient safety is explored. Moreover, the current clerical focus of healthcare practitioners acting in the role of de facto record keepers is renegotiated to establish a shared knowledge creation landscape of action for safer patient interventions. To achieve this potential however requires a platform that will facilitate efficient and trusted unification of all health information available in real-time across the continuum of care. eHaaS provides a sustainable environment and encouragement to realise this potential.

A taxonomy of computation offloading in mobile cloud computing

The ability of cloud computing to provide almost unlimited storage, backup and recovery, and quick deployment contributes to its widespread attention and implementation. Cloud computing has also become an attractive choice for mobile users as well. Due to limited features of mobile devices such as power scarcity and inability to cater computationintensive tasks, selected computation needs to be outsourced to the resourceful cloud servers. However, there are many challenges which need to be addressed in computation offloading for mobile cloud computing such as communication cost, connectivity maintenance and incurred latency. This paper presents taxonomy of the computation offloading approaches which aim to address the challenges. The taxonomy provides guidelines to identify research scopes in computation offloading for mobile cloud computing. We also outline directions and anticipated trends for future research.

Detection of object onset and offset in naturalistic scenes

The present study was conducted to investigate whether ob- servers are equally prone to overlook any kinds of visual events in change blindness. Capitalizing on the finding from visual search studies that abrupt appearance of an object effectively captures observers' attention, the onset of a new object and the offset of an existing object were contrasted regarding their detectability when they occurred in a naturalistic scene. In an experiment, participants viewed a series of photograph pairs in which layouts of seven or eight objects were depicted. One object either appeared in or disappeared from the layout, and participants tried to detect this change. Results showed that onsets were detected more quickly than offsets, while they were detected with equivalent ac- curacy. This suggests that the primacy of onset over offset is a robust phenomenon that likely makes onsets more resistant to change blindness under natural viewing conditions.

Universal codes from switching strategies

We discuss algorithms for combining sequential prediction strategies, a task which can be viewed as a natural generalisation of the concept of universal coding. We describe a graphical language based on Hidden Markov Models for defining prediction strategies, and we provide both existing and new models as examples. The models include efficient, parameterless models for switching between the input strategies over time, including a model for the case where switches tend to occur in clusters, and finally a new model for the scenario where the prediction strategies have a known relationship, and where jumps are typically between strongly related ones. This last model is relevant for coding time series data where parameter drift is expected. As theoretical contributions we introduce an interpolation construction that is useful in the development and analysis of new algorithms, and we establish a new sophisticated lemma for analysing the individual sequence regret of parameterised models.