Behavioral adaptation of information systems through goal models

Customizing software to perfectly fit individual needs is becoming increasingly important in information systems engineering. Users want to be able to customize software behavior through reference to terms familiar to their diverse needs and experience. We present a requirements-driven approach to behavioral customization of software systems. Goal models are constructed to represent alternative behaviors that users can exhibit to achieve their goals. Customization information is then added to restrict the space of possibilities to those that fit specific users, contexts, or situations. Meanwhile, elements of the goal models are mapped to units of source code. This way, customization preferences posed at the requirements level are directly translated into system customizations. Our approach, which we apply to an on-line shopping cart system and an automated teller machine simulator, does not assume adoption of a particular development methodology, platform, or variability implementation technique and keeps the reasoning computation overhead from interfering with the execution of the configured application.     

A task-centered approach for user modeling in a hypermedia office documentation system

The development of user-adaptive systems is of increasing importance for industrial applications. User modeling emerged from the need to represent in the system knowledge about the user in order to allow informed decisions on how to adapt to match the user's needs. Most of the research in this field, however, has been theoretical, top-down. Our approach, in contrast, was driven by the needs of the application and shows features of bottom-up, user-centered design.We have implemented a user modeling component supporting a task-based interface to a hypermedia information system for hospitals and tested it under realistic conditions. A new architecture for user modeling has been developed which focuses on the tasks performed by users. It allows adaptive browsing support for users with different level of experience, and a level of adaptability. The requirements analysis shows that the differences in the information needs of users with different levels of experience are not only quantitative, but qualitative. Experienced users are not only able to cope with a wider browsing space, but sometimes prefer to organize their search in a different way. That is why the user model and the interface of the system are designed to support a smooth transition in the access options provided to novice users and to expert users.     

Neural network learning from demonstration and epipolar geometry for visual control of a nonholonomic mobile robot

The control of a robot system using camera information is a challenging task regarding unpredictable conditions, such as feature point mismatch and changing scene illumination. This paper presents a solution for the visual control of a nonholonomic mobile robot in demanding real world circumstances based on machine learning techniques. A novel intelligent approach for mobile robots using neural networks (NNs), learning from demonstration (LfD) framework, and epipolar geometry between two views is proposed and evaluated in a series of experiments. A direct mapping from the image space to the actuator command is conducted using two phases. In an offline phase, NN–LfD approach is employed in order to relate the feature position in the image plane with the angular velocity for lateral motion correction. An online phase refers to a switching vision based scheme between the epipole based linear velocity controller and NN–LfD based angular velocity controller, which selection depends on the feature distance from the pre-defined interest area in the image. In total, 18 architectures and 6 learning algorithms are tested in order to find optimal solution for robot control. The best training outcomes for each learning algorithms are then employed in real time so as to discover optimal NN configuration for robot orientation correction. Experiments conducted on a nonholonomic mobile robot in a structured indoor environment confirm an excellent performance with respect to the system robustness and positioning accuracy in the desired location.     

Design,implementation, and performance of an automatic configuration service for distributed component systems

Component technology promotes code reuse by enabling the construction of complex applications by assembling off‐the‐shelf components. However, components depend on certain characteristics of the environment in which they execute. They depend on other software components and on hardware resources. In existing component architectures, the application developer is left with the task of resolving those dependencies, i.e. making sure that each component has access to all the resources it needs and that all the required components are loaded. Nevertheless, according to encapsulation principles, developers should not be aware of the component internals. Thus, it may be difficult to find out what a component really needs. In complex systems, such as the ones found in modern distributed environments, this manual approach to dependency management can lead to disastrous results. Current systems rely heavily on manual configuration by users and system administrators. This is tolerable now, when users have to manage a few computers. But, in the near future, people will have to deal with thousands of computing devices and it will no longer be acceptable to require the user to configure each of them. This paper presents the results of our 6 year research (from 1998 to 2003) in the area of automatic configuration, describing an integrated architecture for managing dependencies in distributed component‐based systems. The architecture supports automatic configuration and dynamic resource management in distributed heterogeneous environments. We describe a concrete implementation of this architecture, present experimental results, and compare our approach to other works in the area. Copyright © 2005 John Wiley & Sons, Ltd.     

