Comparing end-user and intelligent remote control interface generation

Traditional remote controls typically allow users to activate functionality of a single device. Given that users activate a subset of functionality across devices to accomplish a particular task, it is attractive to consider a remote control directly supporting this behavior. We present qualitative and quantitative results from a study of two promising approaches creating such a remote control: end-user programming and machine learning. In general, results show that each approach possesses advantages and disadvantages, and that neither is optimal.     

A software infrastructure for supporting spontaneous and personalized interaction in home computing environments

Our daily lives are expected to change dramatically due to the popularity of ubiquitous computing technologies. These will make it possible to integrate various aspects of our lives. However, a new approach is required to seamlessly deal with devices embedded in our environments. Future embedded systems will embody a new approach that will take into account a variety of new issues, for example, spontaneous interaction, personalization, privacy protection, and interoperability. In this paper, we propose a personal home server that will make it possible to coordinate home appliances. Since everyone will have a different personal home server, it will allow us to personalize how the appliances are used according to individual preferences. Our personal home server can seamlessly discover and configure appliances at any locations, such as at railroad stations, cars, and streets as well as houses. We also discuss its design and implementation and present its current status.     

Ubi-App: a ubiquitous application for universal access from handheld devices

Universal access from a handheld device (such as a PDA, cell phone) at any time or anywhere is now a reality. Ubicomp Assistant (UA) (Sharmin et al. in Proceedings of the 21st annual ACM symposium on applied computing (ACM SAC 2006), Dijon, France, pp 1013–1017, 2006) is an integral service of MARKS (Sharmin et al. in Proceedings of the third international conference on information technology: new generations (ITNG 2006), Las Vegas, Nevada, USA, pp 306–313, 2006). It is a middleware developed for handheld devices, and has been designed to accommodate different types of users (e.g., education, healthcare, marketing, or business). This customizable service employs the ubiquitous nature of current short range, low-power wireless connectivity and readily available, low-cost lightweight mobile devices. These devices can reach other neighbouring devices using a free short-range ad hoc network. To the best of the authors’ knowledge, the UA service is the only service designed for these devices. This paper presents the details of Ubi-App, a ubiquitous application for universal access from any handheld device, which uses UA as a service. The results of a usability test and performance evaluation of the prototype show that Ubi-App is useful, easy to use, easy to install, and does not degrade the performance of the device.     

Location awareness through trajectory prediction

Location-aware computing is a type of ubiquitous computing that uses user’s location information as an essential parameter for providing services and application-related optimization. Location management plays an important role in location-aware computing because the provision of services requires convenient access to dynamic location and location-dependent information. Many existing location management strategies are passive since they rely on system capability to periodically record current location information. In contrast, active strategies predict user movement through trajectories and locations. Trajectory prediction provides richer location and context information and facilitates the means for adapting to future locations. In this paper, we present two models for trajectory prediction, namely probability-based model and learning-based model. We analyze these two models and conduct experiments to test their performances in location-aware systems.     

A cube to learn: a tangible user interface for the design of a learning appliance

In this paper we introduce the design and development of the Learning Cube as a novel tangible learning appliance. Using the common shape of a cube we implemented a general learning platform that supports test based quizzes where questions and answers can be text or image based. Exploiting the physical affordances of the cube and augmenting it with embedded sensors and LCD displays placed on each face, we present different learning appliances as playful learning interfaces for children. Based on the initial observations of the experience with children, we argue that breaking conventions about how a computer has to look like, and providing children with a playful interface is a promising approach to embed and integrate technology into children’s everyday context and activities.     

Empirical prediction models for adaptive resource provisioning in the cloud

Cloud computing allows dynamic resource scaling for enterprise online transaction systems, one of the key characteristics that differentiates the cloud from the traditional computing paradigm. However, initializing a new virtual instance in a cloud is not instantaneous; cloud hosting platforms introduce several minutes delay in the hardware resource allocation. In this paper, we develop prediction-based resource measurement and provisioning strategies using Neural Network and Linear Regression to satisfy upcoming resource demands.Experimental results demonstrate that the proposed technique offers more adaptive resource management for applications hosted in the cloud environment, an important mechanism to achieve on-demand resource allocation in the cloud.     

A middleware framework for application-aware and user-specific energy optimization in smart mobile devices

Mobile battery-operated devices are becoming an essential instrument for business, communication, and social interaction. In addition to the demand for an acceptable level of performance and a comprehensive set of features, users often desire extended battery lifetime. In fact, limited battery lifetime is one of the biggest obstacles facing the current utility and future growth of increasingly sophisticated “smart” mobile devices. This paper proposes a novel application-aware and user-interaction aware energy optimization middleware framework (AURA) for pervasive mobile devices. AURA optimizes CPU and screen backlight energy consumption while maintaining a minimum acceptable level of performance. The proposed framework employs a novel Bayesian application classifier and management strategies based on Markov Decision Processes and Q-Learning to achieve energy savings. Real-world user evaluation studies on Google Android based HTC Dream and Google Nexus One smartphones running the AURA framework demonstrate promising results, with up to 29% energy savings compared to the baseline device manager, and up to 5×savings over prior work on CPU and backlight energy co-optimization.     

