Personalized Course Navigation Based on Grey Relational Analysis

Web personalization has quickly changed from a value-added facility to a service required in presenting large quantities of information because individual users of the Internet have various needs and preferences in seeking information. This paper presents a novel personalized recommendation system with online preference analysis in a distance learning environment called Coursebot. Users can both browse and search for course materials by using the interface of Coursebot. Moreover, the proposed system includes appropriate course materials ranked according to a users interests. In this work, an analysis measure is proposed to combine typical grey relational analysis and implicit rating, and thus a users interests are calculated from the content of documents and the users browsing behavior. This algorithms low computational complexity and ease of adding knowledge support online personalized analysis. In addition, the user profiles are dynamically revised to provide efficiently personalized information that reflects a users interests after each page is visited.     

Sensible appliances: applying context-awareness to appliance design

A sensible appliance is an appliance which understands a users situation and takes appropriate actions to provide a high-quality user experience. A sensible appliance should be able to collect and infer relevant contexts to understand the users situation. It is important for designers to understand context-awareness and its impact on design. In this paper, we introduce what context and context-awareness are by using data from our survey on existing work in the area. Then, we present our categorization of existing appliances based on their contextualness. Finally, as a design case study of context-aware appliances, we describe our work on the Gate Reminder—a ubiquitous and context-aware reminder appliance built into a front door to remind users of things they should take with them when leaving home, and messages they should be aware of before going out.     

Slicing, Chopping, and Path Conditions with Barriers

One of the critiques on program slicing is that slices presented to the user are hard to understand. This is mainly related to the problem that slicing dumps the results onto the user without any explanation. This work will present an approach that can be used to filter slices. This approach basically introduces barriers which are not allowed to be passed during slice computation. An earlier filtering approach is chopping which is also extended to obey such a barrier. The barrier variants of slicing and chopping provide filtering possibilities for smaller slices and better comprehensibility. The concept of barriers is then applied to path conditions, which provide necessary conditions under which an influence between the source and target criterion exists. Barriers make those conditions more precise.     

Delegation of cryptographic servers for capture-resilient devices

A device that performs private key operations (signatures or decryptions), and whose private key operations are protected by a password, can be immunized against offline dictionary attacks in case of capture by forcing the device to confirm a password guess with a designated remote server in order to perform a private key operation. Recent proposals for achieving this allow untrusted servers and require no server initialization per device. In this paper we extend these proposals to enable dynamic delegation from one server to another; i.e., the device can subsequently use the second server to secure its private key operations. One application is to allow a user who is traveling to a foreign country to temporarily delegate to a server local to that country the ability to confirm password guesses and aid the users device in performing private key operations, or in the limit, to temporarily delegate this ability to a token in the users possession. Another application is proactive security for the devices private key, i.e., proactive updates to the device and servers to eliminate any threat of offline password guessing attacks due to previously compromised servers.Received: November 2001, Accepted: January 2003, Extended abstract appears in Proceedings of the 8 ^th ACM Symposium on Computer and Communications Security, November 2001.     

Physically touching and tasting virtual objects enhances the realism of virtual experiences

Experiment 1 explored the impact of physically touching a virtual object on how realistic the virtual environment (VE) seemed to the user. Subjects in a no touch group picked up a 3D virtual image of a kitchen plate in a VE, using a traditional 3D wand. See and touch subjects physically picked up a virtual plate possessing solidity and weight, using a technique called tactile augmentation. Afterwards, subjects made predictions about the properties of other virtual objects they saw but did not interact with in the VE. See and touch subjects predicted these objects would be more solid, heavier, and more likely to obey gravity than the no touch group. In Experiment 2 (a pilot study), subjects physically bit a chocolate bar in one condition, and imagined biting a chocolate bar in another condition. Subjects rated the event more fun and realistic when allowed to physically bite the chocolate bar. Results of the two experiments converge with a growing literature showing the value of adding physical qualities to virtual objects. This study is the first to empirically demonstrate the effectiveness of tactile augmentation as a simple, safe, inexpensive technique with large freedom of motion for adding physical texture, force feedback cues, smell and taste to virtual objects. Examples of practical applications are discussed.Based in part on Physically touching virtual objects using tactile augmentation enhances the realism of virtual environments' by Hunter Hoffman which appeared in the Proceedings of the IEEE Virtual Reality Annual International Symposium '98, Atlanta GA, pp 59–63. ¢ 1998 IEEE.     

