Human action recognition based on scene semantics

Like outdoors, indoor security is also a critical problem and human action recognition in indoor area is still a hot topic. Most studies on human action recognition ignored the semantic information of a scene, whereas indoors contains varieties of semantics. Meanwhile, the depth sensor with color and depth data is more suitable for extracting the semantics context in human actions. Hence, this paper proposed an indoor action recognition method using Kinect based on the semantics of a scene. First, we proposed a trajectory clustering algorithm for a three-dimensional (3D) scene by combining the different characteristics of people such as the spatial location, movement direction, and speed. Based on the clustering results and scene context, it concludes a region of interest (ROI) extraction method for indoors, and dynamic time warping (DTW) is used to study the abnormal action sequences. Finally, the color and depth-data-based 3D motion history image (3D–MHI) features and the semantics context of the scene were combined to recognize human action. In the experiment, two datasets were tested and the results demonstrate that our semantics-based method performs better than other methods.

     

Predicting passenger seat comfort and discomfort on the basis of human,context and seat characteristics: a literature review

Abstract

This literature review focused on passenger seat comfort and discomfort in a human–product–context interaction. The relationships between anthropometric variables (human level), activities (context level), seat characteristics (product level) and the perception of comfort and discomfort were studied through mediating variables, such as body posture, movement and interface pressure. It is concluded that there are correlations between anthropometric variables and interface pressure variables, and that this relationship is affected by body posture. The results of studies on the correlation between pressure variables and passenger comfort and discomfort are not in line with each other. Only associations were found between the other variables (e.g. activities and seat characteristics). A conceptual model illustrates the results of the review, but relationships could not be quantified due to a lack of statistical evidence and large differences in research set-ups between the reviewed papers.

Practitioner Summary: This literature review set out to quantify the relationships between human, context and seat characteristics, and comfort and discomfort experience of passenger seats, in order to build a predictive model that can support seat designers and purchasers to make informed decisions. However, statistical evidence is lacking from existing literature.     

Fuzzy triangulation signature for detection of change in human emotion from face video image sequence

The present article proposes a geometry-based fuzzy relational technique for capturing gradual change in human emotion over time available from relevant face image sequences. As associated features, we make use of fuzzy membership arising out of five triangle signatures such as - (i) Fuzzy Isosceles Triangle Signature (FIS), (ii) Fuzzy Right Triangle Signature (FRS), (iii) Fuzzy Right Isosceles Triangle Signature (FIRS), (iv) Fuzzy Equilateral Triangle Signature (FES), and (v) Other Fuzzy Triangles Signature (OFS) to achieve the task of appropriate classification of facial transition from neutrality to one among the six expressions viz. anger (AN), disgust (DI), fear (FE), happiness (HA), sadness (SA) and surprise (SU). The effectiveness of the Multilayer Perceptron (MLP) classifier is tested and validated through 10 fold cross-validation method on three benchmark image sequence datasets namely Extended Cohn-Kanade (CK+), M&M Initiative (MMI), and Multimedia Understanding Group (MUG). Experimental outcomes are found to have achieved accuracy to the tune of 98.47%, 93.56%, and 99.25% on CK+, MMI, and MUG respectively vindicating the effectiveness by exhibiting the superiority of our proposed technique in comparison to other state-of-the-art methods in this regard.

     

Effects of ICT-enabled social capital on inter-organizational relationships and performance: empirical evidence from an emerging economy

ABSTRACT

Social capital is an influential concept in understanding why and how some firms do better in inter-organizational relationships (IORs). It is an important factor in developing relationships of trust, making the basis for better collaboration among individuals, groups, and organizations. This paper presents findings from an empirical study of the effect of multiple dimensions of Social Capital and the moderating role of Information and Communication Technologies (ICT) on inter-bank strategic collaboration in Sri Lanka, a developing context. The theoretical model is validated using partial least squares-based structural equation modeling based on survey and secondary data. The findings suggest that there is a strong positive combined effect of social capital and ICT toward inter-firm strategic alliances. The results contribute to both social capital theory and theories of ICT for development. It will also contribute to a more holistic perspective that incorporates social, technical, and organizational aspects for building effective strategies.     

