A social approach to context-aware retrieval

We present a general purpose solution to Web content and services perusal by means of mobile devices, named Social Context-Aware Browser. This is a novel approach for information access based on users’ context, that exploits social and collaborative models to overtake the limits of the existing solutions. Instead of relying on a pool of experts and on a rigid categorization, as it is usually done in the context-aware field, our solution allows the crowd of users to model, control, and manage the contextual knowledge through collaboration and participation. To have a dynamic and user-tailored context representation, and to enhance the process of retrieval based on users’ actual situation, the community of users is encouraged to define the contexts of interest, to share, use, and discuss them, and to associate context to content and resources (Web pages, services, applications, etc.). This paper provides an overall presentation of our solution, describing the idea, the implementation, and the evaluation through a benchmark based methodology.     

Context modelling and a context-aware framework for pervasive service creation: A model-driven approach

Pervasive service creation entails a complex process that involves a diversity of development aspects. Context-awareness is an important facet of pervasive service creation, which deals with the acquisition, rendering, representation and utilisation of context information. In this paper we tackle context-awareness at the application level dealing with the representation and utilisation of context by services. We propose a model-driven approach that facilitates the creation of a context modelling framework and simplifies the design and implementation of pervasive services. To conclude, we demonstrate the benefits of our model-driven approach via the creation of a pervasive museum service and its evaluation using selected software metrics.     

A context-aware approach to automated negotiation using reinforcement learning

Agents negotiate depending on individual perceptions of facts, events, trends and special circumstances that define the negotiation context. The negotiation context affects in different ways each agent’s preferences, bargaining strategies and resulting benefits, given the possible negotiation outcomes. Despite the relevance of the context, the existing literature on automated negotiation is scarce about how to account for it in learning and adapting negotiation strategies. In this paper, a novel contextual representation of the negotiation setting is proposed, where an agent resorts to private and public data to negotiate using an individual perception of its necessity and risk. A context-aware negotiation agent that learns through Self-Play and Reinforcement Learning (RL) how to use key contextual information to gain a competitive edge over its opponents is discussed in two levels of temporal abstraction. Learning to negotiate in an Eco-Industrial Park (EIP) is presented as a case study. In the Peer-to-Peer (P2P) market of an EIP, two instances of context-aware agents, in the roles of a buyer and a seller, are set to bilaterally negotiate exchanges of electrical energy surpluses over a discrete timeline to demonstrate that they can profit from learning to choose a negotiation strategy while selfishly accounting for contextual information under different circumstances in a data-driven way. Furthermore, several negotiation episodes are conducted in the proposed EIP between a context-aware agent and other types of agents proposed in the existing literature. Results obtained highlight that context-aware agents do not only reap selfishly higher benefits, but also promote social welfare as they resort to contextual information while learning to negotiate.     

A Galois lattice approach to a context-aware privacy negotiation service

Online privacy has consistently been a major concern for customers that has grown commensurately with the growth of e-services. Service providers have responded by making their privacy policies clearer for customers; however, most providers use legacy systems that are unable to actually change and adapt to user’s concerns, which can lead to fewer customers using the system. Hence, the purpose of this paper is to propose a context-aware privacy policy negotiation service. To do so, we adopt a Galois lattice theory to generate policy concepts embedded in e-services. Based on the Galois lattice, we develop a process for generating privacy policy rules. To show the feasibility of the ideas proposed in this paper, we perform a simulation test with two different online auction sites as an illustrative case in terms of two metrics: the number of rules generated and success throughput. Desirable features in applying the Galois lattice approach to context-aware privacy policy negotiation service are discussed.     

A model-driven development approach for context-aware systems

The widespread usage of various types of computer devices with different platform characteristics created a need for new methods and tools to support the development of context-aware applications capable of dynamically adapting themselves to context changes. In this paper, we present a new model-based approach that addresses the development of context-aware applications from both the theoretical and practical perspectives and that supports all development phases of context-aware systems. On the one hand, we describe how our approach is applied to dynamically capture, observe the change of the context and notify the system at runtime. On the other hand, we show how our approach is used by programmers to develop a context- aware application.     

