ARISupport - Interaction support for augmented reality systems

The communication between user and software is a basic stage in any Interaction System project. In interactive systems, this communication is established by the means of a graphical interface, whose objective is to supply a visual representation of the main entities and functions present in the Virtual Environment. New ways of interacting in computational systems have been minimizing the gap in the relationship between man and computer, and therefore enhancing its usability. The objective of this paper, therefore, is to present a proposal for a non-conventional user interface library called ARISupport, which supplies ARToolKit applications developers with an opportunity to create simple GUI interfaces, and provides some of the functionality used in Augmented Reality systems. © Springer-Verlag Berlin Heidelberg 2005.

Malware distributed collection and pre-classification system using honeypot technology

Malware has become a major threat in the last years due to the ease of spread through the Internet. Malware detection has become difficult with the use of compression, polymorphic methods and techniques to detect and disable security software. Those and other obfuscation techniques pose a problem for detection and classification schemes that analyze malware behavior. In this paper we propose a distributed architecture to improve malware collection using different honeypot technologies to increase the variety of malware collected. We also present a daemon tool developed to grab malware distributed through spam and a pre-classification technique that uses antivirus technology to separate malware in generic classes. © 2009 SPIE.

Wizard CD Plus and ProTaper Universal: analysis of apical transportation using new software

Objective: This study has two aims: 1) to evaluate the apical transportation of the Wizard CD Plus and ProTaper Universal after preparation of simulated root canals; 2) to compare, with Adobe Photoshop, the ability of a new software (Regeemy) in superposing and subtracting images. Material and Methods: Twenty five simulated root canals in acrylic-resin blocks (with 20 degrees curvature) underwent cone beam computed tomography before and after preparation with the rotary systems (70 kVp, 4 mA, 10 s and with the 8x8 cm FoV selection). Canals were prepared up to F2 (ProTaper) and 24.04 (Wizard CD Plus) instruments and the working length was established to 15 mm. The tomographic images were imported into iCAT Vision software and CorelDraw for standardization. The superposition of pre- and post-instrumentation images from both systems was performed using Regeemy and Adobe Photoshop. The apical transportation was measured in millimetres using Image J. Five acrylic resin blocks were used to validate the superposition achieved by the software. Student's t-test for independent samples was used to evaluate the apical transportation achieved by the rotary systems using each software individually. Student's t-test for paired samples was used to compare the ability of each software in superposing and subtracting images from one rotary system per time. Results: The values obtained with Regeemy and Adobe Photoshop were similar to rotary systems (P>0.05). ProTaper Universal and Wizard CD Plus promoted similar apical transportation regardless of the software used for image's superposition and subtraction (P>0.05). Conclusion: Wizard CD Plus and ProTaper Universal promoted little apical transportation. Regeemy consists in a feasible software to superpose and subtract images and appears to be an alternative to Adobe Photoshop.

Development of neurofuzzy architecture for solving the N-Queens problem

Neural networks are dynamic systems consisting of highly interconnected and parallel nonlinear processing elements that are shown to be extremely effective in computation. This paper presents an architecture of recurrent neural networks for solving the N-Queens problem. More specifically, a modified Hopfield network is developed and its internal parameters are explicitly computed using the valid-subspace technique. These parameters guarantee the convergence of the network to the equilibrium points, which represent a solution of the considered problem. The network is shown to be completely stable and globally convergent to the solutions of the N-Queens problem. A fuzzy logic controller is also incorporated in the network to minimize convergence time. Simulation results are presented to validate the proposed approach.

Arquitetura de um sistema multiagente autônomo para supervisão e controle

This paper presents a multi-agent architecture that was designed to develop processes supervision and control systems, with the main objective to automate tasks that are repetitive and stressful, and error prone when performed by humans. A set of agents were identified, based on the study of a number of applications found in the literature, that use the approach of multi-agent systems for data integration and process monitoring to faults detection and diagnosis, these agents are used as basis of the proposed multi-agent architecture. A prototype system for the analysis of abnormalities during oil wells drilling was developed.

