A model driven engineering process of platform neutral agents for ambient intelligence devices

Ambient intelligence (AmI) systems are now considered a promising approach to assist people in their daily life. AmI proposes the development of context aware systems equipped with devices that can recognize your context and act accordingly. Agents provide an effective way to develop such systems since agents are reactive, proactive and exhibit an intelligent and autonomous behavior. However, current agent approaches do not adequately fulfill the requirements posed by AmI systems. From a modeling point of view, the aim should be to help in the design by providing adequate tools that assist in the development of important properties of AmI systems, such as context-awareness; and from an implementation point of view, agent technologies must be adapted to the diversity of AmI devices and communication technologies. As a solution to these issues we propose a Model driven engineering process, which supports the automatic generation of agent-based AmI systems. The source metamodel is PIM4Agents, a general purpose agent metamodel that we have adapted to support the explicit modeling of context aware systems, and the target metamodel is Malaca, an aspect-oriented agent architecture. Aspect-orientation makes Malaca platform-neutral for FIPA compliant agent platforms, simplifying the model driven process. The solution generates MalacaTiny agents, an implementation of Malaca that is able to run in AmI devices. We have evaluated the convenience of applying a model driven approach by measuring the degree of automation of our process and we have evaluated MalacaTiny for mobile phones by assessing different parameters, related to the scarcity of resources in AmI systems. All the results obtained are satisfactory.     

RecAm: a collaborative context-aware framework for multimedia recommendations in an ambient intelligence environment

With an ever-increasing accessibility to different multimedia contents in real-time, it is difficult for users to identify the proper resources from such a vast number of choices. By utilizing the user’s context while consuming diverse multimedia contents, we can identify different personal preferences and settings. However, there is a need to reinforce the recommendation process in a systematic way, with context-adaptive information. The contributions of this paper are twofold. First, we propose a framework, called RecAm, which enables the collection of contextual information and the delivery of resulted recommendation by adapting the user’s environment using Ambient Intelligent (AmI) Interfaces. Second, we propose a recommendation model that establishes a bridge between the multimedia resources, user joint preferences, and the detected contextual information. Hence, we obtain a comprehensive view of the user’s context, as well as provide a personalized environment to deliver the feedback. We demonstrate the feasibility of RecAm with two prototypes applications that use contextual information for recommendations. The offline experiment conducted shows the improvement of delivering personalized recommendations based on the user’s context on two real-world datasets.     

Bringing context-aware access to the web through spoken interaction

The web has become the largest repository of multimedia information and its convergence with telecommunications is now bringing the benefits of web technology to hand-held devices. To optimize data access using these devices and provide services which meet the user needs through intelligent information retrieval, the system must sense and interpret the user environment and the communication context. In addition, natural spoken conversation with handheld devices makes possible the use of these applications in environments in which the use of GUI interfaces is not effective, provides a more natural human-computer interaction, and facilitates access to the web for people with visual or motor disabilities, allowing their integration and the elimination of barriers to Internet access. In this paper, we present an architecture for the design of context-aware systems that use speech to access web services. Our contribution focuses specifically on the use of context information to improve the effectiveness of providing web services by using a spoken dialog system for the user-system interaction. We also describe an application of our proposal to develop a context-aware railway information system, and provide a detailed evaluation of the influence of the context information in the quality of the services that are supplied.     

Ambient intelligence platform using multi-agent system and mobile ubiquitous hardware

In this paper, a novel ambient intelligence (AmI) platform is proposed to facilitate fast integration of different control algorithms, device networks and user interfaces. This platform defines the overall hardware/software architecture and communication standards. It consists of four layers, namely the ubiquitous environment, middleware, multi-agent system and application layer. The multi-agent system is implemented using Java Agent DEvelopment (JADE) framework and allows users to incorporate multiple control algorithms as agents for managing different tasks. The Universal Plug and Play (UPnP) device discovery protocol is used as a middleware, which isolates the multi-agent system and physical ubiquitous environment while providing a standard communication channel between the two. An XML content language has been designed to provide standard communication between various user interfaces and the multi-agent system. A mobile ubiquitous setup box is designed to allow fast construction of ubiquitous environments in any physical space. The real time performance analysis shows the potential of the proposed AmI platform to be used in real-life AmI applications. A case study has also been carried out to demonstrate the possibility of integrating multiple control algorithms in the multi-agent system and achieving a significant improvement on the overall offline learning performance.     

