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.     

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.     

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.     

Design sensitivity analysis of nonlinear dynamic response of structural and mechanical systems

This paper describes a unified variational theory for design sensitivity analysis of nonlinear dynamic response of structural and mechanical systems for shape, nonshape, material and mechanical properties selection, as well as control problems. The concept of an adjoint system, the principle of virtual work and a Lagrangian-Eulerian formulation to describe the deformations and the design variations are used to develop a unified view point. A general formula for design sensitivity analysis is derived and interpreted for usual performance functionals. Analytical examples are utilized to demonstrate the use of the theory and give insights for application to more complex problems that must be treated numerically.Derivatives The comma notation for partial derivatives is used, i.e. G,_u = G/u. An upper dot represents material time derivative, i.e. ü = ²u/t². A prime implies derivative with respect to the time measured in the reference time-domain, i.e. u = du/d.     

Applying Dialectic Agents to Argumentation in E-Commerce

Reaching an agreement or understanding through argumentation is an important aspect of decision making in a virtual society as well as in our real society. In this paper, we consider compromise (Aufheben) and concession (weaker Aufheben) as a simple form of Hegelian dialectical reasoning, which we think are desiderata to deliberate or cognitive agents. We then propose an argument-based agent system that allows for the issue modification among agents concerned during argumentation and reaching an agreement or understanding through argumentation with the dialectical reasoning capability. We illustrate its potential usefulness by showing applications to seller and buyer agents and traveling salesman agents in e-commerce.     

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.     

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.     

Coordinating Multiple Agents via Reinforcement Learning

In this paper, we attempt to use reinforcement learning techniques to solve agent coordination problems in task-oriented environments. The Fuzzy Subjective Task Structure model (FSTS) is presented to model the general agent coordination. We show that an agent coordination problem modeled in FSTS is a Decision-Theoretic Planning (DTP) problem, to which reinforcement learning can be applied. Two learning algorithms, coarse-grained and fine-grained, are proposed to address agents coordination behavior at two different levels. The coarse-grained algorithm operates at one level and tackle hard system constraints, and the fine-grained at another level and for soft constraints. We argue that it is important to explicitly model and explore coordination-specific (particularly system constraints) information, which underpins the two algorithms and attributes to the effectiveness of the algorithms. The algorithms are formally proved to converge and experimentally shown to be effective.     

A predicate transformer for the progress property ‘to-always’

The temporal property to-always has been proposed for specifying progress properties of concurrent programs. Although the to-always properties are a subset of the leads-to properties for a given program, to-always has more convenient proof rules and in some cases more accurately describes the desired system behavior. In this paper, we give a predicate transformerwta, derive some of its properties, and use it to define to-always. Proof rules for to-always are derived from the properties ofwta. We conclude by briefly describing two application areas, nondeterministic data flow networks and self-stabilizing systems where to-always properties are useful.     

A behavioural model of temperature controller usage and energy saving

Temperature controllers are typical devices in most office-buildings, but the intended users very seldom use them. It has been a constant observation during our research that all common temperature controllers to date are quite impossible to use and understand correctly. With the proper use of temperature controllers comfort could be improved and a lot of energy could be saved. This paper presents a model of first use of an OOBE device, in which the actors beliefs are included. A list of conditions for energy saving actions, a design decision matrix and a model of action circle is presented in this article. The model indicates that the users action can be extinguished at various points during the action circle. However, sufficient and understandable initial feedback on the devices interface is the key to correct novel use.

Indeterminancy in Recent Theories of Content

Jerry Fodor has charged that Fred Dretske's account of content suffers from indeterminacy to the extent that we should reject it in favor of Fodors own account. In this paper, we ask what the problem of indeterminacy really is; we distinguish a relatively minor problem we call looseness of fit from a major problem of failing to show how to point to what is not there. We sketch Dretske's account of content and how it is supposed to solve the major problem. After presenting Fodor's challenge as the claim that Dretske has failed to solve the major problem, we articulate a response available to Dretske. Although we do not think the response is ultimately successful, we argue that it is every bit as good as the response Fodor has offered to a similar challenge, in his so-called mixed theory. The upshot is this: despite advertisements to the contrary, Fodor's theory, in its mixed version, offers no real advantages over Dretske's regarding the serious problem of indeterminacy.     