Modeling of active holonic control systems for intelligent buildings

Building facilities have become important infrastructures for modern productive plants dedicated to services. In this context, the control systems of intelligent buildings have evolved while their reliability has evidently improved. However, the occurrence of faults is inevitable in systems conceived, constructed and operated by humans. Thus, a practical alternative approach is found to be very useful to reduce the consequences of faults. Yet, only few publications address intelligent building modeling processes that take into consideration the occurrence of faults and how to manage their consequences. In the light of the foregoing, a procedure is proposed for the modeling of intelligent building control systems, considersing their functional specifications in normal operation and in the of the event of faults. The proposed procedure adopts the concepts of discrete event systems and holons, and explores Petri nets and their extensions so as to represent the structure and operation of control systems for intelligent buildings under normal and abnormal situations. (C) 2012 Elsevier B.V. All rights reserved.

Using Machine Learning Classifiers to Assist Healthcare-Related Decisions: Classification of Electronic Patient Records

Surveillance Levels (SLs) are categories for medical patients (used in Brazil) that represent different types of medical recommendations. SLs are defined according to risk factors and the medical and developmental history of patients. Each SL is associated with specific educational and clinical measures. The objective of the present paper was to verify computer-aided, automatic assignment of SLs. The present paper proposes a computer-aided approach for automatic recommendation of SLs. The approach is based on the classification of information from patient electronic records. For this purpose, a software architecture composed of three layers was developed. The architecture is formed by a classification layer that includes a linguistic module and machine learning classification modules. The classification layer allows for the use of different classification methods, including the use of preprocessed, normalized language data drawn from the linguistic module. We report the verification and validation of the software architecture in a Brazilian pediatric healthcare institution. The results indicate that selection of attributes can have a great effect on the performance of the system. Nonetheless, our automatic recommendation of surveillance level can still benefit from improvements in processing procedures when the linguistic module is applied prior to classification. Results from our efforts can be applied to different types of medical systems. The results of systems supported by the framework presented in this paper may be used by healthcare and governmental institutions to improve healthcare services in terms of establishing preventive measures and alerting authorities about the possibility of an epidemic.

Modelling shared attention through relational reinforcement learning

Shared attention is a type of communication very important among human beings. It is sometimes reserved for the more complex form of communication being constituted by a sequence of four steps: mutual gaze, gaze following, imperative pointing and declarative pointing. Some approaches have been proposed in Human-Robot Interaction area to solve part of shared attention process, that is, the most of works proposed try to solve the first two steps. Models based on temporal difference, neural networks, probabilistic and reinforcement learning are methods used in several works. In this article, we are presenting a robotic architecture that provides a robot or agent, the capacity of learning mutual gaze, gaze following and declarative pointing using a robotic head interacting with a caregiver. Three learning methods have been incorporated to this architecture and a comparison of their performance has been done to find the most adequate to be used in real experiment. The learning capabilities of this architecture have been analyzed by observing the robot interacting with the human in a controlled environment. The experimental results show that the robotic head is able to produce appropriate behavior and to learn from sociable interaction.

Discrimination of media moments and media intervals: sticker-based watch-and-comment annotation

In this paper we discuss the problem of how to discriminate moments of interest on videos or live broadcast shows. The primary contribution is a system which allows users to personalize their programs with previously created media stickers-pieces of content that may be temporarily attached to the original video. We present the system's architecture and implementation, which offer users operators to transparently annotate videos while watching them. We offered a soccer fan the opportunity to add stickers to the video while watching a live match: the user reported both enjoying and being comfortable using the stickers during the match-relevant results even though the experience was not fully representative.

