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Abstract This paper investigates the communicative potential of quick glances at objects in a very particular setting, namely when doctors turn their head briefly to look at their desk-top computer screen while they interact with patients during general practice consultations. The study proposes that these glances often are demonstrably produced to be more than means to acquire information; they are communicative actions that indexically enact a reference to (content on) the computer in support of something the doctor says or does - at the moment or subsequently. In particular, the paper identifies three different kinds of glances: silent glances, glances produced alongside talk, and glances produced in conjunction with pointing gestures and talk. Each type may be produced as a communicative action in its own way. The paper also illustrates how doctors' glances at their computer screens are produced as intrinsic parts of recurrent consultation activities such as test result deliveries, history-takings, and treatment recommendations etc. Overall, the study offers knowledge about the distribution, coordination and organization of glances, and about how doctors use their computers during consultations. Highlights • Doctors regularly glance at their computer screen to communicate and engage in a variety of goal-oriented activities. • Glances are produced to enact an embodied reference to (content on) the computer. • Glances may significantly be produced in silence; alongside talk; or in conjunction with pointing gestures and talk. [ABSTRACT FROM AUTHOR]

Abstract Internet mapping platforms are widely used in participatory planning. They carry the promise of accessibility, a wider audience, and more precise spatial data. However, they are critiqued for issues of trust, digital exclusion, and low usability. While the first two problems can be dependent on the local context, the last one is connected with the introduction of map editing tools. They change browser experience and require accessing spatial knowledge, which is a cognitively demanding task, especially for older people. This can negatively influence both quality and quantity of the data being produced. In this paper, we ask questions about the perception of online forms of participation, the accuracy and reliability of the data being collected in participatory mapping, differences between participants age groups, and design considerations for creating successful and inclusive participatory mapping tools? We conducted usability testing experiment with 30 participants. They were presented with various map editing tasks that were to be completed using a mock internet participatory mapping platform that was designed to mimic real-life applications. The sessions were recorded using eye-tracking equipment. We have observed a considerable enthusiasm for using and interacting with web mapping tools, but this was not reflected in the quality of the data which was very low overall. Known map features were added in wrong places even in simpler tasks. Although there were considerable differences between the oldest and youngest people in the map editing behavior, the quality and quantity of the data were similar. We conclude that mapping platforms can be useful for participatory planning and age of the participants is not a barrier for their inclusion when the interface is properly designed. But in the same time care is needed to provide robust quality control. We also propose a research agenda composed from three avenues of research: data quality, usability and user experience and utility of the map. Highlights • Using web map was a difficult task for experiment participants, regardless of age. • Accuracy of the data produced by participants was very low. • Participants had a positive attitude toward online participation in urban planning. • PPGIS research agenda is proposed: data quality, usability and utility of the map. [ABSTRACT FROM AUTHOR]

Highlights • The advanced calibrated camera and rotating head are utilized for image acquisition. • Each site of image data is processed using an independent adjustment method to obtain a three-dimensional point cloud data. • Each site constitutes a local area network and the bundle block adjustment method is used for each site point cloud data. Abstract At present, the three-dimensional (3D) reconstruction system is based on hand-held camera which causes certain problems, such as needing for a large number of human-computer interactions, demanding on high quality image data and insufficient accuracy of 3D reconstruction. Aiming at these problems, a fully automatic reconstruction of large scene based on multi-site point cloud stitching is presented in this paper. The proposed method uses the Kinect sensor for image acquisition. Since, there are multiple sites in the room, each site of image data is processed using a separate model in order to get good 3D point cloud data. Then, these sites are used to constitute a local area network, and the method of bundle block adjustment is employed to stitch each site point cloud data. The proposed method achieves a high-degree automation and provides a high-precision 3D reconstruction which has two main advantages: (i) the reconstruction process is fully automatic, without any human-computer interaction; (ii) the automatic reconstruction is robust. Experimental results show that proposed automatic reconstruction method is convenient and practical, and can provide better 3D reconstruction model than commonly used methods. Moreover, it can be applied to virtual reality shopping malls, Virtual Reality (VR) and other fields. [ABSTRACT FROM AUTHOR]

