Carlos Henrique Cardonha

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.

Evaluating Auto-scaling Strategies for Cloud Computing Environments

Auto-scaling is a key feature in clouds responsible for adjusting the number of available resources to meet service demand. Resource pool modifications are necessary to keep performance indicators, such as utilisation level, between user-defined lower and upper bounds. Auto-scaling strategies that are not properly configured according to user workload characteristics may lead to unacceptable QoS and large resource waste. As a consequence, there is a need for a deeper understanding of auto-scaling strategies and how they should be configured to minimise these problems. In this work, we evaluate various auto-scaling strategies using log traces from a production Google data centre cluster comprising millions of jobs. Using utilisation level as performance indicator, our results show that proper management of auto-scaling parameters reduces the difference between the target utilisation interval and the actual values-we define such difference as Auto-scaling Demand Index. We also present a set of lessons from this study to help cloud providers build recommender systems for auto-scaling operations.

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.

An architecture and algorithm for context-aware resource allocation for digital teaching platforms

Digital Teaching Platforms (DTPs) are aimed to support personalization of classroom education to help optimize the learning process. A trend for research and development exists regarding methods to analyze multimodal data, aiming to infer how students interact with delivered content and understanding student behavior, academic performance, and the way teachers react to student engagement. Existing DTPs can deliver several types of insights, some of which teachers can use to adjust learning activities in real-time. These technologies require a computing infrastructure capable of collecting and analyzing large volumes of data, and, for this, cloud computing is an ideal candidate solution. Nonetheless, preliminary field tests with DTPs demonstrate that applying fully remote services is prohibitive in scenarios with limited bandwidth and a constrained communication infrastructure. Therefore, we propose an architecture for DTPs and an algorithm to promote the adjustable balance between local and federated cloud resources. The solution works by deciding where tasks should be executed, based on resource availability and the quality of insights they may provide to teachers during learning sessions. In this work, we detail the system architecture, describe a proof-of-concept, and discuss the viability of the proposed approach for practical scenarios.

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.

Taxonomy of Citizen Sensing for Intelligent Urban Infrastructures

Citizen sensing is a new sensor-based data collection paradigm and is focused on the extraction of data generated by people. Initiatives based on this concept are becoming crucial for designers of intelligent urban infrastructures, since they enable the collection of several types of relevant data that cannot be properly captured by traditional physical sensors. A large number of articles and projects associated with the topic appeared over the last few years, and with them the need for properly classifying and organizing these works. In this paper, we propose a taxonomy of citizen sensing initiatives and illustrate each of its dimensions through a survey of recent articles in the area. The proposed scheme also supports the identification and stimulates the development of projects addressing data collection methodologies that have not been properly explored so far. In addition, we present a platform capable of aggregating, analyzing, and extracting knowledge from data generated by physical and human sensing techniques. Finally, we report a real-world experiment in which we used our platform to map accessibility conditions of streets and sidewalks located in a four square kilometer area in São Paulo, Brazil. Our results show that a full coverage was obtained with the support of eight volunteers after only three hours, hence illustrating the effectiveness of the technology.

Exploiting User Patience for Scaling Resource Capacity in Cloud Services

An important feature of cloud computing is its elasticity, that is, the ability to have resource capacity dynamically modified according to the current system load. Auto-scaling is challenging because it must account for two conflicting objectives: minimising system capacity available to users and maximising QoS, which typically translates to short response times. Current auto-scaling techniques are based solely on load forecasts and ignore the perception that users have from cloud services. As a consequence, providers tend to provision a volume of resources that is significantly larger than necessary to keep users satisfied. In this article, we propose a scheduling algorithm and an auto-scaling triggering technique that explore user patience in order to identify critical times when auto-scaling is needed and the appropriate volume of capacity by which the cloud platform should either extend or shrink. The proposed technique assists service providers in reducing costs related to resource allocation while keeping the same QoS to users. Our experiments show that it is possible to reduce resource-hour by up to approximately 8% compared to auto-scaling based on system utilisation.

Smarter board: a community-oriented communication tool

In this demo we present the Smarter Board, a platform designed to facilitate the creation of a community-focused social network, with a special focus on groups of people with disabilities. The communication is based on text messages, which makes the system easy to use and more accessible to communities where network connections are not well-developed and where the people do not have much experience with more advanced technological tools. The solution also provides a manage interface, through which administrators are able to mediate the messages and the users. We also implemented a matching procedure for the identification of related posts (e.g., it can check if there are compatible car ride offers and car ride requests) in order to make users aware of what is being posted and, even more important, adopt the technology.

Marker-assisted recognition of dynamic content in public spaces

In this work we present an image processing-based assistant for helping visually impaired citizens with the task of recognizing dynamic content within fixed layouts of displays in public spaces. Our solution relies on the placement of markers, in order to facilitate the location and recognition of target objects and, at the same time, provide hints to users about how to better position their mobile devices cameras to capture the whole information contained in the display.

A platform to support personalized training of people with disabilities

Digital education has potential to provide different possibilities for personalization and consequently reach a larger and more diverse number of people. Personalization is a key component of solutions addressing important and long-standing pedagogical challenges in education, such as dealing with heterogeneity of learning styles. In particular scenarios where accessibility support is required, personalization depends on the creation of different representations for individual pieces of content. In this light, the main goal of this article is to describe how we addressed the challenges involved in the construction of a platform that satisfies this requirement. We thus present a system that supports the creation, adaptation, and delivery of personalized courses for people with multiple types of disabilities. More specifically, we introduce the technology, describe its main capabilities, and discuss the results of early evaluations by two instructors of an institution that provides vocational training for people with intellectual disabilities. Our initial results show that the tool was favorably assessed by the instructors and can potentially be adopted in this community.