Typing patterns: a key to user identification

As the deficiencies of traditional password-based access systems become increasingly acute, researchers have turned their focus to keystroke biometrics, which seeks to identify individuals by their typing characteristics. However, this field still faces many challenges before it can see full acceptance.     

Smart graphics: a new approach to meeting user needs

A convergence of computer graphics and other technologies such as artificial intelligence is leading to the development of smart graphics, which recognize application requirements, user characteristics, host-machine capabilities, and target usage, and adapt themselves accordingly. These new, dynamic capabilities extend the utility of computer graphics across a broad range of uses and potential applications. They may also engender the creation of new applications. For example, smart-graphics bar charts could change in accordance with fluctuating stock-market conditions. Developers could use smart graphics to customize GUIs and thus make them more useful. In addition, the technology has the potential to revolutionize graphics use in training, simulation, business productivity, and other applications     

Analyzing user requirements by use cases: a goal-driven approach

The purpose of requirements engineering is to elicit and evaluate necessary and valuable user needs. Current use-case approaches to requirements acquisition inadequately support use-case formalization and nonfunctional requirements. Based on industry trends and research, the authors have developed a method to structure use-case models with goals. They use a simple meeting planner system to illustrate the benefits of this new approach     

Study of collective user behaviour in Twitter: a fuzzy approach

The study of collective user behaviours in social networking sites has become an increasing important topic in social media mining. Understanding such behaviours has its potential to extract actionable patterns that can be beneficial to develop effective marketing strategies, optimise user experiences and maximise website revenues. With the rapid development of micro-blogging, Twitter has become a richer source of intelligence that can be used to study collective user behaviour, due to its efficient and meaningful user-to-user interactions. However, the classical statistical methods have some drawbacks in bridging the gap between user-generated data and human analysts who mostly use linguistic terms to analyse data and model/summarise knowledge learned. To address this gap, this work proposes a new approach, which employs the mass assignment theory-based fuzzy association rules algorithm (MASS-FARM), for the first time, to extract useful interaction behaviour of Twitter users. The influential factors (including activity time, number of friends/followers and the number of tweets) are represented as fuzzy granules, and the associations amongst are studied by employing MASS-FARM. The collective user behaviours are analysed in the Reply category and the Non-Reply category, respectively. The applicability and usefulness of the proposed method are demonstrated via an empirical study on a collected Twitter data set. The derived results are also discussed and compared with existing works.     

On a cuckoo search optimization approach towards feedback system identification

This paper presents a cuckoo search algorithm (CSA) based adaptive infinite impulse response (IIR) system identification scheme. The proposed scheme prevents the local minima problem encountered in conventional IIR modeling mechanisms. The performance of the new method has been compared with that obtained by other evolutionary computing algorithms like genetic algorithm (GA) and particle swarm optimization (PSO). The superior system identification capability of the proposed scheme is evident from the results obtained through an exhaustive simulation study.     

Closed-loop identification: a two step approach

The accuracy aspects of identification (with respect to both variance and bias of estimates) and the role of filtering in closed-loop identification is discussed in this paper. It is shown that the key difference between closed-loop and open-loop identification is the existence of the sensitivity function. A closed-loop identification algorithm which asymptotically yields the same expressions as open-loop identification, in both variance and bias errors, is proposed. The proposed algorithm is evaluated by simulated examples as well as experiments performed on a computer-interfaced pilot-scale process.     

Interactive user modeling: An integrative explicit-implicit approach

User modeling issues are examined in the context of a user-adapted guidance system. The system provides users with instructions about natural tasks without introducing a special time-consuming sub-dialog to learn the user's knowledge. A model for providing such guidance is developed on the basis of a phenomenological analysis of human guidance, and illustrated by a system that gives directions in geographical domains. The main features of the user model design include: (1) Bothimplicit andexplicit acquisition methods are employed in a flexible manner; (2) The guidance instructions and the user model are generated incrementally and interchangeably; (3) User's responses and no-responses are employed as a source of information for the user modeling. The model and the resulting system's performance are examined in light of recent development in the cognitive literature.E. Shifroni is a PhD candidate in Computer Science at the Technion. He received his B.A. in Mathematics and Computer Science from the Hebrew University of Jerusalem in 1982, and his M.S. degree in Computer Science from the same University in 1984. His primary interests lie in the areas of dialog systems and user modeling. This paper summarizes the current state of his thesis work on interactive user modeling.B. Shanon studied philosophy and linguistics in Tel-Aviv university, and linguistics and psychology at Stanford (PhD in psychology, 1974). After teaching at MIT joined the Department of Psychology of the Hebrew University, of which he is now chairperson. Also served as a visiting professor at Cornell University and Swarthmore College, as a visiting fellow at Princeton University, and as a visiting scholar at the center of interdisciplinary Research of the University of Bielefeld and at the Rockefeller Foundation Study center in Bellagio. Research interest focus on the conceptual foundation of cognitive science, the phenomenology of human conscious mentation, and on the psychology of creativity. A monograph presenting a comprehensive critique of the representational view of mind and a search for alternatives to it is forthcoming.     