Evaluating design guidelines for reducing user disorientation in a desktop virtual environment

Navigation in virtual environments can be difficult. One contributing factor is user disorientation. Two major causes of this are the lack of navigation cues in the environment and problems with navigating too close to or through virtual world objects. Previous work has developed guidelines, informed by cinematography conventions, for the construction of virtual environments to aid user comprehension of virtual space to reduce user disorientation. To validate these guidelines, two user studies have been performed where users of a desktop virtual environment are to complete a navigation task in a virtual maze. In an initial study [12], collision detection with the maze walls was not enabled and the results indicated that the guidelines were effective for reducing disorientation but not for developing the users awareness of the environment space. A second study has been performed where collision detection was enabled. Results suggest that the use of the guidelines can help reduce the incidences of user disorientation and aid navigation tasks. However, the guidelines have little impact on users ability to construct cognitive maps of the desktop virtual environment.

How level and type of deafness affect user perception of multimedia video clips

Our research investigates the impact that hearing has on the perception of multimedia, with and without captions, by discussing how hearing loss, captions and deafness type affect user quality of perception (QoP). QoP encompasses both the users level of satisfaction and their ability to assimilate informational content of multimedia.Experimental results show that hearing has a significant effect on participants ability to assimilate information, independent of video type or use of captions. It is shown that captioned video does not necessarily provide deaf users with a greater level of information but changes user QoP, providing a greater level of video contextualisation.     

Collaboration and Teamwork: Immersion and Presence in an Online Learning Environment

In the world of OTIS, an online Internet School for occupational therapists, students from four European countries were encouraged to work collaboratively through problem-based learning by interacting with each other in a virtual semi-immersive environment. This paper describes, often in their own words, the experience of European occupational therapy students working together across national and cultural boundaries. Collaboration and teamwork were facilitated exclusively through an online environment, since the students never met each other physically during the OTIS pilot course. The aim of the paper is to explore the observations that (1) there was little interaction between students from different tutorial groups and (2) virtual teamwork developed in each of the cross-cultural tutorial groups. Synchronous data from the students was captured during tutorial sessions and peer-booked meetings and analyzed using the qualitative constructs of immersion, presence and reflection in learning. The findings indicate that immersion was experienced only to a certain extent. However, students found both presence and shared presence, within their tutorial groups, to help collaboration and teamwork. Other evidence suggests that communities of interest were established. Further study is proposed to support group work in an online learning environment. It is possible to conclude that collaborative systems can be designed, which encourage students to build trust and teamwork in a cross cultural online learning environment.This revised version was published online in March 2005 with corrections to the cover dateFunded by the European Union through the TENTelecom programme.     

From devices to tasks: automatic task prediction for personalized appliance control

One of the driving applications of ubiquitous computing is universal appliance interaction: the ability to use arbitrary mobile devices to interact with arbitrary appliances, such as TVs, printers, and lights. Because of limited screen real estate and the plethora of devices and commands available to the user, a central problem in achieving this vision is predicting which appliances and devices the user wishes to use next in order to make interfaces for those devices available. We believe that universal appliance interaction is best supported through the deployment of appliance user interfaces (UIs) that are personalized to a users habits and information needs. In this paper, we suggest that, in a truly ubiquitous computing environment, the user will not necessarily think of devices as separate entities; therefore, rather than focus on which device the user may want to use next, we present a method for automatically discovering the users common tasks (e.g., watching a movie, or surfing TV channels), predicting the task that the user wishes to engage in, and generating an appropriate interface that spans multiple devices. We have several results. We show that it is possible to discover and cluster collections of commands that represent tasks and to use history to predict the next task reliably. In fact, we show that moving from devices to tasks is not only a useful way of representing our core problem, but that it is, in fact, an easier problem to solve. Finally, we show that tasks can vary from user to user.     