In this paper, a novel analysis method for iterative learning control (ILC) algorithms is presented. Even though expressed in the lifted system representation and hence in the time-domain, the convergence rate as a function of the frequency content of the error signal can be determined. Subsequently, based on the analysis method, a novel ILC algorithm (F-ILC) is proposed. The convergence rate at specific frequencies can be set directly in the design process, which allows simple tuning and a priori known convergence rates. Using the F-ILC design, it is shown how to predict the required number of iterations until convergence is achieved, depending on the reference trajectory and information on the system repeatability. Numerical examples are given and experimental results obtained on an internal combustion engine test bench are shown for validation. [ABSTRACT FROM AUTHOR]

With deep learning becoming the dominant approach in computer vision, the use of representations extracted from Convolutional Neural Nets (CNNs) is quickly gaining ground on Fisher Vectors (FVs) as favoured state-of-the-art global image descriptors for image instance retrieval. While the good performance of CNNs for image classification are unambiguously recognised, which of the two has the upper hand in the image retrieval context is not entirely clear yet. We propose a comprehensive study that systematically evaluates FVs and CNNs for image instance retrieval. The first part compares the performances of FVs and CNNs on multiple publicly available data sets and for multiple criteria. We show that no descriptor is systematically better than the other and that performance gains can usually be obtained by using both types together. The second part of the study focuses on the impact of geometrical transformations. We show that performance of CNNs can quickly degrade in the presence of certain transformations and propose a number of ways to incorporate the required invariances in the CNN pipeline. Our findings are organised as a reference guide offering practically useful and simply implementable guidelines to anyone looking for state-of-the-art global descriptors best suited to their specific image instance retrieval problem. [ABSTRACT FROM AUTHOR]

In image classification, can sparse representation (SR) associate one test image with all training ones from the correct class, but not associate with any training ones from the incorrect classes? The backward sparse representation (bSR) which contains complementary information in an opposite direction can remedy the imperfect associations discovered by the general forward sparse representation (fSR). Unfortunately, this complementarity between the fSR and the bSR has not been studied in face recognition. There are two key problems to be solved. One is how to produce additional bases for the bSR. In face recognition, there is no other bases than the single test face image itself for the bSR, which results in large reconstruction residual and weak classification capability of the bSR. The other problem is how to deal with the robustness of the bSR to image corruption. In this paper, we introduce a CoSR model, which combines the fSR and the bSR together, into robust face recognition, by proposing two alternative methods to these two key problems: learning bases and unknown faces help to enrich the bases set of the bSR. Thereby, we also propose two improved algorithms of the CoSR for robust face recognition. Our study shows that our CoSR algorithms obtain inspiring and competitive recognition rates, compared with other state-of-the-art algorithms. The bSR with the proposed methods enriching the bases set contributes the most to the robustness of our CoSR algorithm, and unknown faces works better than learned bases . Moreover, since our CoSR model is performed in a subspace with a very low dimensionality, it gains an overwhelming advantage on time consumption over the traditional RSR algorithm in image pixel space. In addition, our study also reveals that the sparsity plays an important role in our CoSR algorithm for face recognition. [ABSTRACT FROM AUTHOR]

This paper present the use of hand gestures for human-computer interaction, this paper presents an approach to identify hand gestures using muscle activity separated from electromyogram (EMG) using ANN. To retain a constraint-free user's environment, EMG sensing is limited to three arm muscles. EMG signals are processed to attain parameters that are related to the muscles temporal activities. The attainment of these parameters through time constructs a unique signature for each particular gesture. Experimental investigation was carried out to examine the system's reliability in recognizing 6 arm gestures. The results show that the system can recognize the 6 gestures with a success rate of 97.5%. The advantage of such a system is that it is easy to train by a layer, and can easily be implemented in real time after the initial training. [ABSTRACT FROM AUTHOR]