Scanning on the fly: An approach to the user interface

Educational software must provide a “friendly” user interface in order to prevent student frustration. A recurring source of student frustration is the task of responding to prompts issued by the computer. Scanning on the fly reduces the difficulty of this task. It prevents the possibility of errors of form in student responses by accepting only keystrokes that lead to correctly formatted responses. This approach has the advantage that all text displayed by the computer pertains to the content of the lesson and none to the technical use of the computer. Its implementation is based on the notion of a finite state automaton, yielding reliable and easily modifiable software. Scanning on the fly has been used in the development of microcomputer-based instructional software for college algebra and trigonometry.     

On the series expansion approach to the identification ofHammerstein systems

A polynomial identification algorithm for recovering a nonlinearity in the Hammerstein system is proposed. The estimate employs the Legendre orthogonal system with adaptively selected number of terms. The global consistency along with rates of convergence are established. No assumptions concerning continuity of the nonlinearity or its functional form are made. A data-driven method using the cross-validation technique for selecting the number of terms in the estimate is presented     

Evaluation of user interfaces: EVADIS II— a comprehensive evaluation approach

As a result of the importance of the usability approach in system development and the EC's 'Directive concerning the minimum safety and health requirements for VDT workers' (EWG 1990), there is an accepted need for practical evaluation methods for user interfaces. The usability approach and the EC Directive are not restricted to user interface design, as they include the design of appropriate hardware and software, as well as organization, job, and task design. Therefore system designers are faced with many, often conflicting, requirements and need to address the question, 'How can usability requirements comprehensively be considered and evaluated in system development?' Customers buying hardware and software and introducing them into their organization ask, (How can I select easy-to-use hardware and software?' Both designers and customers need an evaluation procedure that covers all the organizational, user, hard- and software requirements. The evaluation method, EVADIS.II, we present in this paper overcomes characteristic deficiencies of previous evaluation methods. In particular, it takes the tasks, the user, and the organizational context into consideration during the evaluation process, and provides computer support for the use of the evaluation procedure.     

Visual perception-based criminal identification: a query-based approach

Abstract

The visual perception of eyewitness plays a vital role in criminal identification scenario. It helps law enforcement authorities in searching particular criminal from their previous record. It has been reported that searching a criminal record manually requires too much time to get the accurate result. We have proposed a query-based approach which minimises the computational cost along with the reduction of search space. A symbolic database has been created to perform a stringent analysis on 150 public (Bollywood celebrities and Indian cricketers) and 90 local faces (our data-set). An expert knowledge has been captured to encapsulate every criminal’s anatomical and facial attributes in the form of symbolic representation. A fast query-based searching strategy has been implemented using dynamic decision tree data structure which allows four levels of decomposition to fetch respective criminal records. Two types of case studies - viewed and forensic sketches have been considered to evaluate the strength of our proposed approach. We have derived 1200 views of the entire population by taking into consideration 80 participants as eyewitness. The system demonstrates an accuracy level of 98.6% for test case I and 97.8% for test case II. It has also been reported that experimental results reduce the search space up to 30 most relevant records.     

Towards a model-driven approach for multiexperience AI-based user interfaces

Software and Systems Modeling - Software systems start to include other types of interfaces beyond the “traditional” Graphical-User Interfaces (GUIs). In particular, Conversational User...     

L-two-optimal identification of errors-in-variables models: a frequency-domain approach

This paper proposes an L-two-optimal identification approach to cope with errors-in-variables model (EIVM) identification. With normalized coprime factor model (NCFM) representations, L-two-optimal approximate models are derived from the framework of an EIVM according to the kernel and image representations of related signals. Based on the optimal approximate models, the v-gap metric is employed as a minimization criterion to optimize the parameters of a system model, and thus the resulting optimization problem can be solved by linear matrix inequalities (LMIs). In terms of the optimized system model, the noise model (NM) can be readily obtained by right multiplication of an inner. Compared with other EIVM identification methods, the proposed one has a wider scope of applications because the statistical properties of disturbing noises are not demanded. It is also capable of giving identifiability. Finally, a numerical simulation is used to verify the effectiveness of the proposed method.     

Simultaneous identification of linear systems: a heuristic approach

The problem of simultaneous system identification is posed and an efficient algorithm for its solution is formulated. Our algorithm is a blend of an A^* search together with Simulated Annealing. The proposed algorithm returns the optimal solution while the number of required operations is usually much smaller than any other brute-force algorithm.     