Gestural systems for the voice: performance approaches and repertoire

ABSTRACT

Singing, like dance, emerges directly from the body. The voice, in combination with whole body movement, constitutes a potent form of self-expression. Gestural systems offer a specialized context in which to explore the intersection between voice and movement. The practice-based investigation presented in this article charts the development of an original musical work, Intangible Spaces, which gives form to the invisible aspects of voice and movement through gestural control, physical modelling synthesis and visual feedback. I draw on embodied and performative autoethnographic methods to capture the felt sensations and sound-movement associations that arise during the composition process. I also explore the performance approaches of key practitioners in the area to gain a broader understanding of the ways in which musicians leverage existing performance skills to uncover novel connections between movement and voice in gestural performance.     

The Influence of Competitiveness on SMEs Internationalization Effectiveness. Online Versus Offline Business Networking

ABSTRACT

The article aims to analyze the importance of reinforcing the relational capital of the small- and medium-sized enterprises (SMEs) in the context of a globalized market. It elaborates on the influences of competitiveness on online versus offline business networking with a view to achieve effectiveness in the European steel pipe SMEs internationalization process. The results indicate that internationalization effectiveness is indirectly influenced by competitiveness, with online and offline networking mediating the relationship between the two variables.     

Network-based innovation systems: A capital base for the Monterrey city-region,Mexico

This paper advances notions of interactive learning as one of the key drivers of the knowledge based-development perspective. The paper explores the strategic role and close relationship between social and institutional learning as critical processes in order to generate knowledge and innovation in an urban context—i.e., knowledge city. The research reported in this paper makes an account of: (i) considering a capitals system perspective for knowledge flows to add value for the development of knowledge cities and communities; (ii) learning interactive processes among actors that leverage institutional capacity within regional innovation systems, and; (iii) adopting a knowledge-based development framework for investigating the capital basis of the City of Monterrey, Mexico. This research sheds light on how knowledge-intensive elements—such as higher education institutions, research centers, firms and other local actors—are contributing community building in a knowledge-based urban context—i.e., knowledge city.     

Characteristics of software ecosystems for Federated Embedded Systems: A case study

ContextTraditionally, Embedded Systems (ES) are tightly linked to physical products, and closed both for communication to the surrounding world and to additions or modifications by third parties. New technical solutions are however emerging that allow addition of plug-in software, as well as external communication for both software installation and data exchange. These mechanisms in combination will allow for the construction of Federated Embedded Systems (FES). Expected benefits include the possibility of third-party actors developing add-on functionality; a shorter time to market for new functions; and the ability to upgrade existing products in the field. This will however require not only new technical solutions, but also a transformation of the software ecosystems for ES.ObjectiveThis paper aims at providing an initial characterization of the mechanisms that need to be present to make a FES ecosystem successful. This includes identification of the actors, the possible business models, the effects on product development processes, methods and tools, as well as on the product architecture.MethodThe research was carried out as an explorative case study based on interviews with 15 senior staff members at 9 companies related to ES that represent different roles in a future ecosystem for FES. The interview data was analyzed and the findings were mapped according to the Business Model Canvas (BMC).ResultsThe findings from the study describe the main characteristics of a FES ecosystem, and identify the challenges for future research and practice.ConclusionsThe case study indicates that new actors exist in the FES ecosystem compared to a traditional supply chain, and that their roles and relations are redefined. The business models include new revenue streams and services, but also create the need for trade-offs between, e.g., openness and dependability in the architecture, as well as new ways of working.     

Interactive Design Using the Example of a Complex Medical Application

Applications in the field of capital goods make specific requirements in terms of efficiency, fault tolerance, and verifiability-and therefore of the overall quality of use. Visual design-regarded as something derived logically from the functional stipulations and not just as an accessory to be applied later-contributes to this.

The link between functional requirement and formal design will be illustrated by using Syngo, which is a software platform for controlling the image-transmitting procedure in radiology.

In the context of the annual competition "Industrieforum Design Hannover" (Industrial Design Forum Hanover), Syngo was awarded the special "Best of Category" prize at the "Interaction Design Award 2000" (see Color Plate 11, Figures 1 and 2).     

Response preparation in a lane change task

Anticipation of future events is crucial for driving performance and safety. The aim of this study is to assess the relevance of theoretical frameworks of response preparation (response priming, movement integration theory) for driving. In a customised lane change task, valid and invalid primes were used to indicate the direction of the forthcoming lane changes. Reaction time (RT) and phase durations from steering movements served as dependent measures. In agreement with the theoretical considerations, we found a clear effect of validity on RT and steering kinematics. RTs were faster and the duration of the initial steering phase was shorter with valid than with invalid advance information. The experimental outcomes suggest that the theoretical considerations about benefits and costs of response preparation can be generalised to driving manoeuvres. Therefore, response priming paradigms might be well suited to investigate preparatory effects of advance information, e.g. in the context of advanced driving assistance systems.