Explanations in cognitive science

Cognitive science may be loosely described as the activity of trying to model aspects of human behaviour upon a computer. It has emerged as a blending of the techniques of artificial intelligence and cognitive psychology but these two disciplines have different, and incompatible, philosophies. Searle has detected elements of behaviourism and operationism within artificial intelligence, whereas cognitive psychology is essentially anti-behaviourist. It is argued that cognitive science needs to distinguish between competence and performance in order to clarify this point, and that such a distinction has far-reaching effects on how we view computer simulations of behaviour.     

Implementing the ‘Fool's model’ of combinatory logic

This paper studies Fool's models of combinatory logic, and relates them to Hindley's D-completeness problem. A fool's model is a family of sets of formulas, closed under condensed detachment. Alternatively, it is a model ofCL in naive set theory. We examine Resolution; and the P-W problem. A sequel shows T is D-complete; also, its extensions. We close with an implementation FMO of these ideas.     

Boolean and Cartesian abstraction for model checking C programs

We show how to attack the problem of model checking a C program with recursive procedures using an abstraction that we formally define as the composition of the Boolean and the Cartesian abstractions. It is implemented through a source-to-source transformation into a Boolean C program; we give an algorithm to compute the transformation with a cost that is exponential in its theoretical worst-case complexity but feasible in practice.     

Hierarchical parallel search for markov control with enhanced selector

A new method of hierarchical parallel search is proposed for dealing with Markov planning/control processes in systems with uncertain information. It is based on a new concept of analyzing alternative with uncertain cost evaluation. Under definite conditions, instead of making an immediate choice based on expectation of cost at each step of the search, it is recommended to postpone the final decision until information is improved, and the uncertainty is reduced. In addition to elementary alternatives, their combinations are also considered for possible pursuit. The best set of rough elementary solutions is to be determined at the upper of two adjacent planning/control levels, then all elementary alternatives of this set as well as their combinations, are being pursued at the lower level with a higher resolution of information, while evaluation of the remaining cost for each of the alternatives, is being constantly improved due to the process of evolutionary uncertainty reduction. This bilevel process is easily extendible over the whole hierarchy of the system. The method is working in the graph-search and dynamic programming paradigms. The set of problems to be solved is formulated and some of them are addressed. Various applications are contemplated.     

The Implications of an Externalist Theory of Rule-Following Behaviour for Robot Cognition

Given (1) Wittgensteins externalist analysis of the distinction between following a rule and behaving in accordance with a rule, (2) prima facie connections between rule-following and psychological capacities, and (3) pragmatic issues about training, it follows that most, even all, future artificially intelligent computers and robots will not use language, possess concepts, or reason. This argument suggests that AIs traditional aim of building machines with minds, exemplified in current work on cognitive robotics, is in need of substantial revision.     

Zheng and Seberry’s public key encryption scheme revisited

In this paper, we prove that a slightly modified version of Zheng and Seberrys public key encryption scheme presented at Crypto 92 is secure against adaptive chosen ciphertext attacks in the random oracle model, assuming the Gap Diffie–Hellman problem is intractable. A further contribution of this paper is to show that Soldera, Seberry, and Qus recent security analysis of Zheng and Seberrys scheme is in fact flawed.     

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.     

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.     

An analysis of a contention resolution algorithm

Summary A single multiaccess channel is studied with the outcome of a transmission being either idle, success, or collision (ternary channel). Packets involved in a collision must be retransmitted, and an efficient way to solve a collision is known in the literature as Gallager-Tsybakov-Mikhailov algorithm. Performance analysis of the algorithm is quite hard. In fact, it bases on a numerical solution of some recurrence equations and on a numerical evaluation of some series. The obvious drawback of it is lack of insight into the behavior of the algorithm. We shall present a new approach of looking at the algorithm and discuss some attempts of analyzing its performance. In particular, expected lengths of a resolution interval and a conflict resolution interval as well as throughput of the algorithm will be discussed using asymptotic approximation and a small input rate approximation.