Fernando Koch

A crowdsourcing platform for the construction of accessibility maps

We present in this article a crowdsourcing platform that enables the collaborative creation of accessibility maps. The platform provides means for integration of different kind of data, collected automatically or with user intervention, to augment standard maps with accessibility information. The article shows the architecture of the platform, dedicating special attention to the smartphone applications we developed for data collection. The article also describes a preliminar experiment conducted on field, showing how the analysis of data produced by our solution can bring novel insights in accessibility challenges that can be found in cities.

Context-Aware Job Scheduling for Cloud Computing Environments

The more instrumented society is demanding smarter services to help coordinate daily activities and exceptional situations. Applications become sophisticated and context-aware as the pervasiveness of technology increases. In order to cope with resource limitations of mobile-based environments, it is a common practice to delegate processing intensive components to a Cloud Computing infrastructure. In this scenario, executions of server-based jobs are still dependent on the local variations of the end-user context. We claim that there is a need for an advanced model for smarter services that combines techniques of context awareness and adaptive job scheduling. This model aims at rationalising the resource utilisation in a Cloud Computing environment, while leading to significant improvement of quality of service. In this paper, we introduce such a model and describe its performance benefits through a combination of social and service simulations. We analyse the results by demonstrating gains in performance, quality of service, reduction of wasted jobs, and improvement of overall end-user experience.

Optimising resource costs of cloud computing for education

There is a growing interest around the utilisation of cloud computing in education. As organisations involved in the area typically face severe budget restrictions, there is a need for cost optimisation mechanisms that explore unique features of digital learning environments. In this work, we introduce a method based on Maximum Likelihood Estimation that considers heterogeneity of IT infrastructure in order to devise resource allocation plans that maximise platform utilisation for educational environments. We performed experiments using modelled datasets from real digital teaching solutions and obtained cost reductions of up to 30%, compared with conservative resource allocation strategies. Context-aware algorithm for allocating computing resources for class- rooms.Experiment setup based on real-world school data.Evaluation analysis considering security margin, costs, and QoS.

A Platform for Citizen Sensing in Sentient Cities

This work develops upon the concepts of Sentient City – living in a city that can remember, correlate, and anticipate – and Citizen Sensor Networks. We aim at technologies to interconnect people, allowing them to actively observe, report, collect, analyse, and disseminate information about urban events. We are investigating new methods and technologies to enhance administrators’ capabilities in urban planning and management. We are proposing a platform to instrument citizens and cities, interconnect parties, analyse related events, and provide recommendation and feedback reports. The solution encompasses four types of elements: (i) mobile applications for intentional and non-intentional reporting of events; (ii) enhanced analytic models to centralize information, analyse the data, identify trends and operation patterns, and provide insightful information to decision makers; (iii) advanced social simulations to anticipate “what if” scenarios for infrastructure planning; and (iv) interfaces for monitoring, feedback, and recommendation. This research builds upon the IBM Smarter Cities project, part of the IBM Smarter Planet program. The outcomes of this research yield significant social contributions. By using it, administrators can make reliable decisions that will impact social services, traffic, energy and utilities, public safety, retail, communications, and economic development.

A Wearable Face Recognition System Built into a Smartwatch and the Blind and Low Vision Users

Assistive technologies need to be affordable, ergonomic and easy to use. In this work we argue that smartwatches could be assistive devices for the visually impaired, if they have the potential to run complex applications. Hence, in this paper we propose a face recognition system to show that it’s technically possible to develop a real-time computer vision system in a wearable device with limited hardware, since such systems generally require powerful hardware. A case study is presented using the first generation Samsung Galaxy Gear smartwatch. The system runs only on the watch’s hardware and consists in a face detection and recognition software that emits an audio feedback so that visually impaired users know who is around them. The case study includes an evaluation of the proposal with users. Results are shown and discussed validating the technological aspects of the proposal and pointing out room for improving the aspects of interaction.

Recognition and Recommendation of Parking Places

Current solutions to recommend available parking spaces rely on options like: intentional user feedback; installing data collectors in volunteering fleet vehicles, or; installing static sensors to monitor available parking spaces. In this paper we propose a solution based application that runs on commodity smartphones and makes use of the advanced sensor capabilities in these devices, along with methods of statistical analysis of the collected sensor data to provide useful recommendations. We exploit a combination of \(k\)-medoid clustering and Conditional Random Fields to reliably detect a user parking with a limited sensor capability. Next, we outline a method based on Markov Chains to calculate the probability of finding a parking space near a given location. We also enhance the solution with more sensor capability to discover desirable properties in parking spaces.

CanIHelp: A Platform for Inclusive Collaboration

Technology plays a key role in daily life of people with special needs, being a mean of integration or even communication with society. By built up experience, we find that support tools play a crucial part in empowerment of persons with special needs and small advances may represent shifts and opportunities. The diversity of solutions and the need for dedicated hardware to each feature represents a barrier to its use, compromising the success of the solutions against, among others, problems of usability and scale. This paper aims to explore the concept of inclusive collaboration to enhance the mutual interaction and assistance. The proposed approach combines and generalizes the usage of human computation in a collaborative environment with assistive technologies creating redundancy and complementarity in the solutions provided, contributing to enhance the quality of life of people with special needs and the elderly. The CanIHelp platform is an embodiment of the concept as a result from an orchestrated model using mechanisms of collective intelligence through social inclusion initiatives. The platform features up for integrating assistive technologies, collaborative tools and multiple multimedia communication channels, accessible through multimodal interfaces for universal access. A discussion of the impacts of fostering collaboration and broadening from the research concepts to the societal impacts is presented. As final remarks a set of future research challenges and guidelines are identified.

Towards an Educator-Centred Digital Teaching Platform: The Ground Conditions for a Data-Driven Approach

We introduce innovations in a Digital Teaching Platform (DTP) through tools centred on supporting the teacher. We focus on the utilisation of data about the students and the class in order to recommend actions and content for the teacher. For this, we need a platform with novel capabilities. First, we augment the content delivery application with data collecting capabilities. Second, we create a cloud-based analytics engine that infers student profiles and context parameters from multi-modal sources. Third, we provide a web-based platform for content composition that makes use of the inferred student and context profiles to support teachers in lesson planning. Our solution implements the complete cycle from content composition to delivery and adjustment, allowing for the research and development of new features and intelligences in Digital Education.

A Quantitative Analysis of Learning Objects and Their Metadata in Web Repositories

This work conducts a quantitative analysis of a number of Learning Object Repositories (LORs) of Learning Objects (LOs) in both English and Portuguese languages. The focus of this exercise is to understand how the contributors organize their metadata, the update frequency, and measurement upon LOR items such as: (i) the size distribution; (ii) growth rate, and; (iii) statistics about metadata completion, blank fields and LO types. We conclude our analysis with a discussion about the implications of our findings upon tasks such as LO search and recommendation.

A Wearable Face Recognition System Built into a Smartwatch and the Visually Impaired User

Practitioners usually expect that real-time computer vision systems such as face recognition systems will require hardware components with high processing power. In this paper, we present a concept to show that it is technically possible to develop a simple real-time face recognition system in a wearable device with low processing power ??? in this case an assistive device for the visually impaired. Our platform of choice here is the first generation Samsung Galaxy Gear smartwatch. Running solely in the watch, without pairing to a phone or tablet, the system detects a face in the image captured by the camera, and then performs face recognition (on a limited dictionary), emitting an audio feedback that either identifies the recognized person or indicates that s/he is unknown. For the face recognition approach we use a variation of the K-NN algorithm which accomplished the task with high accuracy rates. This paper presents the proposed system and preliminary results on its evaluation.