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This paper demonstrates an innovative application in the context of autism using a Humanoid robot. A nine-month long home-based care trial with 191 hours of interaction is conducted to investigate the engagement with two young people with autism through adaptation on service design (e.g., change from narrative voice to more interactive story telling using emotion expressions, sounds effects and background music) and service personalization (service novelty). It involves big data collected to learn the participants' preferences and activity patterns. The experimental evidence on how to improve the long-term benefits of use of social robots is reported. The research establishes that in home-based care a Humanoid robot can maintain their engagement with young people with autism through service personalization over long periods of time.

Precision nitrogen (N) management (PNM) is a promising strategy to improve N use efficiency and protect the environment while maintaining or increasing crop yield. In-season non-destructive diagnosis of crop N status is crucial for the success of this strategy. The objectives of this study were to (i) evaluate how well the GreenSeeker active canopy sensor can non-destructively estimate N status indicators of spring maize (Zea mays L.) in Northeast China and (ii) evaluate different N status diagnostic approaches based on N nutrition index (NNI) estimated via GreenSeeker sensor measurements. Two N rate field experiments involving 6 N rates (0, 60, 120,180, 240, and 300 kg N ha−1) were conducted in 2014 in Lishu County, Jilin Province in Northeast China. The results indicated that across sites and growth stages, GreenSeeker-based vegetation indices explained 89%–90% and 80%–86% of maize aboveground biomass and plant N uptake variability, respectively. The performance of GreenSeeker for estimating N status indicators from crop growth stage V7 to V10 was better than early growth stages (V5 and V6). The normalized difference vegetation index (NDVI) became saturated when aboveground biomass reached about 3.1 t ha−1 or plant N uptake reached about 75 kg ha−1; whereas no obvious saturation effect was found with ratio vegetation index (RVI). Across growth stages, about 50% of variability in maize N concentration was explained, but the standard error of estimate (SEa) was not acceptable. The NNI values were significantly correlated with GreenSeeker-based vegetation indices, with R2 being 0.64–0.80 at a specific growth stage. It is concluded that the GreenSeeker sensor has good potential for in-season non-destructive diagnosis of spring maize N status at V7–V8, but more studies are needed to further evaluate and improve its performance for practical applications.

This study presents a model inspired from a grounded theory methodology based on the successful performance of socially assistive robots in aged-care facilities. Conducting 17 in-depth interviews with nurses and scientists who worked with these robots, we investigated how social robot engagement can lead to the quality of life for aged people. The qualitative findings show 4 categories including socially assistive robots, robot mediation, service enablement and aged-care service innovation. The findings support modelling and implementing aged-care service innovation by social assistive robots.

Like most of the developed countries, Australia's population is ageing. In this paper the authors report on deployment of a socially assistive robot (Matilda) based multi-layer service architecture to support People with Dementia (PwD) in home-based care. The robots deliver a range of services in an emotionally engaging manner based on the lifestyle of PwD. The assistive social robots have been deployed in several Australian households over a six-month period. The results of trial demonstrate that multi-layer service architecture with personalized services and human-like communication modalities of Matilda have the ability of breaking technology barriers, providing sensory enrichment and social connectivity to the PwD as well as augmenting their good memories and providing respite to the partners.

Australia like most of the western world is bracing up for the looming ageing population crisis. Assistive robots are being seen as one of the ways for supporting aged care facilities building capacity and improving the quality of care of the elderly. In this paper, the authors report on three field trials of Matilda, a human like affective communication (service and companion) robot in nursing homes in Australia. The work demonstrates Matilda's ability to break technology barriers with the elderly, support nursing homes in group and one to one activities with elderly to help increase capacity of care as well as improve emotional well being of elderly to improve quality of care.