The Ambient Intelligence (AmI) paradigm envisions systems whose central entity is the user. AmI integrates technologies such as Artificial Intelligence, implicit Human Computer Interaction, and Ubiquitous Services. Each capability of AmI systems is oriented towards assistance of humans at work, in the classroom, or even at home. In consequence, the AmI development process usually incorporates the final user since the first stages. However, having users available during all this long process is not always possible. Agent-based social simulations where the users׳ role is played by simulated entities can be used to make the AmI development process faster and more effective. In this scenario, the modelling of CMHBs (Computational Models of Human Behaviour) is a major challenge. To address this issue, this paper proposes a methodology whose main contributions are: (1) the use of domain experts׳ knowledge to create CMHBs; (2) a common methodological framework to develop CMHBs by combining information obtained from sensors׳ perceptions and experts׳ experiences; and, (3) open source tools to support this development paradigm. The paper also presents a full case of study in a hospital which illustrates: the number of recommendations made by the methodology; the techniques proposed (mainly the use of ontologies and temporal reasoning); and, the potential of the methodology to model the personnel in a hospital. [ABSTRACT FROM AUTHOR]

Abstract: Complex networks constitute a recurring issue in the analysis of neuroimaging data. Recently, network motifs have been identified as patterns of interconnections since they appear in a significantly higher number than in randomized networks, in a given ensemble of anatomical or functional connectivity graphs. The current approach for detecting and enumerating motifs in brain networks requires a predetermined motif repertoire and can operate only with motifs of small size (consisting of few nodes). There is a growing interest in methodologies for frequent graph-based pattern mining in large graph datasets that can facilitate adaptive design of motifs. The results presented in this paper are based on the graph-based Substructure pattern mining (gSpan) algorithm and introduce a manifold of ways to exploit it for data-driven motif extraction in connectomics research. Functional connectivity graphs from electroencephalographic (EEG) recordings during resting state and mental calculations are used to demonstrate our approach. Relying on either time-invariant or time-evolving graphs, characteristic motifs associated with various frequency bands were derived and compared. With a suitable manipulation, the gSpan discovers motifs which are specific to performing mental arithmetics. Finally, the subject-dependent temporal signatures of motifs’ appearance revealed the transient nature of the evolving functional connectivity (math-related motifs “come and go”). [Copyright &y& Elsevier]

Abstract: This work proposes a real time human hand gesture recognition system for human computer interaction. The proposed system can recognize 10 different hand gestures at faster rate with reasonable accuracy. The gestures are classified on the basis of shape-based features. Four different shape based features are used for better accuracy. The illumination compensation technique is employed for robust recognition under varying background lightning conditions. Skin color segmentation is used to minimize the chances of false detection. The proposed system is modeled using Verilog HDL and targeted for Xillinx Virtex2 Pro FPGA board. The accuracy of the system is computed as 94.40%. [Copyright &y& Elsevier]

Abstract: This study aimed to investigate the effects of split size (small vs. large splits) and odd–even congruency (parity congruency vs. parity incongruency) of answers on arithmetical equation processing. Event-related potentials were recorded from 18 students in a first-answer-then-equation presentation mode. The ERP results showed that the odd–even congruency had a significant effect on the late positive wave in the small, but not the large split, with a late positive slow wave elicited by the small-split and the odd–even congruent conditions. This result suggests that the split effect has priority over parity congruency when both split and parity are at play simultaneously. Specifically, in non-retrieval arithmetic verification tasks, parity is modulated in cases where a direct judgment cannot be reached simply by depending on the split information. [Copyright &y& Elsevier]

Abstract: The Formal Elements Art Therapy Scale (FEATS) Rating Manual includes a rotation scale to measure the angle or tilt of objects, such as a tree or a person, in drawings. Rotation has been shown to be an important criterion in distinguishing certain patient groups, such as schizophrenia and organic brain disorders. However, this FEATS scale has been traditionally difficult to score accurately by human raters. This difficulty is due in part to the lack of standardization of the rotation scale in the FEATS manual. The present study is an initial step toward standardizing rotation in the FEATS through the use of computer tools. Mental health workers using the computer (“human-computer”) (n =2) and mental workers not using a computer (“human”) (n =7) rated angles from Person Picking an Apple from a Tree (PPAT) drawings completed by individuals with schizophrenia (n =2). Inter-rater Kappa was higher for human-computer raters (.67, p <.001) than human raters (.48, p <.001), and the human raters increased their criteria ratings based upon the human-computer results. From this pilot study (n =11), results imply that human-computer rating tools may be more accurate than human perception, and human perceivers may tend to underrate angle sizes in PPAT drawings. [Copyright &y& Elsevier]

