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Ethics and Design in the Brazilian Context
Often driven by practical and immediate requirements, more and more people are incorporating technology into a variety of aspects of their lives, often without reflecting on the consequences of using them. On the other hand, studies on interactive system development that lead to behavioral change have been gaining ground on the agenda of large HCI conferences. This movement brings to the forefront the fundamental issues of ethics in design and technology use. A designer’s intentions, when directing certain actions or behaviors, are not always explicit or desired by the stakeholders affected by the use of the technology. Systems that induce an undesired purchase, or even those that use conditioning strategies to cause a behavioral change are examples of such intentions. The challenge proposed is therefore about the relationship between design and personal freedom in a way that these technology users do not become victims, either passively or submissively, of the effects of its use. This advance allows for the redefinition of the relationship between man and technology, and the application of new forms of designing and developing interactive systems that take into account the ethical aspects of this relationship
Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms
The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications
Artificial Intelligence in the Context of Human Consciousness
Artificial intelligence (AI) can be defined as the ability of a machine to learn and make decisions based on acquired information. AI’s development has incited rampant public speculation regarding the singularity theory: a futuristic phase in which intelligent machines are capable of creating increasingly intelligent systems. Its implications, combined with the close relationship between humanity and their machines, make achieving understanding both natural and artificial intelligence imperative. Researchers are continuing to discover natural processes responsible for essential human skills like decision-making, understanding language, and performing multiple processes simultaneously. Artificial intelligence attempts to simulate these functions through techniques like artificial neural networks, Markov Decision Processes, Human Language Technology, and Multi-Agent Systems, which rely upon a combination of mathematical models and hardware
Digital Ecosystems: Ecosystem-Oriented Architectures
We view Digital Ecosystems to be the digital counterparts of biological
ecosystems. Here, we are concerned with the creation of these Digital
Ecosystems, exploiting the self-organising properties of biological ecosystems
to evolve high-level software applications. Therefore, we created the Digital
Ecosystem, a novel optimisation technique inspired by biological ecosystems,
where the optimisation works at two levels: a first optimisation, migration of
agents which are distributed in a decentralised peer-to-peer network, operating
continuously in time; this process feeds a second optimisation based on
evolutionary computing that operates locally on single peers and is aimed at
finding solutions to satisfy locally relevant constraints. The Digital
Ecosystem was then measured experimentally through simulations, with measures
originating from theoretical ecology, evaluating its likeness to biological
ecosystems. This included its responsiveness to requests for applications from
the user base, as a measure of the ecological succession (ecosystem maturity).
Overall, we have advanced the understanding of Digital Ecosystems, creating
Ecosystem-Oriented Architectures where the word ecosystem is more than just a
metaphor.Comment: 39 pages, 26 figures, journa
Future bathroom: A study of user-centred design principles affecting usability, safety and satisfaction in bathrooms for people living with disabilities
Research and development work relating to assistive technology
2010-11 (Department of Health)
Presented to Parliament pursuant to Section 22 of the Chronically Sick and Disabled Persons Act 197
NeuroSVM: A Graphical User Interface for Identification of Liver Patients
Diagnosis of liver infection at preliminary stage is important for better
treatment. In todays scenario devices like sensors are used for detection of
infections. Accurate classification techniques are required for automatic
identification of disease samples. In this context, this study utilizes data
mining approaches for classification of liver patients from healthy
individuals. Four algorithms (Naive Bayes, Bagging, Random forest and SVM) were
implemented for classification using R platform. Further to improve the
accuracy of classification a hybrid NeuroSVM model was developed using SVM and
feed-forward artificial neural network (ANN). The hybrid model was tested for
its performance using statistical parameters like root mean square error (RMSE)
and mean absolute percentage error (MAPE). The model resulted in a prediction
accuracy of 98.83%. The results suggested that development of hybrid model
improved the accuracy of prediction. To serve the medicinal community for
prediction of liver disease among patients, a graphical user interface (GUI)
has been developed using R. The GUI is deployed as a package in local
repository of R platform for users to perform prediction.Comment: 9 pages, 6 figure
Steps to an Ecology of Networked Knowledge and Innovation: Enabling new forms of collaboration among sciences, engineering, arts, and design
SEAD network White Papers ReportThe final White Papers (posted at http://seadnetwork.wordpress.com/white-paper- abstracts/final-white-papers/) represent a spectrum of interests in advocating for transdisciplinarity among arts, sciences, and technologies. All authors submitted plans of action and identified stakeholders they perceived as instrumental in carrying out such plans. The individual efforts led to an international scope. One of the important characteristics of this collection is that the papers do not represent a collective aim toward an explicit initiative. Rather, they offer a broad array of views on barriers faced and prospective solutions. In summary, the collected White Papers and associated Meta- analyses began as an effort to take the pulse of the SEAD community as broadly as possible. The ideas they generated provide a fruitful basis for gauging trends and challenges in facilitating the growth of the network and implementing future SEAD initiatives.National Science Foundation Grant No.1142510. Additional funding was provided by the ATEC program at the University of Texas at Dallas and the Institute for Applied Creativity at Texas A&M University
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