Emotional well being is one of two aspects of personal well-being that can be measured in quality of life assessments, and is a critical part of a senior citizen to help them age gracefully and remain independent as well as integrated with society. In order to improve the emotional well being of the elderly in the era of looming ageing population crisis, in this paper the authors report on design and a field trial of Matilda, a human-like assistive communication assistive robot in Australian residential care and home-based care facilities. The work demonstrates that by marrying the embodiment of care with artificial intelligence, emotion measuring techniques and positive emotion generation in its design and applications, Matilda has the ability to improve emotional well being of the elderly.

This research aims to contribute to the body of knowledge regarding the use of assistive robots for tele-health care in the home through an innovative service model. In contrast to existing tele-consultation, tele-monitoring and tele-medicine service models, the assistive robot enabled tele-health model focuses on providing holistic care (which includes reactive, proactive as well as preventative care). Additionally, unlike existing tele-health technologies it facilitates human-like engagement and reciprocal association between assistive robot and the human partner.

Emotions form an important component of human behavior and decision making. This paper reports on the embodiment of emotions and other human attributes like gestures, speech and motion in a communication robot for conducting a job interview and measuring emotional and cultural fitness of a candidate for a sales job. The contributions include enhanced information quality for managerial decision making, customization of follow up face to face interviews, and enhancement of social interaction between people and communication robot in interview situations.

The purpose of this paper is to obtain the object. In particular, we focus on the face detection. By using the proposed method, we recognize faces with a near-infrared (NIR) camera. The face detection that used images from the NIR camera is comparatively difficult to be done, because they are gray scale images. In this paper, the filter by using the GA is designed for the purpose of getting the faces. The method of detecting faces and the position from the NIR images is proposed in the on-line image. It is demonstrated that our approach is effective for vehicle driver monitoring. Finally, it was confirmed that the proposed method works well.

This paper describes a method and system for monitoring and correlating digitized non-verbal data (like emotions and eye-gaze) with other kinds of data for decision support and information personalization. This is illustrated through two applications in this paper. The first one describes detection, monitoring and correlation of emotional state changes for behavior profiling of sales candidates. The second one outlines design of eye gaze tracking system using standard web camera (instead of more expensive infra-red camera) for holiday destination selection. The paper makes unique contributions in terms of modelling as well as correlation of non-verbal data in natural environments for improving digital information quality and decision making in business areas like recruitment and tourism.

Research in cognitive psychology and learning literature clearly suggests a link between emotions and constructive learning. In this paper the authors report on role of non-verbal emotional responses for designing e-Training programs. It involves measuring arousal or lack of it as a means of pro-active diagnostic assessment of a sales management trainee and using the assessment to customize e-Training course in inter-personal communication

This paper describes the role and correlation of non-verbal data (emotions) with other data in managerial decision making in natural environment. The work is targeted towards context-aware emotionally intelligent Human Resource Management (HRM), information personalization and managerial decision making in consumer and service sector industries like finance/banking, electronics, retail, tourism and health care.

In a health care context, the use RFID (radio frequency identification) technology can be employed for not only bringing down health care costs but also facilitate automating and streamlining patient identification processes in hospitals and use of mobile devices like PDA, smart phones, for design a health care management systems. In this paper, we outline a RFID model for designing a system in the health care. An application of the architecture is described in the area of RFID-based real-time hospital patient management system (HPMS).

Emotions form an important component of human behaviour and decision making and thus are an essential part of context-aware web-based decision support systems. In this paper a conceptual model based on sensemaking, affective and situational feedback is outlined for design of context-aware web-based decision support systems. An application of the model is illustrated in the area of web-based behaviour profiling and recruitment of salespeople. In this application cognitive responses of a sales candidate to behavioural questions from a e-Sales Recruitment and Benchmarking System (e-SRBS) are mediated with their real-time emotional or affective responses to facilitate context-aware decision support in e-recruitment applications. . The work reported has implications in not only e-recruitment but also in knowledge management, data mining, web personalisation and context-aware web-based decision support systems in general.