CoolBOT: a component-oriented programming framework for robotics

[EN] Programming software for controlling robotic systems in order to built working systems that perform adequately according to their design requirements remains being a task that requires an important development effort. Currently, there are no clear programming paradigms for programming robotic systems, and the programming techniques which are of common use today are not adequate to deal with the complexity associated with these systems. The work presented in this document describes a programming tool, concretely a framework, that must be considered as a first step to devise a tool for dealing with the complexity present in robotics systems. In this framework the software that controls a system is viewed as a dynamic network of units of execution inter-connected by means of data paths. Each one of these units of execution, called a component, is a port automaton which provides a given functionality, hidden behind an external interface specifying clearly which data it needs and which data it produces. Components, once defined and built, may be instantiated, integrated and used as many times as needed in other systems. The framework provides the infrastructure necessary to support this concept for components and the inter communication between them by means of data paths (port connections) which can be established and de-established dynamically. Moreover, and considering that the more robust components that conform a system are, the more robust the system is, the framework provides the necessary infrastructure to control and monitor the components than integrate a system at any given instant of time.

Diseño, implementación y verificación de un controlador para robots educativos

[EN] This project briefly analyzes the scope and applications of Industrial Robotics, as well as the importance that this technical discipline has gained in the past decades. In addition, it proposes a modern platform to assist in teaching this discipline in colleges and universities. This new educational platform for the teaching of Industrial Robotics is based on the robotic systems from Rhino Robotics Ltd., using the existing robotic arms and replacing the control electronics by a newer, modern and yet backwards-compatible controller. In addition to the controller, this platform also provides new, up-to-date software utilities that are more intuitive than those provided with the old system. The work to be done consists essentially in receiving commands from a personal computer which the controller must interpret in order to control the motors of the robotic arm. The controller itself will be implemented as an embedded system based on microcontrollers. This requires the implementation of a communication protocol between the personal computer and the microcontroller, the design of a command interpreter, the design of the electronics for motor control using PWM and H-bridges, and the implementation of control techniques (more precisely, PID control). Hence, this project combines software and hardware design and integration techniques with motor control techniques and feedback control methods from Control Engineering, along with the kinematic analysis of the Rhino XR-4 robotic arm. 

Knowledge Patterns for the Web: extraction, tranformation and reuse

This thesis aims at investigating methods and software architectures for discovering what are the typical and frequently occurring structures used for organizing knowledge in the Web. We identify these structures as Knowledge Patterns (KPs). KP discovery needs to address two main research problems: the heterogeneity of sources, formats and semantics in the Web (i.e., the knowledge soup problem) and the difficulty to draw relevant boundary around data that allows to capture the meaningful knowledge with respect to a certain context (i.e., the knowledge boundary problem). Hence, we introduce two methods that provide different solutions to these two problems by tackling KP discovery from two different perspectives: (i) the transformation of KP-like artifacts to KPs formalized as OWL2 ontologies; (ii) the bottom-up extraction of KPs by analyzing how data are organized in Linked Data. The two methods address the knowledge soup and boundary problems in different ways. The first method provides a solution to the two aforementioned problems that is based on a purely syntactic transformation step of the original source to RDF followed by a refactoring step whose aim is to add semantics to RDF by select meaningful RDF triples. The second method allows to draw boundaries around RDF in Linked Data by analyzing type paths. A type path is a possible route through an RDF that takes into account the types associated to the nodes of a path. Then we present K~ore, a software architecture conceived to be the basis for developing KP discovery systems and designed according to two software architectural styles, i.e, the Component-based and REST. Finally we provide an example of reuse of KP based on Aemoo, an exploratory search tool which exploits KPs for performing entity summarization.