Ontology-based modeling of dynamic ubiquitous computing applications as evolving activity spheres

As the computer disappears in the environments surrounding our activities, the objects therein become augmented with sensors, actuators, processors, memories, wireless communication modules and they can receive, store, process and transmit information. In addition to objects, spaces also undergo a change towards becoming smart and eventually Ambient Intelligence (AmI) spaces. In order to model the way everyday activities are carried out within an AmI environment, we introduce the notion of “activity sphere”. In this paper, we are interested in the ontology-based representation of activity spheres from two different perspectives (as creators and as observers), as well as the modeling and control of the dynamic nature of activity spheres.     

Providing universally accessible interactive services through TV sets: implementation and validation with elderly users

One of the challenges that Ambient Intelligence (AmI) faces is the provision of a usable interaction concept to its users, especially for those with a weak technical background. In this paper, we describe a new approach to integrate interactive services provided by an AmI environment with the television set, which is one of the most widely used interaction client in the home environment. The approach supports the integration of different TV set configurations, guaranteeing the possibility to develop universally accessible solutions. An implementation of this approach has been carried out as a multimodal/multi-purpose natural human computer interface for elderly people, by creating adapted graphical user interfaces and navigation menus together with multimodal interaction (simplified TV remote control and voice interaction). In addition, this user interface can also be suited to other user groups. We have tested a prototype that adapts the videoconference and the information service with a group of 83 users. The results from the user tests show that the group found the prototype to be both satisfactory and efficient to use.     

Ambient intelligence: Technologies, applications, and opportunities

Ambient intelligence is an emerging discipline that brings intelligence to our everyday environments and makes those environments sensitive to us. Ambient intelligence (AmI) research builds upon advances in sensors and sensor networks, pervasive computing, and artificial intelligence. Because these contributing fields have experienced tremendous growth in the last few years, AmI research has strengthened and expanded. Because AmI research is maturing, the resulting technologies promise to revolutionarize daily human life by making people’s surroundings flexible and adaptive.In this paper, we provide a survey of the technologies that comprise ambient intelligence and of the applications that are dramatically affected by it. In particular, we specifically focus on the research that makes AmI technologies “intelligent”. We also highlight challenges and opportunities that AmI researchers will face in the coming years.     

Opportunities and challenges in improving surgical work flow

Safe and efficient surgical operations depend on a work environment larger than the individual operating room (OR) and on communications at different levels of the hospital organization. Extensive communication is needed before and during surgery to ensure that surgical rooms, equipment, and supplies; patients; surgeons; supporting personnel; and accompanying documentation are all ready at the appropriate times. In this article, we compile the results of three of our studies of communication activities outside ORs, with the goal of identifying opportunities and challenges in the workflow of surgery. The first study demonstrates the amount of communication work performed by OR coordinators. The second study demonstrates the potential functions of a whiteboard in communication. The third study describes an organizational learning strategy of proactive event reporting. We apply computer-supported cooperative work and organizational learning concepts to the findings and suggest ways in which information technology may improve surgical workflow.     

Combining the real world with simulations for a robust testing of Ambient Intelligence services

This paper proposes a general architecture for testing, validating and verifying Ambient Intelligence (AmI) environments: AmISim. The development of AmI is a very complex task because this technology must often adapt to contextual information as well as unpredictable behaviours and environmental features. The architecture presented deals with AmI applications in order to cover the different components of these kinds of systems: environment, users, context and adaptation. This architecture is the first one that is able to cover all these features, which are needed in a full AmI system. The paper shows that AmISim is able to cover a complete AmI system and to provide a framework which can test scenarios that would be impossible to test in real environments or even with previous simulation approaches. Simulated and real elements coexist in AmISim for a robust testing, validation and verification of the AmI systems, which provide an easier and less costly deployment.     

A design-and-play approach to accessible user interface development in Ambient Intelligence environments

User interface development in Ambient Intelligence (AmI) environments is anticipated to be a particularly complex and programming intensive endeavor. Additionally, AmI environments should ensure accessibility and usability of interactive technologies by users with different characteristics and requirements in a mainstream fashion. Therefore, appropriate user interface development methods and tools are required, capable of both reducing development efforts and ‘injecting’ accessibility issues into AmI applications from the early design stages. This paper introduces two tools, named AmIDesigner and AmIPlayer, which have been specifically developed to address the above challenges through automatic generation of accessible Graphical User Interfaces in AmI environments. The combination of these two tools offers a simple and rapid design-and-play approach, and the running user interfaces produced integrate non-visual feedback and a scanning mechanism to support accessibility. AmIDesigner and AmIPlayer have been evaluated to assess their usability by designers, and have been put to practice in the redevelopment of a light control application in a smart environment as a case study demonstrating the viability of the design-and-play approach. The results confirm the usefulness and usability of the tools themselves. Overall, the proposed approach has the potential to contribute significantly to the development, up-take and user acceptance of AmI technologies in the home environment.     

