Cooperation and competition have same benefits but different costs.

Cooperation and competition shape everyday human interactions and impact individuals' chances of success in different domains. Using a virtual Stroop test, classically employed to assess general cognitive interference, we examined the impact of social context (cooperation and competition) and other's ability (higher and lower performers) on performance, perceived stress, and autonomic activity. In Experiment 1, we found that both cooperation with a lower performer and competition with a higher performer led to similar enhancement in performance. However, only competition with a more skilled opponent induced an increase in perceived stress and physiological activity. Experiment 2 further demonstrated that these effects persisted even with prolonged exposure to these contexts. In summary, cooperation can be just as effective as competition in improving individuals' performance. However, cooperation does not carry the same level of stress and physiological burden as the competitive context, representing a healthier and more optimal way to boost individual performance.

A Brief History of AI: How to Prevent Another Winter (A Critical Review).

Artificial intelligence has witnessed exponential growth in the past decade. Advances in computing power and the design of sophisticated artificial intelligence algorithms have enabled computers to outperform humans in a variety of tasks. Yet, artificial intelligence's path has never been smooth, having essentially fallen apart twice in its lifetime after periods of popular success. We provide a brief rundown of artificial intelligence's evolution, highlighting its crucial moments and major turning points from inception to the present. In doing so, we attempt to learn, anticipate the future, and discuss what steps may be taken to prevent another winter.

Augmented Reality Learning Environment for Basic Life Support and Defibrillation Training: Usability Study.

BACKGROUND: Basic life support (BLS) is crucial in the emergency response system, as sudden cardiac arrest is still a major cause of death worldwide. Unfortunately, only a minority of victims receive cardiopulmonary resuscitation (CPR) from bystanders. In this context, training could be helpful to save more lives, and technology-enhanced BLS simulation is one possible solution. OBJECTIVE: The aim of this study is to assess the feasibility and acceptability of our augmented reality (AR) prototype as a tool for BLS training. METHODS: Holo-BLSD is an AR self-instruction training system, in which a standard CPR manikin is "augmented" with an interactive virtual environment that reproduces realistic scenarios. Learners can use natural gestures, body movements, and spoken commands to perform their tasks, with virtual 3D objects anchored to the manikin and the environment. During the experience, users were trained to use the device while being guided through an emergency simulation and, at the end, were asked to complete a survey to assess the feasibility and acceptability of the proposed tool (5-point Likert scale; 1=Strongly Disagree, 5=Strongly Agree). RESULTS: The system was rated easy to use (mean 4.00, SD 0.94), and the trainees stated that most people would learn to use it very quickly (mean 4.00, SD 0.89). Voice (mean 4.48, SD 0.87), gaze (mean 4.12, SD 0.97), and gesture interaction (mean 3.84, SD 1.14) were judged positively, although some hand gesture recognition errors reduced the feeling of having the right level of control over the system (mean 3.40, SD 1.04). CONCLUSIONS: We found the Holo-BLSD system to be a feasible and acceptable tool for AR BLS training.

Street Viewer: An Autonomous Vision Based Traffic Tracking System.

The development of intelligent transportation systems requires the availability of both accurate traffic information in real time and a cost-effective solution. In this paper, we describe Street Viewer, a system capable of analyzing the traffic behavior in different scenarios from images taken with an off-the-shelf optical camera. Street Viewer operates in real time on embedded hardware architectures with limited computational resources. The system features a pipelined architecture that, on one side, allows one to exploit multi-threading intensively and, on the other side, allows one to improve the overall accuracy and robustness of the system, since each layer is aimed at refining for the following layers the information it receives as input. Another relevant feature of our approach is that it is self-adaptive. During an initial setup, the application runs in learning mode to build a model of the flow patterns in the observed area. Once the model is stable, the system switches to the on-line mode where the flow model is used to count vehicles traveling on each lane and to produce a traffic information summary. If changes in the flow model are detected, the system switches back autonomously to the learning mode. The accuracy and the robustness of the system are analyzed in the paper through experimental results obtained on several different scenarios and running the system for long periods of time.

ANAlyte: A modular image analysis tool for ANA testing with indirect immunofluorescence.

BACKGROUND AND OBJECTIVES: The automated analysis of indirect immunofluorescence images for Anti-Nuclear Autoantibody (ANA) testing is a fairly recent field that is receiving ever-growing interest from the research community. ANA testing leverages on the categorization of intensity level and fluorescent pattern of IIF images of HEp-2 cells to perform a differential diagnosis of important autoimmune diseases. Nevertheless, it suffers from tremendous lack of repeatability due to subjectivity in the visual interpretation of the images. The automatization of the analysis is seen as the only valid solution to this problem. Several works in literature address individual steps of the work-flow, nonetheless integrating such steps and assessing their effectiveness as a whole is still an open challenge. METHODS: We present a modular tool, ANAlyte, able to characterize a IIF image in terms of fluorescent intensity level and fluorescent pattern without any user-interactions. For this purpose, ANAlyte integrates the following: (i) Intensity Classifier module, that categorizes the intensity level of the input slide based on multi-scale contrast assessment; (ii) Cell Segmenter module, that splits the input slide into individual HEp-2 cells; (iii) Pattern Classifier module, that determines the fluorescent pattern of the slide based on the pattern of the individual cells. RESULTS: To demonstrate the accuracy and robustness of our tool, we experimentally validated ANAlyte on two different public benchmarks of IIF HEp-2 images with rigorous leave-one-out cross-validation strategy. We obtained overall accuracy of fluorescent intensity and pattern classification respectively around 85% and above 90%. We assessed all results by comparisons with some of the most representative state of the art works. CONCLUSIONS: Unlike most of the other works in the recent literature, ANAlyte aims at the automatization of all the major steps of ANA image analysis. Results on public benchmarks demonstrate that the tool can characterize HEp-2 slides in terms of intensity and fluorescent pattern with accuracy better or comparable with the state of the art techniques, even when such techniques are run on manually segmented cells. Hence, ANAlyte can be proposed as a valid solution to the problem of ANA testing automatization.