Verifying workflow processes: a transformation-based approach

Workflow modeling is a challenging activity and designers are likely to introduce errors, especially in complex industrial processes. Effective process verification is essential at design time because the cost of fixing errors during runtime is substantially higher. However, most user-oriented workflow modeling languages lack formal semantics that hinders such verification. In this paper, we propose a generic approach based on the model transformation to verify workflow processes. The model transformation includes two steps: first, it formalizes the desirable semantics of each modeling element; secondly, it translates a workflow process with clear semantics to an equivalent Petri net. Thus, we can verify the original workflow process using existing Petri net theory and analysis tools. As a comprehensive case study, verifying workflow processes in an industrial modeling language (TiPLM) is presented. Experimental evaluations on verifying real-world business processes validate our approach.     

A knowledge engineering approach to developing mindtools for context-aware ubiquitous learning

Recent developments in computing and mobile technologies have enabled the mobile and ubiquitous learning approach, which situates students in an environment that combines real-world and digital-world learning resources. Although such an approach seems to be innovative and interesting, several problems have been revealed when applying it to practical learning activities. One major problem is owing to the lack of proper learning strategies or tools that can guide or assist the students to learn in such a complex learning scenario. Students might feel excited or interested when using the mobile devices to learn in the real world; however, their learning achievements could be disappointing. To cope with this problem, in this study, a knowledge engineering approach is proposed to develop Mindtools for such innovative learning scenarios. Experimental results from a natural science course of an elementary school show that this innovative approach not only enhances learning motivation, but also improves the learning achievements of the students.     

A context-aware video prompt approach to improving students' in-field reflection levels

Engaging students in reflection during real-world observation and inquiry activities has been recognized as an important issue for improving students' learning achievement and motivation. Many studies in the literature have adopted text-based reflection prompts as an important strategy to promote learning performance, this study argues that using videos as a reflection prompt would be more effective for improving learners' ubiquitous learning experiences and can provide a more sensible scenario that is better fitting the real-world contexts than the text-based prompts. In this study, a context-aware video-based prompt approach is proposed for improving students' reflection levels and satisfaction by providing timely and personalized guidance using mobile, wireless communication and sensing technologies. An experiment was conducted for evaluating the effectiveness of the proposed video-based prompt strategy with 70 participants in two groups. The results show that learners' reflection levels were significantly improved with the use of the proposed approach and that positive attitudes were observed toward the use of video-based prompts in the context-aware ubiquitous learning environment.     

A social network approach to resolving group-level conflict in context-aware services

Group conflict that occurs when delivering context-aware services negatively affects service usage within a specific service space, and researchers have proposed few conflict resolution methods to address the issue. This paper presents a methodology to autonomously resolve the conflict arising between users in identical contexts, such as place and computing resources. To do so, a two-phase case-based reasoning method is proposed. To minimize privacy concerns, a social network model is applied to produce key social context data, secondary to personal data, and which is used in place of the users’ personal data for case-based reasoning. To show the feasibility of the idea proposed in this paper, a prototype system with simulation-based validation is tested to determine conflict resolution strategies and a simulation test has been performed.     

A sketch-based approach to human body modelling

In this paper, we present a new approach and a novel interface, Virtual Human Sketcher (VHS), which enables those who can draw, to sketch-out various human body models. Our approach supports freehand drawing input and a “Stick Figure→Fleshing-out→Skin Mapping” modelling pipeline. Following this pipeline, a stick figure is drawn first to illustrate a figure pose, which is automatically reconstructed into 3D through a “Multi-layered Back-Front Ambiguity Clarifier”. It is then fleshed-out with freehand body contours. A “Creative Model-based Method” is developed for interpreting the body size, shape, and fat distribution of the sketched figure and transferring it into a 3D human body through graphical comparisons and generic model morphing. The generic model is encapsulated with three distinct layers: skeleton, fat tissue, and skin. It can be transformed sequentially through rigid morphing, fatness morphing, and surface matching to match the 2D figure sketch. The initial resulting 3D body model can be incrementally modified through sketching directly on the 3D model. In addition, this body surface can be mapped onto a series of posed stick figures to be interpolated as a 3D character animation. VHS has been tested by various users on Tablet PC. After minimal training, even a beginner can create plausible human bodies and animate them within minutes.     