UTILIZING CONTEXT HISTORY TO PROVIDE DYNAMIC ADAPTATIONS

In order to provide “intimate” and “dynamic” adaptations under Weiser's vision for ubiquitous computing environments, we propose the utilization of context history together with user modeling and machine learning techniques. Our approach supports proactive adaptations by inducing patterns of user behavior. In addition, we support the requirement for enabling the user to receive an explicit and understandable explanation when a proactive adaptation occurs in order to encourage a trust relationship between the user and the context-aware system. In this article, we describe an experiment to examine the feasibility of our approach for supporting proactive adaptations in the domain of an intelligent office environment. The initial results of our experiment are promising and demonstrate how our system could gradually learn the user's preferences for controling his office environment by making inductions from the context history. Based on these initial findings, we believe that context history has a concrete role to play in supporting proactive adaptation in a ubiquitous computing environment.     

Using shared representations to improve coordination and intent inference

In groupware, users must communicate about their intentions and aintain common knowledge via communication channels that are explicitly designed into the system. Depending upon the task, generic communication tools like chat or a shared whiteboard may not be sufficient to support effective coordination. We have previously reported on a methodology that helps the designer develop task specific communication tools, called coordinating representations, for groupware systems. Coordinating representations lend structure and persistence to coordinating information. We have shown that coordinating representations are readily adopted by a user population, reduce coordination errors, and improve performance in a domain task. As we show in this article, coordinating representations present a unique opportunity to acquire user information in collaborative, user-adapted systems. Because coordinating representations support the exchange of coordinating information, they offer a window onto task and coordination-specific knowledge that is shared by users. Because they add structure to communication, the information that passes through them can be easily exploited by adaptive technology. This approach provides a simple technique for acquiring user knowledge in collaborative, user-adapted systems. We document our application of this approach to an existing groupware system. Several empirical results are provided. First, we show how information that is made available by a coordinating representation can be used to infer user intentions. We also show how this information can be used to mine free text chat for intent information, and show that this information further enhances intent inference. Empirical data shows that an automatic plan generation component, which is driven by information from a coordinating representation, reduces coordination errors and cognitive effort for its users. Finally, our methodology is summarized, and we present a framework for comparing our approach to other strategies for user knowledge acquisition in adaptive systems.     

Design and evaluation of a gamified system for ERP training

Enterprise Resource Planning Systems (ERP) facilitate the flow of information within a company by storing data in common databases. These systems offer a holistic view of the organization because they reduce information redundancy, offer information in real time, help with process standardization, and improve information flow and communication among employees. Nevertheless, the benefits attributed to an ERP implementation can be lost without an effective user training. Previous studies have observed that common training mechanisms don't provide meaningful learning to users, and that user satisfaction rates range from neutral to low. Therefore, in this paper we study the effects that gamification has on ERP training concerning user learning and user satisfaction. Gamification is the use of game elements and game design techniques in non-gaming environments. Several applications prove that gamified systems increase user engagement and performance. Our hypotheses were that a gamified system for ERP training improve user learning and user satisfaction levels during the training period. To test our hypotheses, we designed and evaluated a gamified system. The results showed that users trained using a gamified system performed better than those trained using a conventional, non-gamified, training mechanism.     

Investigating Knowledge Management Factors Affecting Chinese ICT Firms Performance: An Integrated KM Framework

This article sets out to investigate the critical factors of Knowledge Management (KM) which are considered to have an impact on the performance of Chinese information and communication technology (ICT) firms. This study confirms that the cultural environment of an enterprise is central to its success in the context of China. It shows that a collaborated, trusted, and learning environment within ICT firms will have a positive impact on their KM performance.     

Context-aware recommendations through context and activity recognition in a mobile environment

The mobile Internet introduces new opportunities to gain insight in the user’s environment, behavior, and activity. This contextual information can be used as an additional information source to improve traditional recommendation algorithms. This paper describes a framework to detect the current context and activity of the user by analyzing data retrieved from different sensors available on mobile devices. The framework can easily be extended to detect custom activities and is built in a generic way to ensure easy integration with other applications. On top of this framework, a recommender system is built to provide users a personalized content offer, consisting of relevant information such as points-of-interest, train schedules, and touristic info, based on the user’s current context. An evaluation of the recommender system and the underlying context recognition framework shows that power consumption and data traffic is still within an acceptable range. Users who tested the recommender system via the mobile application confirmed the usability and liked to use it. The recommendations are assessed as effective and help them to discover new places and interesting information.     