In vitro accuracy evaluation of image-guided robot system for direct cochlear access

HYPOTHESIS A previously developed image-guided robot system can safely drill a tunnel from the lateral mastoid surface, through the facial recess, to the middle ear, as a viable alternative to conventional mastoidectomy for cochlear electrode insertion. BACKGROUND Direct cochlear access (DCA) provides a minimally invasive tunnel from the lateral surface of the mastoid through the facial recess to the middle ear for cochlear electrode insertion. A safe and effective tunnel drilled through the narrow facial recess requires a highly accurate image-guided surgical system. Previous attempts have relied on patient-specific templates and robotic systems to guide drilling tools. In this study, we report on improvements made to an image-guided surgical robot system developed specifically for this purpose and the resulting accuracy achieved in vitro. MATERIALS AND METHODS The proposed image-guided robotic DCA procedure was carried out bilaterally on 4 whole head cadaver specimens. Specimens were implanted with titanium fiducial markers and imaged with cone-beam CT. A preoperative plan was created using a custom software package wherein relevant anatomical structures of the facial recess were segmented, and a drill trajectory targeting the round window was defined. Patient-to-image registration was performed with the custom robot system to reference the preoperative plan, and the DCA tunnel was drilled in 3 stages with progressively longer drill bits. The position of the drilled tunnel was defined as a line fitted to a point cloud of the segmented tunnel using principle component analysis (PCA function in MatLab). The accuracy of the DCA was then assessed by coregistering preoperative and postoperative image data and measuring the deviation of the drilled tunnel from the plan. The final step of electrode insertion was also performed through the DCA tunnel after manual removal of the promontory through the external auditory canal. RESULTS Drilling error was defined as the lateral deviation of the tool in the plane perpendicular to the drill axis (excluding depth error). Errors of 0.08 ± 0.05 mm and 0.15 ± 0.08 mm were measured on the lateral mastoid surface and at the target on the round window, respectively (n =8). Full electrode insertion was possible for 7 cases. In 1 case, the electrode was partially inserted with 1 contact pair external to the cochlea. CONCLUSION The purpose-built robot system was able to perform a safe and reliable DCA for cochlear implantation. The workflow implemented in this study mimics the envisioned clinical procedure showing the feasibility of future clinical implementation.

Clinical decision support capabilities of commercially-available clinical information systems.

BACKGROUND: The most effective decision support systems are integrated with clinical information systems, such as inpatient and outpatient electronic health records (EHRs) and computerized provider order entry (CPOE) systems. Purpose The goal of this project was to describe and quantify the results of a study of decision support capabilities in Certification Commission for Health Information Technology (CCHIT) certified electronic health record systems. METHODS: The authors conducted a series of interviews with representatives of nine commercially available clinical information systems, evaluating their capabilities against 42 different clinical decision support features. RESULTS: Six of the nine reviewed systems offered all the applicable event-driven, action-oriented, real-time clinical decision support triggers required for initiating clinical decision support interventions. Five of the nine systems could access all the patient-specific data items identified as necessary. Six of the nine systems supported all the intervention types identified as necessary to allow clinical information systems to tailor their interventions based on the severity of the clinical situation and the user's workflow. Only one system supported all the offered choices identified as key to allowing physicians to take action directly from within the alert. Discussion The principal finding relates to system-by-system variability. The best system in our analysis had only a single missing feature (from 42 total) while the worst had eighteen.This dramatic variability in CDS capability among commercially available systems was unexpected and is a cause for concern. CONCLUSIONS: These findings have implications for four distinct constituencies: purchasers of clinical information systems, developers of clinical decision support, vendors of clinical information systems and certification bodies.

An Architecture to offer Cloud-Based Radio Access Network as a Service

This paper addresses the novel notion of offering a radio access network as a service. Its components may be instantiated on general purpose platforms with pooled resources (both radio and hardware ones) dimensioned on-demand, elastically and following the pay-per-use principle. A novel architecture is proposed that supports this concept. The architecture's success is in its modularity, well-defined functional elements and clean separation between operational and control functions. By moving much processing traditionally located in hardware for computation in the cloud, it allows the optimisation of hardware utilization and reduction of deployment and operation costs. It enables operators to upgrade their network as well as quickly deploy and adapt resources to demand. Also, new players may easily enter the market, permitting a virtual network operator to provide connectivity to its users.