A cognitive-experiential approach to modelling web navigation

Flow experience, the degree to which a person feels involved in a particular activity, is an important influence on human–computer interaction. Building on Guo and Poole’s (2009) model of flow experience in Web navigation, and van Schaik and Ling's (in press) cognitive-experiential approach to modelling interaction experience, this research demonstrates the crucial role of the preconditions of flow experience in human–computer interaction. In an experiment, the preconditions of flow experience – but not flow experience proper – mediated the effects of artefact complexity, task complexity and intrinsic motivation (as a situation-specific trait) on both flow and task outcome. However, preconditions did not predict overall artefact evaluation. Within a staged model of flow experience, the broader implications of this work for human–computer interaction are explored.     

以儿童为中心的情景式人机交互方法

通过分析儿童绘画过程中的心理过程和行为特点,提出了一种以儿童为中心的情景式人机交互方法,并成功应用到计算机辅助儿童绘画系统中.该系统采用三维技术模拟现实中的绘画场景,并且集合了笔、语音以及摄像头等多通道交互技术以提高交互效率.系统通过提供写生、临摹等功能,让儿童在享受绘画乐趣的同时提高绘画技能.     

A context-aware approach for trustworthy worker selection in social crowd

Crowdsourcing applications like Amazon Mechanical Turk (AMT) make it possible to address many difficult tasks (e.g., image tagging and sentiment analysis) on the internet and make full use of the wisdom of crowd, where worker quality is one of the most crucial issues for the task owners. Thus, a challenging problem is how to effectively and efficiently select the high quality workers, so that the tasks online can be accomplished successfully under a certain budget. The existing methods on the crowd worker selection problem mainly based on the quality measurement of the crowd workers, those who have to register on the crowdsourcing platforms. With the connect of the OSNs and the crowdsourcing applications, the social contexts like social relationships and social trust between participants and social positions of participants can assist requestors to select one or a group of trustworthy crowdsourcing workers. In this paper, we first present a contextual social network structure and a concept of Strong Social Component (SSC), which emblems a group of workers who have high social contexts values. Then, we propose a novel index for SSC, and a new efficient and effective algorithm C-AWSA to find trustworthy workers, who can complete the tasks with high quality. The results of our experiments conducted on four real OSN datasets illustrate that the superiority of our method in trustworthy worker selection.     

A new architecture for transformation-based generators

A challenge of many transformation-based generators is that they are trying to achieve three mutually antagonistic goals simultaneously: 1) deeply factored operators and operands to gain the combinatorial programming leverage provided by composition, 2) high-performance code in the generated program, and 3) small (i.e., practical) generation search spaces. The anticipatory optimization generator (AOG) has been built to explore architectures and strategies that address this challenge. The fundamental principle underlying all of AOG's strategies is to solve separate, narrow, and specialized generation problems by strategies that are narrowly tailored to specific problems rather than a single, universal strategy aimed at all problems. A second fundamental notion is the preservation and use of domain-specific information as a way to gain extra leverage on generation problems. This paper focuses on two specific mechanisms: 1) Localization: the generation and merging of implicit control structures and 2) Tag-directed transformations: a new control structure for transformation-based optimization that allows differing kinds of retained domain knowledge (e.g., optimization knowledge) to be anticipated, affixed to the component parts in the reuse library, and triggered when the time is right for its use.     