MTi: A method for user identification for multitouch displays

This paper describes MTi, a biometric method for user identification on multitouch displays. The method is based on features obtained only from the coordinates of the 5 touchpoints of one of the user's hands. This makes MTi applicable to all multitouch displays large enough to accommodate a human hand and detect 5 or more touchpoints without requiring additional hardware and regardless of the display's underlying sensing technology. MTi only requests that the user places his hand on the display with the fingers comfortably stretched apart. A dataset of 34 users was created on which our method reported 94.69% identification accuracy. The method's scalability was tested on a subset of the Bosphorus hand database (100 users, 94.33% identification accuracy) and a usability study was performed.     

Modeling user action planning: A comprehension based approach

We review our efforts to model user command production in an attempt to characterize the knowledge users of computers have at various stages of learning. We modeled computer users with a system called NETWORK (Mannes and Kintsch, 1988; 1991) and modeled novice, intermediate, and expert UNIX command production data collected by Doane et al. (1990b) with a system called UNICOM (Doane et al., 1989a; 1991). We use the construction-integration theory of comprehension proposed by Kintsch (1988) as a framework for our analyses. By focusing on how instructions activate the knowledge rele/ant to the performance of the specified task, we have successfully modeled major aspects of correct user performance by incorporating in the model knowledge about individual commands and knowledge that allows the correct combination of elementary commands into complex, novel commands. Thus, experts can be modeled in both NETWORK and in UNICOM. We further show that salient aspects of novice and intermediate performance can be described by removing critical elements of knowledge from the expert UNICOM model. Results suggest that our comprehension-based approach has promise for understanding user interactions and implications for system design are discussed.Dr. Stephanie Doane is Assistant Professor of Psychology and appointed at the Beckman Institute at the University of Illinois. Shereceived her BAin Experimental Psychology from the University of California, Santa Barbara, her MS in Experimental Psychology from Villanova University, and her PhD in Cognitive Psychology from the University of California, Santa Barbara. Dr. Doane's research has focused on skill acquisition and the development and validation of theoretically-based computational models of cognitive processes. Her current research addresses issues of learning to interact with complex systems and the role of learning context in skill acquisition.Dr. Suzanne Mannes is Assistant Professor of Psychology at the University of Delaware. She received her BA in Psychology from the State University of New York College at Plattsburgh and received her PhD in Cognitive Psychology from the University of Colorado at Boulder. Her experimental research focuses on the role of prior knowledge in text comprehension, particularly as it pertains to problem-solving abilities. She also investigates the use of hybrid computer systems to simulate results from such studies.Dr. Walter Kintsch is Professor of Psychology and Director of the Institute of Cognitive Science at the University of Colorado in Boulder. He received his MA and PhD degrees in Experimental Psychology from the University of Kansas. His main area of interest has been the psychology of language and memory. He is currently the editor of the Psychological review.Peter Poison is Professor of Psychology and member of the Institute of Cognitive Science at the University of Colorado. He received his BA degree in Psychology and BS degree in Industrial Engineering from Stanford University and his PhD degree in Psychology from Indiana University. Dr. Poison's research has focused on the development and empirical evaluation of mathematical and computer simulation models of cognitive processes including transfer of training, problem solving, and the acquisition of cognitive skills. His current research deals with quantitative models of human-computer interaction and the application of such models to the design of more easily learned computer systems.     

The relationship between user participation and system success: a simultaneous contingency approach

The relationship between user participation and information system (IS) success has drawn attention from researchers for some time. It is assumed that strong participation of future users in the design of IS would lead to successful outcomes in terms of more IS usage, greater user acceptance, and increased user satisfaction. However, in spite of this, much of the empirical research so far has been unable to demonstrate its benefits. This paper examines the participation–success relationship in a broader context, where the effects of user participation and two other factors, user attitudes and user involvement, on system success occur simultaneously. Other contingency variables considered here are: system impact, system complexity, and development methodology. The theoretical framework and the associated hypotheses are empirically tested by a survey of 32 organizations. Empirical results corroborate the positive link between user participation and user satisfaction and provide evidence on the interplay between user attitudes and user involvement.     

Automating user reviews using ontologies: an agent-based approach

The Web is becoming a global market place, where the same services and products are offered by different providers. When obtaining a service, consumers have to select one provider among many alternatives to receive a service or buy a product. In real life, when obtaining a service, many consumers depend on the user reviews. User reviews—presumably written by other consumers—provide details on the consumers’ experiences and thus are more informative than ratings. The down side is that such user reviews are written in natural language, making it extremely difficult to be interpreted by computers. Therefore, current technologies do not allow automation of user reviews and require too much human effort for tasks such as writing and reading reviews for the providers, aggregating existing information, and finally choosing among the possible candidates. In this paper, we represent consumers’ reviews as machine processable structures using ontologies and develop a layered multiagent framework to enable consumers to find satisfactory service providers for their needs automatically. The framework can still function successfully when consumers evolve their language and when deceptive reviewers enter the system. We show the flexibility of the framework by employing different algorithms for various tasks and evaluate them for different circumstances.