Practitioner summary: Benefits and costs of response preparation were assessed in the context of driving. The findings suggest that the understanding of preparatory processes is of relevance to enhance driving performance and safety. It is possible to derive some implications that may be useful for the design of assistance and information systems.     

Comparison between satellite and ship measurements of sea surface temperature in the north-east Atlantic Ocean

Abstract

A comparison is made between in situ measurements of sea surface temperature (SST) in the north-east Atlantic Ocean, obtained during Cruise 145 of RRS Discovery, and SSTs derived from several overpasses of the Advanced Very High Resolution Radiometer on the NOAA-7 satellite during the same period in March 1984. The objective of the analysis was to test the ability of the satellite to map the detailed features of SST structure and to examine the performance of atmospheric correction and temperature calibration procedures in this context. Gross comparison between all the cloud-free ship and satellite data confirms agreement normally within 1 degC. More detailed comparison of SST structure along the cruise tracks shows that the satellite data are able to identify features with a resolution of 0.3°C.     

Social Media Use for News and Individuals' Social Capital,Civic Engagement and Political Participation

Recently, scholars tested how digital media use for informational purposes similarly contributes to foster democratic processes and the creation of social capital. Nevertheless, in the context of today's socially‐networked‐society and the rise of social media applications (i.e., Facebook) new perspectives need to be considered. Based on U.S. national data, results show that after controlling for demographic variables, traditional media use offline and online, political constructs (knowledge and efficacy), and frequency and size of political discussion networks, seeking information via social network sites is a positive and significant predictor of people's social capital and civic and political participatory behaviors, online and offline.     

Artificial neural network and fuzzy expert system comparison for prediction of performance and emission parameters on a gasoline engine

This study is deals with artificial neural network (ANN) and fuzzy expert system (FES) modelling of a gasoline engine to predict engine power, torque, specific fuel consumption and hydrocarbon emission. In this study, experimental data, which were obtained from experimental studies in a laboratory environment, have been used. Using some of the experimental data for training and testing an ANN for the engine was developed. Also the FES has been developed and realized. In this systems output parameters power, torque, specific fuel consumption and hydrocarbon emission have been determined using input parameters intake valve opening advance and engine speed. When experimental data and results obtained from ANN and FES were compared by t-test in SPSS and regression analysis in Matlab, it was determined that both groups of data are consistent with each other for p > 0.05 confidence interval and differences were statistically not significant. As a result, it has been shown that developed ANN and FES can be used reliably in automotive industry and engineering instead of experimental work.     

The role of cue utilisation in reducing the workload in a train control task

Abstract

Skilled performance has been characterised, in part, by the capacity to accurately identify and respond to patterns as cues in the environment. The outcome is a reduction in cognitive load and a greater residual capacity to undertake concurrent tasks. The present study was designed to examine the relationship between cue utilisation and temporal pattern recognition in the context of a simulated, rail control task. Sixty-one university students undertook an assessment of cue utilisation and engaged in a rail control simulation. The appearance and movement of trains followed a consistent but implicit (undisclosed) pattern. Throughout the second half of the rail task, a secondary task was included. The results indicated that participants with relatively higher cue utilisation were more likely to identify the implicit pattern of rail movements, were more accurate and responded more rapidly under increased workload conditions. The results suggest that a propensity to identify patterns as cues may provide an opportunity to reduce cognitive demands, thereby facilitating performance in a novel task. Implications for selection and system design are discussed.

Practitioner Summary: This study was designed to explain differences in the way in which people learn, particularly when tasks involve recurring patterns. Using simulated rail control, the results indicated that participants who display behaviour that is indicative of the utilisation of cues also recognise patterns in the movement of simulated trains. This enables them to manage trains more effectively, even while undertaking other tasks.     

Structural analysis of the main uraniferous chalcedony vein at G. El Missikat environs,Central Eastern Desert,Egypt

Abstract

This study deals with the structural analysis of joints in the uranium-bearing vein deposits and the surrounding enclosing granite rocks in G. El Missikat pluton, Central Eastern Desert, Egypt. The study includes the different structural features such as the intensity of joints, their opening and length for the different sets taken on a systematic pattern through and along the upper and lower contact of the uraniferous chalcedony vein, and their correspondence in the subsurface mining working.