In this paper we outline a seven layer context-aware data mining architecture which combines context, sensemaking (cognitive and affective) and data mining technologies to design adaptive context-aware data mining systems. We particularly show how cognitive constructs and emotional attitudes of a user mediate in interpretation of meaning in hidden patterns. We illustrate the role of cognitive constructs in interpreting a CRM situation by a relationship manager in a banking and finance application. We also illustrate the role of emotional attitudes as an important factor in context-aware interpretation of mined behavioral patterns in a sales recruitment and benchmarking application.

In this paper we describe a novel intelligent web based system which employs human feedback and evaluation to assist an Internet user to interactively identify the clothing of a missing person. It is novel in terms of employing GA to interactively create different shirt designs. It also illustrates human-centeredness from two perspectives. Firstly, from a multimedia information system perspective, in order to capture the user semantics it employs user feedback. Secondly, from a computational intelligence perspective the user is involved in defining objective function of a GA agent for optimizing search. The system can be used by the law enforcement authorities to identify the type, color and design of the shirt worn by a missing person. It has been tested on a small number of users with encouraging results.

Emotions form an important component of human behavior and decision making. In this paper we employ image processing and soft computing techniques to design and implement emotional state profiling of a sales candidate while they provide psychological inputs related to their selling behavior to an intelligent sales recruitment and benchmarking system. This research is part of a larger effort aimed at giving computers and other pervasive devices the skills of emotional intelligence, such as the ability to recognize a person's emotions and to respond appropriately to those emotions. The work reported has implications in not only in recruitment and benchmarking but also web recommender and information personalization systems.

This paper proposes a human-centered multi-layered multi-agent architecture for performance optimization of data mining applications. Feedback from user is used for optimizing the data mining performance. This paper describes application of the architecture in banking and finance domain

In this paper we outline a task-centered agent-oriented approach to modelling (or software engineering) of intelligent real-time systems for critical events like environmental disasters (e.g., bush fires, earthquakes), alarm processing in critical and emergency situations in power systems, and in war situations involving detection and destruction of enemy planes by fighter pilots. We model the user (e.g., operator or fighter pilot) at two distinct levels. These include the problem solving or the task level and performance optimisation level. Alarm processing is used as an application to illustrate the modelling at the task and performance optimisation levels respectively

Most existing approaches for determining serious pathological conditions involve analysis of stained images of human tissue. Recently, unstained methods have been used for classification and analysis of cells in human and mammalian tissues. The classifications accuracies have been have been quite poor. We describe a novel application of genetic algorithms for significantly improving the segmentation and classification of cells in unstained Chinese hamster ovarian image samples. The multiagent soft computing model represents a symbiotic relationship between soft computing agents like genetic algorithms, neural networks and water immersion and morphological agents for segmentation and classification of cells in unstained Chinese hamster ovarian image samples.

Nowadays, recommendation systems in e-commerce are booming because of their potential and applicability for personalized services to customers. However, recommendation is not always what customers are expecting. Odd pitches and poor matches in the system have led to outpouring of anecdotes. It means that quality control doesn't apply to the recommendation here. To cope with this problem, this paper proposes a new way to improve both the quality of rating and recommendation itself for e-recommendation system. The concepts will integrate to the implementation of our on-going e-recommendation system and the second concept is illustrated in a financial domain.

Most existing approaches for determining serious pathological conditions involve analysis of stained images of human tissue. In this paper we describe a multi-agent distributed control system model for image processing of unstained human (mammalian) cell images. The control system model develops a symbiotic relationship between soft computing agents like neural networks and water immersion and morphological agents for segmentation and classification of cells in unstained Chinese hamster ovarian image samples.

It is often known that an EEG has a personal characteristic. However, there are no researches to achieve the consideration of the personal characteristic. Then, the analyzed frequency components of the EEG have that the frequency components in which characteristics are contained significantly, and that not. Moreover, these combinations have the human equation. We think that these combinations are the personal characteristics frequency components of the EEG. In this paper, the EEG analysis method by using the GA, the FA and the NN is proposed. The GA is used for selecting the personal characteristics frequency components. The FA is used for extracting the characteristic data of the EEG. The NN is used for estimating extracted the characteristics data of the EEG. Finally, in order to show the effectiveness of the proposed method, EEG pattern was classified using computer simulations. The EEG pattern has 4 conditions, which are listening to rock music, Schmaltzy Japanese ballad music, healing music and classical music. The result, in the case of not using the personal characteristics frequency components, gave over 95% accuracy. This result of our experiment shows the effectiveness of the proposed method.