Using incremental Web log mining to create adaptive web servers

Personalization of content returned from a Web site is an important problem in general and affects e-commerce and e-services in particular. Targeting appropriate information or products to the end user can significantly change (for the better) the user experience on a Web site. One possible approach to Web personalization is to mine typical user profiles from the vast amount of historical data stored in access logs. We present a system that mines the logs to obtain profiles and uses them to automatically generate a Web page containing URLs the user might be interested in. Profiles generated are only based on the prior traversal patterns of the user on the Web site and do not involve providing any declarative information or require the user to log in. Profiles are dynamic in nature. With time, a users traversal pattern changes. To reflect changes to the personalized page generated for the user, the profiles have to be regenerated, taking into account the existing profile. Instead of creating a new profile, we incrementally add and/or remove information from a user profile, aiming to save time as well as physical memory requirements.     

A Case Based System for Oil and Gas Well Design with Risk Assessment

A case base system for a complex problem like oil field design needs to be richer than the usual case based reasoning system. Genesis, the system described in this paper contains large heterogeneous cases with metalevel knowledge. A multi-level indexing scheme with both preallocated and dynamically computed indexing capability has been implemented. A user interface allows dynamic creation of similarity measures based on modelling of the users intentions. Both user aiding and problem solution facilities are supported, a novel feature is that risk estimates are also provided. Performance testing indicates that the case base produces on average, better predictions for new well developments than company experts. Early versions of the system have been deployed into oil companies in 6 countries around the world and research is continuing on refining the system in response to industry feedback.     

Customising WAP-based information services on mobile networks

In addition to voice transmission over mobile networks, the demand of data communication has been increasing. To deploy data-oriented applications for mobile terminals, the wireless application protocol (WAP) has provided a promising solution. However, as in the World Wide Web (WWW), the increasing information leads to the problem of information overload. One way to overcome such a problem is to build intelligent recommender systems to provide customised information services. By analyzing the information collected from the user, a customised recommender system is able to reason his personal preferences and to build a model of predictions. In this way, only the information predicted as user-interested can reach the end user. This paper presents a multi-agent framework in which a decision tree-based approach is employed to learn a users preferences. To assess the proposed framework, a mobile phone simulator is used to represent a mobile environment and a series of experiments are conducted. The experimental studies have concentrated on how to recommend appropriate information to the individual user, and on how the system can adapt to a users most recent preferences. The results and analysis show that based on our framework the WAP-based customised information services can be successfully performed.     

Exhaustive Interpretation of Complex Sentences

In terms of Groenendijk and Stokhofs (1984) formalization of exhaustive interpretation, many conversational implicatures can be accounted for. In this paper we justify and generalize this approach. Our justification proceeds by relating their account via Halpern and Moses (1984) non-monotonic theory of only knowing to the Gricean maxims of Quality and the first sub-maxim of Quantity. The approach of Groenendijk and Stokhof (1984) is generalized such that it can also account for implicatures that are triggered in subclauses not entailed by the whole complex sentence.     

Explanation-based interpretation of open-textured concepts in logical models of legislation

In this paper we discuss a view of the Machine Learning technique called Explanation-Based Learning (EBL) or Explanation-Based Generalization (EBG) as a process for the interpretation of vague concepts in logic-based models of law.The open-textured nature of legal terms is a well-known open problem in the building of knowledge-based legal systems. EBG is a technique which creates generalizations of given examples on the basis of background domain knowledge. We relate these two topics by considering EBG's domain knowledge as corresponding to statute law rules, and EBG's training example as corresponding to a precedent case.By making the interpretation of vague predicates as guided by precedent cases, we use EBG as an effective process capable of creating a link between predicates appearing as open-textured concepts in law rules, and predicates appearing as ordinary language wording for stating the facts of a case.Standard EBG algorithms do not change the deductive closure of the domain theory. In the legal context, this is only adequate when concepts vaguely defined in some law rules can be reformulated in terms of other concepts more precisely defined in other rules. We call theory reformulation the process adopted in this situation of complete knowledge.In many cases, however, statutory law leaves some concepts completely undefined. We then propose extensions to the EBG standard that deal with this situation of incomplete knowledge, and call theory revision the extended process. In order to fill in knowledge gaps we consider precedent cases supplemented by additional heuristic information. The extensions proposed treat heuristics represented by abstraction hierarchies with constraints and exceptions.In the paper we also precisely characterize the distinction between theory reformulation and theory revision by stating formal definitions and results, in the context of the Logic Programming theory.We offer this proposal as a possible contribution to cross fertilization between machine learning and legal reasoning methods.     