GAMS: A stylistic approach to economic modelling

GAMS (General Algebraic Modelling System) is a software package which will make numerical optimization a much simpler endeavor for specialists from a wide range of disciplines. The need for writing personal fortran codes for optimizers and unfriendly fixed-formatfortran type input files is no longer necessary. This review gives an overall view of GAMS' capabilities and uses in the field of numerical optimization.     

A linked data approach to multi-scale energy modelling

Interactions between built infrastructure are complex and nuanced; changes to any one component can have disproportionate effects on the system as a whole. For instance, adoption of heat pumps or electric vehicles by a significant proportion of a population in an urban centre would place new demands on both electricity transmission and distribution networks. It is essential that planners – both national and local – can understand and share information about the resource demands that this type of change places on national and local infrastructure. Access to integrated sources of information – from building component to national levels – is key to supporting policy makers and decision takers. However, over time, information – and as a consequence, the software that manages it – has evolved into functional silos; this has, in turn, affected the definition of data exchange standards. This limits the ability of experts in functional areas to exchange data and implement broader decision support systems. This paper describes the use of linked data approaches to permit queries across large, diverse information sources to provide reasoning about complex questions at multiple scales. The methodology defines a central context to which various external sources can be attached. These distributed sources are, in themselves, registered in a central catalogue; they remain, however, under the control of their source organisations. In this way a large, extensible, interconnected network of distributed data describing, for example, a built environment or electricity transmission network; this network of data resources can be queried centrally to provide customised views of subsets of the data, and so provide a richer view than one source in isolation. The approach was applied to prepare and integrate information about Ireland’s transmission grid and administrative boundaries, along with domestic housing stock into a single data source. The resulting data network is queried by a scenario exploration tool. This tool successfully allows analysis, at a national level by economists, of the effects of the adoption of new technologies on the national grid of Ireland.     

A functional approach to modelling of manufacturing processes

A functional model of batch processes is formulated. The model was derived from considerations of multi-stage decision processes when some degree of optimization is required. Consistent with system theory and dynamic programming techniques, the model was derived by considering the overall system output and working backwards in time, or order of command priority. To facilitate this mode of thinking the process was divided into two basic functions, enabling the final stage to be treated in a linear, autonomous way. Application of the definition of optimality indicated that the final stage of the model is of paramount importance and produces a logical starting point for mathematical and managerial decision making policies.     

A Quasi-ARMAX approach to modelling of non-linear systems

This paper proposes a class of quasi-ARMAX models for non-linear systems. Similar to ordinary non-linear ARMAX models, the quasi-ARMAX models are flexible black-box models, but they have various linearity properties similar to those of linear ARMAX models. A modelling scheme is introduced to construct models consisting of two parts: a macro-part and a kernel-part. By using Taylor expansion and other mathematical transformation techniques, it is first constructed as a class of quasi-ARMAX interfaces (macro-parts) that have various linearity properties but contain some complicated coefficients. MIMO neurofuzzy models (kernel-parts) are then introduced to represent the complicated coefficients. It is shown that the proposed quasi-ARMAX models have both good approximation ability and some easy-to-use properties. The proposed models have been successfully applied to prediction, fault detection and adaptive control of non-linear systems.     

A context-aware reminder system for elders based on fuzzy linguistic approach

To date, several reminder systems for elders have been developed, but when and how to prompt the reminding message has not been fully explored yet. This paper presents a context-aware system that provides reminding messages for elders based on fuzzy linguistic model in order to properly deliver a reminder in an appropriate time and way. By separating the user activity contexts and contexts utilized to trigger a reminder, it is much easier for the elderly or their care givers to schedule and maintain reminders. We first adopt a fuzzy linguistic model to determine the prompting level based on the interrupt degree of user current activity and the urgent level of to-be-prompt reminder. An adaptive mapping strategy is then presented to transfer the prompting level into machine-readable parameters. Finally, the proposed approach is verified through our system prototype and experiments.