An automated approach to the segmentation of HEp-2 cells for the indirect immunofluorescence ANA test.

The automatization of the analysis of Indirect Immunofluorescence (IIF) images is of paramount importance for the diagnosis of autoimmune diseases. This paper proposes a solution to one of the most challenging steps of this process, the segmentation of HEp-2 cells, through an adaptive marker-controlled watershed approach. Our algorithm automatically conforms the marker selection pipeline to the peculiar characteristics of the input image, hence it is able to cope with different fluorescent intensities and staining patterns without any a priori knowledge. Furthermore, it shows a reduced sensitivity to over-segmentation errors and uneven illumination, that are typical issues of IIF imaging.

MusA: using indoor positioning and navigation to enhance cultural experiences in a museum.

In recent years there has been a growing interest in the use of multimedia mobile guides in museum environments. Mobile devices have the capabilities to detect the user context and to provide pieces of information suitable to help visitors discover and follow the logical and emotional connections that develop during the visit. In this scenario, location based services (LBS) currently represent an asset, and the choice of the technology to determine users' position, combined with the definition of methods that can effectively convey information, become key issues in the design process. In this work, we present Museum Assistant (MusA), a general framework for the development of multimedia interactive guides for mobile devices. Its main feature is a vision-based indoor positioning system that allows the provision of several LBS, from way-finding to the contextualized communication of cultural contents, aimed at providing a meaningful exploration of exhibits according to visitors' personal interest and curiosity. Starting from the thorough description of the system architecture, the article presents the implementation of two mobile guides, developed to respectively address adults and children, and discusses the evaluation of the user experience and the visitors' appreciation of these applications.

A compreensão leitora de jovens e adultos tardiamente escolarizados / Under-schooled youngsters' and adults' reading comprehension

Foi analisada a importância de diversas habilidades linguístico-cognitivas para a compreensão leitura de jovens e adultos tardiamente escolarizados: leitura de palavras, consciência fonológica, consciência morfológica, nomeação automatizada rápida, memória de trabalho e vocabulário. A escolaridade aparece entre os melhores preditores para a compreensão leitora, incluindo as perguntas de macroestrutura, argumentação e inferência, mas não para os itens de detalhe. Além da escolaridade figuram entre os melhores preditores da compreensão leitora e das perguntas de macroestrutura a leitura de palavras e o vocabulário. O vocabulário é também um bom preditor para as perguntas de argumentação e juntamente com a memória de trabalho e a RAN para os itens de inferência. Finalmente, os melhores preditores para as perguntas de detalhe são memória de trabalho e RAN...

The importance of different linguistic and cognitive abilities of reading comprehension (word reading, phonological awareness, morphological awareness, Rapid Automatized Naming (RAN), working memory and vocabulary) to under-schooled youngsters and adults was examined. Schooling is one of the best predictors for reading comprehension as well as for items related to macrostructure, argumentation and inference; but it is not to questions related to specific details. Best predictors for reading comprehension and items of macrostructure also included word reading and vocabulary. Vocabulary was also a good predictor to items related to argumentation, and along with working memory and RAN to inferential questions. Finally, best predictors to items related to specific details include working memory and RAN...

The exploitation of data from remote and human sensors for environment monitoring in the SMAT project.

In this paper, we outline the functionalities of a system that integrates and controls a fleet of Unmanned Aircraft Vehicles (UAVs). UAVs have a set of payload sensors employed for territorial surveillance, whose outputs are stored in the system and analysed by the data exploitation functions at different levels. In particular, we detail the second level data exploitation function whose aim is to improve the sensors data interpretation in the post-mission activities. It is concerned with the mosaicking of the aerial images and the cartography enrichment by human sensors--the social media users. We also describe the software architecture for the development of a mash-up (the integration of information and functionalities coming from the Web) and the possibility of using human sensors in the monitoring of the territory, a field in which, traditionally, the involved sensors were only the hardware ones.

The visual hull of smooth curved objects.

The visual hull is a geometric entity that relates the shape of an object to its silhouettes or shadows. This paper develops the theory of the visual hull of generic smooth objects. We show that the visual hull can be constructed using surfaces which partition the viewpoint space of the aspect graph of the object. The surfaces are those generated by the visual events tangent crossing and triple point. An analysis based on the shape of the object at the tangency points of these surfaces allows pruning away many surfaces and patches not relevant to the construction. An algorithm for computing the visual hull is outlined.