Adaptive systems: from intelligent tutoring to autonomous agents

Computer systems which can automatically alter aspects of their functionality or interface to suit the needs of individuals or groups of users have appeared over the years in a variety of guises. Most recently, attention has focused on intelligent interface agents, which are seen as specialised, knowledge-based systems acting on behalf of the user in some aspect of the interaction. Similar requirements for automatic adaptation have been noted in intelligent tutoring systems, natural-language systems and intelligent interfaces. The paper brings together the research which has emanated from a number of backgrounds, and provides a unifying perspective on adaptive systems in general. An architecture for adaptive systems and a methodology for their development are presented. The paper also describes software support for producing adaptive systems, and offers some experimental evidence to justify both the desirability and feasibility of exploiting an adaptive system approach to human-computer interaction     

Rule effectiveness in rule-based systems: A credit scoring case study

Rule-based systems may sometimes grow very large, making their acceptance by users and their maintenance quite problematic. One therefore needs to make rule-bases as compact as possible. The classical definition of rule redundancy in the literature is based upon logic and graph theory. Another, complementary, view of redundancy is proposed here. The suggested approach is based on the contribution of individual rules to the overall system’s accuracy.

It is shown here, though an analysis of a real-world credit scoring rule-based system, that by taking into account system’s accuracy, one can sometimes significantly reduce the size of a rule-base; even one which is already free from logic-related abnormalities. The approach taken here is not proposed as a substitution to classical logic and graph-based methods. Rather, it complements them.     

Deferred system's design: Situated system requirements gathering with Hyper-Tmodeller

The conceptual foundations underpinning the approach to system requirements gathering considered in this paper aredeferred system's design andtailorable information systems. In this approach users of information systems are regarded as active developers. System requirements gathering is considered from an interpretative and situated perspective using the Hyper-Tmodeller CASE tool. The tool enables better interpretative and situated system requirements gathering, through visual modelling by users and professional system developers. It is designed to address the requirements communication gap between system analysts, designers and eventual users.     

The Plan B OS for ubiquitous computing. Voice control, security, and terminals as case studies

The conventional approach to building pervasive environments relies on middleware to integrate different systems. Instead, we have built a system that can deal with these environments by exporting system resources through distributed virtual file systems. This requires no middleware, simplifies interoperation, and permits the application of general purpose tools to any system resource. A constraint-based file system import mechanism allows the system to adapt to changes in the environment and permits users to customize the environment and tailor adaptations according to their needs. The system has been in use for over a year to carry out our daily work and is underlying the smart space that we built for our department. The system, and some novel services, including ubiquitous voice interfaces, a distributed security architecture, and remote terminals for smart spaces, are also described in this paper.     

Improving human robot collaboration through Force/Torque based learning for object manipulation

Human–Robot Collaboration (HRC) is a term used to describe tasks in which robots and humans work together to achieve a goal. Unlike traditional industrial robots, collaborative robots need to be adaptive; able to alter their approach to better suit the situation and the needs of the human partner. As traditional programming techniques can struggle with the complexity required, an emerging approach is to learn a skill by observing human demonstration and imitating the motions; commonly known as Learning from Demonstration (LfD). In this work, we present a LfD methodology that combines an ensemble machine learning algorithm (i.e. Random Forest (RF)) with stochastic regression, using haptic information captured from human demonstration. The capabilities of the proposed method are evaluated using two collaborative tasks; co-manipulation of an object (where the human provides the guidance but the robot handles the objects weight) and collaborative assembly of simple interlocking parts. The proposed method is shown to be capable of imitation learning; interpreting human actions and producing equivalent robot motion across a diverse range of initial and final conditions. After verifying that ensemble machine learning can be utilised for real robotics problems, we propose a further extension utilising Weighted Random Forest (WRF) that attaches weights to each tree based on its performance. It is then shown that the WRF approach outperforms RF in HRC tasks.     

Embryonic approach to the development of information systems

Prototyping in information systems (IS) development has recently shown increased benefits. In principle, the prototyping process provides users with more opportunities to improve their work, to verify that their needs are provided for, and that the terms used in the interface of the designed system are consistent with those in use in their work. As a result, they should be highly motivated to participate in an IS development process.However, certain drawbacks inherited from traditional prototyping in industrial production could limit the use of this approach in IS development. Some problems are identified in this paper, such as: (1) product-oriented thinking; (2) feedback delay; (3) the preoccupation of designers with respect to the experimental approach; (4) problems arising from the users' participation being indirect, and (5) negative attitudes towards contradiction. This paper proposes an organic approach, the ‘Embryonic Approach’ (EmA), in order to explore the full potentialities of prototyping in IS development. This approach is based on two fundamental elements; an adaptive and expandable kernel-structure, and a built-in communication mechanism.     