Human-oriented and machine-oriented reasoning: Remarks on some problems in the history of Automated Theorem Proving

Examples in the history of Automated Theorem Proving are given, in order to show that even a seemingly mechanical activity, such as deductive inference drawing, involves special cultural features and tacit knowledge. Mechanisation of reasoning is thus regarded as a complex undertaking in cultural pruning of human-oriented reasoning. Sociological counterparts of this passage from human- to machine-oriented reasoning are discussed, by focusing on problems of man-machine interaction in the area of computer-assisted proof processing.     

DETECTOR: A knowledge-based system for injection molding diagnostics

A knowledge-based system (KBS) for diagnosis of multiple defects in injection molding is presented. The general scheme for knowledge representation based on fuzzy set theory has been shown useful in representing inexact and incomplete information for developing the KBS. An optimality criterion is created for selecting a simple and best cover to explain the given problem. An efficient search algorithm for finding such cover is also discussed.     

Communication device to use an acceleration sensor for the seriously disabled

A communication device is proposed for use as an acceleration sensor for the seriously disabled. This device is mainly for disabled people with problems such as amyotrophic lateral sclerosis (ALS) or a cerebral infarction, who have communication difficulties because of a physical disability. A feature of this device is that the sensor can readily be mounted on the users body. The mounting of the sensor on the body is easy, unlike a conventional touch sensor. Another feature is that even though the physical disability of the user is serious, this device can be operated by slight movements of a part of the body. In addition, we have developed an adaptive word processing software for seriously disabled people. A feature of this software is that the arrangement and size of the buttons and the window on the display can be optimized based on the users physical ability. In a field test, we confirmed that our communication device incorporating this adaptive word processing software was applicable to disabled people.This work was presented in part at the 8th International Symposium on Artificial Life and Robotics, Oita, Japan, January 24–26, 2003     

An Agenda- and Justification-Based Framework for Discovery Systems

We propose and evaluate an agenda- and justification-based architecture for discovery systems that selects the next tasks to perform, as well as heuristics for use in discovery systems. This framework has many desirable properties: (1) it selects its own tasks to perform based upon how plausible they are judged to be; (2) it facilitates the encoding of general discovery strategies using a variety of background knowledge; and (3) it tailors its behavior toward a users interests. Many experiments with a prototype discovery program called HAMB demonstrate that both reasons and estimates of interestingness contribute to performance in the domains of protein crystallization and patient rehabilitation data. The programs heuristics provide good initial solutions to problems encountered when implementing fully autonomous discovery systems.     

Collaborative Filtering Using a Regression-Based Approach

The task of collaborative filtering is to predict the preferences of an active user for unseen items given preferences of other users. These preferences are typically expressed as numerical ratings. In this paper, we propose a novel regression-based approach that first learns a number of experts describing relationships in ratings between pairs of items. Based on ratings provided by an active user for some of the items, the experts are combined by using statistical methods to predict the users preferences for the remaining items. The approach was designed to efficiently address the problem of data sparsity and prediction latency that characterise collaborative filtering. Extensive experiments on Eachmovie and Jester benchmark collaborative filtering data show that the proposed regression-based approach achieves improved accuracy and is orders of magnitude faster than the popular neighbour-based alternative. The difference in accuracy was more evident when the number of ratings provided by an active user was small, as is common for real-life recommendation systems. Additional benefits were observed in predicting items with large rating variability. To provide a more detailed characterisation of the proposed algorithm, additional experiments were performed on synthetic data with second-order statistics similar to that of the Eachmovie data. Strong experimental evidence was obtained that the proposed approach can be applied to data over a large range of sparsity scenarios and is superior to non-personalised predictors even when ratings data are very sparse.     

User’s manual as a requirements specification: case studies

This paper argues that a users manual makes an excellent software requirements specification. It describes several experiences, including one in industry, of writing users manuals as requirements specifications. Finally, it discusses several lessons learned from the experiences.