Personalization for unobtrusive service interaction

Increasingly, mobile devices play a key role in the communication between users and the services embedded in their environment. With ever greater number of services added to our surroundings, there is a need to personalize services according to the user needs and environmental context avoiding service behavior from becoming overwhelming. In order to prevent this information overload, we present a method for the development of mobile services that can be personalized in terms of obtrusiveness (the degree in which each service intrudes the user’s mind) according to the user needs and preferences. That is, services can be developed to provide their functionality at different obtrusiveness levels depending on the user by minimizing the duplication of efforts. On the one hand, we provide mechanisms for describing the obtrusiveness degree required for a service. On the other hand, we make use of Feature Modeling techniques in order to define the obtrusiveness level adaptation in a declarative manner. An experiment was conducted in order to put in practice the proposal and evaluate the user acceptance for the personalization capabilities provided by our approach.     

基于深度学习的情境感知推荐系统研究进展

传统的推荐系统存在数据高度稀疏、冷启动及用户偏好建模难等问题,而把情境信息融入推荐系统中能有效缓解此类问题.深度学习技术已经成为人工智能领域研究热点,把深度学习应用在情境感知推荐系统当中,为推荐领域的研究带来新的机遇与挑战.本文从情境感知推荐系统相关概念出发,综合整理国内外研究相关文献,介绍深度学习技术融入情境感知推荐系统相关应用模型,提出了基于深度学习的情境感知推荐系统研究的不足以及对未来的展望.     

An affiliated search system for an electronic commerce and software component architecture

This paper describes an Electronic Commerce Goods Search System (ECGSS) that has functions that increase the precision of search results through training of the search system and uses affiliated business transaction processes. The software component architecture for ECGSS also allows the effective deployment of the system on every local business site, in view of the evolving trend in information technology toward easier configuration and re-usability. A general information gathering system with the infrastructure to accept every Internet communication protocol and access control is described. In this affiliated business transaction model, we classify Internet sites into two groups: a cooperative sites group, and a non-cooperative sites group. While designing the components, we optimized their specifications with respect to the whole architecture analysis, dependencies, and interface types using a component-based software development process. Experiments on the effectiveness of the user training function for the search system and the response time for simple queries for each communication protocol are presented. Comparisons of commercial search solutions and architectural standards of several organizations are also given.     

Modeling the user state for context-aware spoken interaction in ambient assisted living

Ambient Assisted Living (AAL) systems must provide adapted services easily accessible by a wide variety of users. This can only be possible if the communication between the user and the system is carried out through an interface that is simple, rapid, effective, and robust. Natural language interfaces such as dialog systems fulfill these requisites, as they are based on a spoken conversation that resembles human communication. In this paper, we enhance systems interacting in AAL domains by means of incorporating context-aware conversational agents that consider the external context of the interaction and predict the user’s state. The user’s state is built on the basis of their emotional state and intention, and it is recognized by means of a module conceived as an intermediate phase between natural language understanding and dialog management in the architecture of the conversational agent. This prediction, carried out for each user turn in the dialog, makes it possible to adapt the system dynamically to the user’s needs. We have evaluated our proposal developing a context-aware system adapted to patients suffering from chronic pulmonary diseases, and provide a detailed discussion of the positive influence of our proposal in the success of the interaction, the information and services provided, as well as the perceived quality.     

Application of context-aware and personalized recommendation to implement an adaptive ubiquitous learning system

Advances in wireless networking, mobile broadband Internet access technology as well as the rapid development of ubiquitous computing means e-learning is no longer limited to certain settings. A ubiquitous learning (u-learning) system must however not only provide the learner with learning resources at any time and any place. However, it must also actively provide the learner with the appropriate learning assistance for their context to help him or her complete their e-learning activity. In the traditional e-learning environment, the lack of immediate learning assistance, the limitations of the screen interface or inconvenient operation means the learner is unable to receive learning resources in a timely manner and incorporate them based on the actual context into the learner’s learning activities. The result is impaired learning efficiency. Though developments in technology have overcome the constraints on learning space, an inability to appropriately exploit the technology may make it an obstacle to learning instead. When integrating the relevant information technology to develop a u-learning environment, it is therefore necessary to consider the personalization requirements of the learner to ensure that the technology achieves its intended result. This study therefore sought to apply context aware technology and recommendation algorithms to develop a u-learning system to help lifelong learning learners realize personalized learning goals in a context aware manner and improve the learner’s learning effectiveness.