Supporting serendipity: Using ambient intelligence to augment user exploration for data mining and web browsing

Serendipity is the making of fortunate discoveries by accident, and is one of the cornerstones of scientific progress. In today's world of digital data and media, there is now a vast quantity of material that we could potentially encounter, and so there is an increased opportunity of being able to discover interesting things. However, the availability of material does not imply that we will be able to actually find it; the sheer quantity of data mitigates against us being able to discover the interesting nuggets.This paper explores approaches we have taken to support users in their search for interesting and relevant information. The primary concept is the principle that it is more useful to augment user skills in information foraging than it is to try and replace them. We have taken a variety of artificial intelligence, statistical, and visualisation techniques, and combined them with careful design approaches to provide supportive systems that monitor user actions, garner additional information from their surrounding environment and use this enhanced understanding to offer supplemental information that aids the user in their interaction with the system.We present two different systems that have been designed and developed according to these principles. The first system is a data mining system that allows interactive exploration of the data, allowing the user to pose different questions and understand information at different levels of detail. The second supports information foraging of a different sort, aiming to augment users browsing habits in order to help them surf the internet more effectively. Both use ambient intelligence techniques to provide a richer context for the interaction and to help guide it in more effective ways: both have the user as the focal point of the interaction, in control of an iterative exploratory process, working in indirect collaboration with the artificial intelligence components.Each of these systems contains some important concepts of their own: the data mining system has a symbolic genetic algorithm which can be tuned in novel ways to aid knowledge discovery, and which reports results in a user-comprehensible format. The visualisation system supports high-dimensional data, dynamically organised in a three-dimensional space and grouped by similarity. The notions of similarity are further discussed in the internet browsing system, in which an approach to measuring similarity between web pages and a user's interests is presented. We present details of both systems and evaluate their effectiveness.     

基于极端评分行为的相似度计算

随着互联网技术的迅猛发展,互联网信息急剧增长,信息过载问题愈发凸显。面对海量的互联网信息,用户往往需要耗费大量的时间来搜索所需的信息或产品,而搜索的解往往受到制约。为解决信息过载问题,推荐系统应运而生。推荐系统根据用户的历史行为推测其需求、兴趣等,将用户感兴趣的信息、产品等推荐给用户。作为推荐领域中一类重要的推荐方法,基于记忆的协同过滤方法通常依据用户或产品的近邻信息来构造评分预测函数,其核心在于准确度量用户或产品之间的相似度。传统的相似度量,如皮尔逊、余弦及秩相关系数等,通常只考虑了用户之间的线性关系;而启发式相似度如基于3个特殊因子的PIP相似度及其改进方法,则只刻画了用户之间的非线性关系。事实上,在推荐系统中,就用户之间的相似关系而言,仅用线性或是非线性函数来度量均是不准确的。为了更为精细地刻画用户之间的相似程度,文中提出了基于非线性函数的用户极端评分行为的相似程度度量指数,通过将该指数融入传统的线性相关系数,构造了一个考虑极端评分行为的新的相似度。为验证该方法的有效性,基于Ml(100k)和Ml-latest-small两个数据集,将其与传统相似度以及启发式相似度进行比较,结果显示基于极端评分行为相似度的协同过滤方法在MAE和RMSE指标上能够获得更好的表现。     

A topology for secure MVS systems

Ultimate protection of computers against programming users appears unachievable. True security seems within reach only within systems without programming users. However, programming has to be done within each computing centre. To meet these conflicting ends, this paper proposes a means of isolating any enterprise's vital data from abuse by fully mutually isolating systems at three security levels from one another.The approach proposed is already partly implemented in major computing centres, though with an effectiveness far from that required. Specifically, it is shown that shared DASD degrades the overall security level to that of the least secure system connected. A higher degree of security, as this paper suggests, is reachable in current systems by defining and implementing a three-level (minimal) topology as part of an overall security strategy.     

Incentivized provision of metadata, semantic reasoning and time-driven filtering: Making a puzzle of personalized e-commerce

e-Commerce recommender systems select potentially interesting products for users by looking at their purchase histories and preferences. In order to compare the available products against those included in the user’s profile, semantics-based recommendation strategies consider metadata annotations that describe their main attributes. Besides, to ensure successful suggestions of products, these strategies adapt the recommendations as the user’s preferences evolve over time. Traditional approaches face two limitations related to the aforementioned features. First, product providers are not typically willing to take on the tedious task of annotating accurately a huge diversity of commercial items, thus leading to a substantial impoverishment of the personalization quality. Second, the adaptation process of the recommendations misses the time elapsed since the user has bought an item, which is an essential parameter that affects differently to each purchased product. This results in some pointless recommendations, e.g. including regularly items that the users are only willing to buy sporadically. In order to fight both limitations, we propose a personalized e-commerce system with two main features. On the one hand, we incentivize the users to provide high-quality metadata for commercial products; on the other, we explore a strategy that offers time-aware recommendations by combining semantic reasoning about these annotations with item-specific time functions. The synergetic effects derived from this combination lead to suggestions adapted to the particular needs of the users at any time. This approach has been experimentally validated with a set of users who accessed our personalized e-commerce system through a range of fixed and handheld consumer devices.     