The relative movement of the chalcedony vein is interpreted from slickensides striation along the enclosing fault plane. The analysis of microfolds along the fault planes also indicates the movement picture. Photolineaments are traced showing the main fracturing intensity trend in the granite pluton.

The study reveals that the main fracturing surface sets trend ENE-WSW and NW-SE stained by surface uranium mineralization. The first set in the subsurface mining working is found rotated anticlockwise relative to the surface and trends NE-SW, while the another set varies from the WNW-ESE to NW-SE trend.

The opening of the ENE-WSW surface trend and its subsurface corresponding NE-SW trend increases downwards. The last conclusion is good evidence for the possibility that uranium mineralization increases downwards. The subsequent compressional forces acted upon the area from the NW and NNW directions. The ENE shear fractures produced during the first movement were then rejuvenated, reopened and invaded with silica phases and uranium mineralization during the second movement.     

Feature‐Driven Visual Analytics of Chaotic Parameter‐Dependent Movement

Analyzing movements in their spatial and temporal context is a complex task. We are additionally interested in understanding the movements’ dependency on parameters that govern the processes behind the movement. We propose a visual analytics approach combining analytic, visual, and interactive means to deal with the added complexity. The key idea is to perform an analytical extraction of features that capture distinct movement characteristics. Different parameter configurations and extracted features are then visualized in a compact fashion to facilitate an overview of the data. Interaction enables the user to access details about features, to compare features, and to relate features back to the original movement. We instantiate our approach with a repository of more than twenty accepted and novel features to help analysts in gaining insight into simulations of chaotic behavior of thousands of entities over thousands of data points. Domain experts applied our solution successfully to study dynamic groups in such movements in relation to thousands of parameter configurations.     

Recognizing human gestures in videos by modeling the mutual context of body position and hands movement

In our days, due the evolution of high-speed computers, the old Human–Computer Interface (HCI) legacies based on mouse and keyboard are slowly becoming obsolete and cannot be accurate enough and respond in a timely manner to the flow of information today. This is why new ways of communicating with the computer have to be researched, the most natural one being the use of gestures. In this paper, a two-level architecture for recognizing human gestures from video frames is proposed. The architecture makes use of several feed-forward neural networks to compute the gestures based on the Haar-like features of body, hand and finger as well as a stochastic-free context grammar that is employed to comprise the mutual context between body pose and hand movement. Trained and tested on 10 gestures (Swipe Right, Swipe Left, Swipe Up, Swipe Down, Horizontal Wave, Vertical Wave, Circle, Point, Palm Up and Fist) the over 94 % accuracy of the system surpasses the current state of the art and compared with a system with no mutual context between body position and hand movement our proposed architecture shows an increase in accuracy with up to 7 %.     

Determination of the time-dependent reaction coefficient and the heat flux in a nonlinear inverse heat conduction problem

ABSTRACT

Diffusion processes with reaction generated by a nonlinear source are commonly encountered in practical applications related to ignition, pyrolysis and polymerization. In such processes, determining the intensity of reaction in time is of crucial importance for control and monitoring purposes. Therefore, this paper is devoted to such an identification problem of determining the time-dependent coefficient of a nonlinear heat source together with the unknown heat flux at an inaccessible boundary of a one-dimensional slab from temperature measurements at two sensor locations in the context of nonlinear transient heat conduction. Local existence and uniqueness results for the inverse coefficient problem are proved when the first three derivatives of the nonlinear source term are Lipschitz continuous functions. Furthermore, the conjugate gradient method (CGM) for separately reconstructing the reaction coefficient and the heat flux is developed. The ill-posedness is overcome by using the discrepancy principle to stop the iteration procedure of CGM when the input data is contaminated with noise. Numerical results show that the inverse solutions are accurate and stable.     

A turning point prediction method of stock price based on RVFL-GMDH and chaotic time series analysis

Stock market prediction is extremely important for investors because knowing the future trend of stock prices will reduce the risk of investing capital for profit. Therefore, seeking an accurate, fast, and effective approach to identify the stock market movement is of great practical significance. This study proposes a novel turning point prediction method for the time series analysis of stock price. Through the chaos theory analysis and application, we put forward a new modeling approach for the nonlinear dynamic system. The turning indicator of time series is computed firstly; then, by applying the RVFL-GMDH model, we perform the turning point prediction of the stock price, which is based on the fractal characteristic of a strange attractor with an infinite self-similar structure. The experimental findings confirm the efficacy of the proposed procedure and have become successful for the intelligent decision support of the stock trading strategy.