Recently in the world, many researches of personal identification method using biometrics are widely done. Especially, personal identification using the face is used because of needless for physical contact. In this paper, we propose the new method to identify the personal using the SPCA and the NN. The SPCA has been proposed in order for the PCS to achieve high-speed processing. Moreover, the personal identification system needs to have not only ability to recognize registrant correctly but also ability to reject un-registrant certainly. Therefore, we perform simulation using un-registrant. Furthermore, we analyzed and examined about the individual feature in a face by using the GA and the SPCA. Then, in order to show the effectiveness of the proposed method, we show computer simulations by suing the real image. From these results, we show the effectiveness.

Intelligent agents (or software agents) today can be seen as providing support to practitioners and problem solvers at three levels, namely, clerical, tool and task levels. In particular, we outline computational level based agent definitions of fuzzy neural agents. We show the application of these agents in determining faulty components in a electrical power system network.

An integrated model for real time alarm processing in a real world terminal power station is applied. The integrated model is a combination of a generic neuro-expert system model, object model, and UNIX operating system process (UOSP) model. It is shown how the massive parallelism and fast execution features of ANNs help to cope with real-time system constraints like data variability and fast response time. For further enhancing reliability, a practical use of competing expert system-artificial neural networks (ES-ANN) objects is proposed. >

We explain the different approaches to fusing neural networks and symbolic processing aspects. Four categories are distinguished namely fusion, transformation, combination and associative systems. A brief description of each is given. We also briefly describe the GEMSES architecture as an example of an associative system which has been built from philosophical, cognitive, practical viewpoints.

Intelligent Multi-Agent Hybrid Distributed Architecture (IMAHDA) for complex systems has been defined at the task structure level and the computational level. IHMADA has been built around five information processing phases. The five phases represent a deliberate as well as automated or reflexive reasoning structure. This paper outlines some aspects of the computational level of IMAHDA. The computational level is derived from four layers, namely, object layer, software agent layer, intelligent agent layer, and problem solving agent layer. Agent description in terms of percepts, goals, actions, and environment for some of the problem solving agents is also outlined. IMAHDA has been applied in engineering (real time systems) and management areas.

In this paper we outline an intelligent hybrid multi-agent architecture for engineering complex systems. The hybrid multi-agent architecture is described at the task structure level. The architecture has been successfully applied in a real time alarm processing and fault diagnosis system in a power system control centre with good performance results.

In this paper, we show the relevance of fuzzy systems in an integrated symbolic and subsymbolic architecture for information processing. We also show how fuzzy logic can be used to represent knowledge in the integrated architecture. We end up with an integrated architecture of knowledge based, connectionist and fuzzy systems for information processing and knowledge modelling. The integrated information processing architecture assists the problem solver in determining the relevance of the three computational paradigms, and their strategy for integrating them in problem solving.

Knowledge based systems, fuzzy systems, and artificial neural networks (ANN) are the three most widely used computational paradigms for emulating different aspects of human cognition like information processing, knowledge representation, and learning. A proper integration of these three paradigms can help to realize powerful problem solving strategies especially for large data intensive domains. In this direction we have developed a generic architecture for integration of symbolic (knowledge based and fuzzy) and connectionist (ANN) systems for large, data intensive domains at the task structure level, computational (symbol) level, and the program level. In this paper we outline the knowledge modeling aspects of the integrated symbolic (knowledge based and fuzzy)-connectionist architecture. The knowledge content of the architecture can facilitate a problem solver in modeling the knowledge required for using as well as integrating the three intelligent paradigms.