NEWER: A system for NEuro-fuzzy WEb Recommendation

In the era of the Web, there is urgent need for developing systems able to personalize the online experience of Web users on the basis of their needs. Web recommendation is a promising technology that attempts to predict the interests of Web users, by providing them with information and/or services that they need without explicitly asking for them. In this paper we propose NEWER, a usage-based Web recommendation system that exploits the potential of Computational Intelligence techniques to dynamically suggest interesting pages to users according to their preferences. NEWER employs a neuro-fuzzy approach in order to determine categories of users sharing similar interests and to discover a recommendation model as a set of fuzzy rules expressing the associations between user categories and relevances of pages. The discovered model is used by a online recommendation module to determine the list of links judged relevant for users. The results obtained on both synthetic and real-world data show that NEWER is effective for recommendation, leading to a quality of the generated recommendations comparable and often significantly better than those of other approaches employed for the comparison.     

Utilizing contextual ontological user profiles for personalized recommendations

As users may have different needs in different situations and contexts, it is increasingly important to consider user context data when filtering information. In the field of web personalization and recommender systems, most of the studies have focused on the process of modelling user profiles and the personalization process in order to provide personalized services to the user, but not on contextualized services. Rather limited attention has been paid to investigate how to discover, model, exploit and integrate context information in personalization systems in a generic way. In this paper, we aim at providing a novel model to build, exploit and integrate context information with a web personalization system. A context-aware personalization system (CAPS) is developed which is able to model and build contextual and personalized ontological user profiles based on the user’s interests and context information. These profiles are then exploited in order to infer and provide contextual recommendations to users. The methods and system developed are evaluated through a user study which shows that considering context information in web personalization systems can provide more effective personalization services and offer better recommendations to users.     

Followee recommendation based on text analysis of micro-blogging activity

Nowadays, more and more users keep up with news through information streams coming from real-time micro-blogging activity offered by services such as Twitter. In these sites, information is shared via a followers/followees social network structure in which a follower receives all the micro-blogs from his/her followees. Recent research efforts on understanding micro-blogging as a novel form of communication and news spreading medium have identified three different categories of users in these systems: information sources, information seekers and friends. As social networks grow in the number of registered users, finding relevant and reliable users to receive interesting information becomes essential. In this paper we propose a followee recommender system based on both the analysis of the content of micro-blogs to detect users' interests and in the exploration of the topology of the network to find candidate users for recommendation. Experimental evaluation was conducted in order to determine the impact of different profiling strategies based on the text analysis of micro-blogs as well as several factors that allows the identification of users acting as good information sources. We found that user-generated content available in the network is a rich source of information for profiling users and finding like-minded people.     

Optimised Learning from Demonstrations for Collaborative Robots

The approach of Learning from Demonstrations (LfD) can support human operators especially those without much programming experience to control a collaborative robot (cobot) in an intuitive and convenient means. Gaussian Mixture Model and Gaussian Mixture Regression (GMM and GMR) are useful tools for implementing such a LfD approach. However, well-performed GMM/GMR require a series of demonstrations without trembling and jerky features, which are challenging to achieve in actual environments. To address this issue, this paper presents a novel optimised approach to improve Gaussian clusters then further GMM/GMR so that LfD enabled cobots can carry out a variety of complex manufacturing tasks effectively. This research has three distinguishing innovative characteristics: 1) a Gaussian noise strategy is designed to scatter demonstrations with trembling and jerky features to better support the optimisation of GMM/GMR; 2) a Simulated Annealing-Reinforcement Learning (SA-RL) based optimisation algorithm is developed to refine the number of Gaussian clusters in eliminating potential under-/over-fitting issues on GMM/GMR; 3) a B-spline based cut-in algorithm is integrated with GMR to improve the adaptability of reproduced solutions for dynamic manufacturing tasks. To verify the approach, cases studies of pick-and-place tasks with different complexities were conducted. Experimental results and comparative analyses showed that this developed approach exhibited good performances in terms of computational efficiency, solution quality and adaptability.     

Client-Led Information System Creation (CLIC): navigating the gap

Abstract.  This paper offers a new framework to facilitate an interpretive approach to client-led information system development, referred to as CLIC (Client-Led Information System Creation). The challenge of moving seamlessly through a process of information systems (IS) design is still the subject of much research in the IS field. Attempts to address the difficulties of 'bridging the gap' between a client's business needs and an information system definition have hitherto not provided a coherent and practical approach. Rather than attempting to bridge the gap, this paper describes an approach to managing this gap by facilitating the clients' navigating through the information system design process (or inquiry process) in a coherent manner. The framework has been developed through practice, and the paper provides an example of navigating through the design phase taken from an Action Research field study in a major UK bank.