Abstract: Human age estimation from facial images has a wide range of real-world applications in human computer interaction (HCI). In this paper, we use the bio-inspired features (BIF) to analyze different facial parts: (a) eye wrinkles, (b) whole internal face (without forehead area) and (c) whole face (with forehead area) using different feature shape points. The analysis shows that eye wrinkles which cover 30% of the facial area contain the most important aging features compared to internal face and whole face. Furthermore, more extensive experiments are made on FG-NET database by increasing the number of missing pictures in older age groups using MORPH database to enhance the results. [Copyright &y& Elsevier]

Abstract: The categorization of art (paintings, literature) into distinct styles such as Expressionism, or Surrealism has had a profound influence on how art is presented, marketed, analyzed, and historicized. Here, we present results from human and computational experiments with the goal of determining to which degree such categories can be explained by simple, low-level appearance information in the image. Following experimental methods from perceptual psychology on category formation, naive, non-expert participants were first asked to sort printouts of artworks from different art periods into categories. Converting these data into similarity data and running a multi-dimensional scaling (MDS) analysis, we found distinct categories which corresponded sometimes surprisingly well to canonical art periods. The result was cross-validated on two complementary sets of artworks for two different groups of participants showing the stability of art interpretation. The second focus of this paper was on determining how far computational algorithms would be able to capture human performance or would be able in general to separate different art categories. Using several state-of-the-art algorithms from computer vision, we found that whereas low-level appearance information can give some clues about category membership, human grouping strategies included also much higher-level concepts. [Copyright &y& Elsevier]

Abstract: As computing curricula have evolved, human–computer interaction has gradually become part of many of them, and the recent ACM/IEEE report on the core of computing science and engineering includes human–computer interaction as one of the fundamental sub-areas that should be addressed. However, both technology and human–computer interaction are developing rapidly, thus a continuous effort is needed to maintain a programme, bibliography, and a set of practical assignments up to date and adapted to the current technology. This paper briefly presents an introductory course on human–computer interaction offered to electrical and computer engineering students at the University of Aveiro. [Copyright &y& Elsevier]

An increasing body of evidence has shown that attention is a multi-type and multilevel cognitive faculty. The dominant computational modeling approaches to attention have often focused on one specific type of attention at one specific level. In particular, various connectionist modeling techniques at the subsymbolic level have been widely adopted. In this paper, we report a symbolic computational model of the Attentional Network Test, which simultaneously involves different types of attention (alerting, orienting, and executive control), each subserved by distinctive attentional networks in the brain. The model was developed in ACT-R, a rule-based cognitive architecture. The results show that the model, by sequentially firing rules at a rate of about one every 40 ms, was able to capture the effect of each attentional network. The model implies that while the attentional networks can be distinguished at both neuroanatomical and behavioral levels, different attentional networks may adopt similar computational operations at least at a symbolic rule level. [Copyright &y& Elsevier]

This paper describes a mean field approach to defining and implementing policy-based system administration. The concepts of regulation and optimization are used to define the notion of maintenance. These are then used to evaluate stable equilibria of system configuration, that are associated with sustainable policies for system management. Stable policies are thus associated with fixed points of a mapping that describes the evolution of the system. In general, such fixed points are the solutions of strategic games. A consistent system policy is not sufficient to guarantee compliance; the policy must also be implementable and maintainable. The paper proposes two types of model to understand policy driven management of Human-Computer systems: (i) average dynamical descriptions of computer system variables which provide a quantitative basis for decision, and (ii) competitive game theoretical descriptions that select optimal courses of action by generalizing the notion of configuration equilibria. It is shown how models can be formulated and simple examples are given. [Copyright &y& Elsevier]

Presents the history of the human computer. Division of labor in computing the return of Halley's comet by Alexis-Claude Clairaut's friends in 1758; Organization of computing groups in 1759; Advocacy for specialization of skills by Adam Smith; Gaspard de Prony's preparation of math tables; Charles Babbage's use of mechanical control; Professionalization of human computing; End of human computers.