Analysing and evaluating the task of automatic tweet generation: Knowledge to business

In this paper a study concerning the evaluation and analysis of natural language tweets is presented. Based on our experience in text summarisation, we carry out a deep analysis on user's perception through the evaluation of tweets manual and automatically generated from news. Specifically, we consider two key issues of a tweet: its informativeness and its interestingness. Therefore, we analyse: (1) do users equally perceive manual and automatic tweets?; (2) what linguistic features a good tweet may have to be interesting, as well as informative? The main challenge of this proposal is the analysis of tweets to help companies in their positioning and reputation on the Web. Our results show that: (1) automatically informative and interesting natural language tweets can be generated as a result of summarisation approaches; and (2) we can characterise good and bad tweets based on specific linguistic features not present in other types of tweets.     

Evaluation of four cursor control devices during a target acquisition task for laparoscopic tool control

Current laparoscopic surgery instruments create awkward postures which produce fatigue and pressure points in surgeons. In order to alleviate some of this discomfort a new laparoscopic tool had been developed with the inclusion of an articulating end-effector manipulated by a trackball. The current study was developed to access the performance of four input devices which could replace the manual trackball in a powered laparoscopic tool. A simple Fitts’ law task was conducted and the devices’ performance was evaluated with both subjective and objective measures. This article makes three main contributions to the scientific community. First, it provides a comparison of four control devices (TouchPad, Mouse Button Module, MiniJoystick Module and MicroJoystick) for use in a powered laparoscopic tool. Second, it provides an understanding of how the non-traditional measure of target re-entry can be utilized to compare control devices and how this relates to the more traditional measures of throughput and error rate. Finally, it contributes to the understanding of how a user's familiarity with a control device could affect the subjective and objective performance of the device. The main results indicate that the TouchPad and MicroJoystick are the best candidate-devices for use in a powered laparoscopic tool. The article also provides support for utilizing the new measure target re-entry when comparing control performance. Although studied in the application of laparoscopic surgery, the results can be generalized for the design of any hand-held device in which the speed and accuracy of the control device is critical.     

Complementary computing: policies for transferring callers from dialog systems to human receptionists

We describe a study of the use of decision-theoretic policies for optimally joining human and automated problem-solving efforts. We focus specifically on the challenge of determining when it is best to transfer callers from an automated dialog system to human receptionists. We demonstrate the sensitivities of transfer actions to both the inferred competency of the spoken-dialog models and the current sensed load on human receptionists. The policies draw upon probabilistic models constructed via machine learning from cases that were logged by a call routing service deployed at our organization. We describe the learning of models that predict outcomes and interaction times and show how these models can be used to generate expected-utility policies that identify when it is best to transfer callers to human operators. We explore the behavior of the policies with simulations constructed from real-world call data. See D’Agostino (2005) for a reflection from the business community about the failure to date of automated speech recognition systems to penetrate widely.     

A Literature Survey on Applications of Neural Networks for Human-Computer Interaction

This article presents an overview of the neural networks approach to user modelling and intelligent interface. We analyse and discuss activities in user modelling and intelligent interface. Activities of various neural networks models are introduced to illustrate how user modelling problems can be solved by neural networks. The practical utility of neural networks in supporting user modelling and intelligent interface is demonstrated by reviewing a selection of neural networks developed in this area. Structured summaries are provided for comparative purpose.     

From Aardvark to Zorro: A Benchmark for Mammal Image Classification

Current object recognition systems aim at recognizing numerous object classes under limited supervision conditions. This paper provides a benchmark for evaluating progress on this fundamental task. Several methods have recently proposed to utilize the commonalities between object classes in order to improve generalization accuracy. Such methods can be termed interclass transfer techniques. However, it is currently difficult to asses which of the proposed methods maximally utilizes the shared structure of related classes. In order to facilitate the development, as well as the assessment of methods for dealing with multiple related classes, a new dataset including images of several hundred mammal classes, is provided, together with preliminary results of its use. The images in this dataset are organized into five levels of variability, and their labels include information on the objects’ identity, location and pose. From this dataset, a classification benchmark has been derived, requiring fine distinctions between 72 mammal classes. It is then demonstrated that a recognition method which is highly successful on the Caltech101, attains limited accuracy on the current benchmark (36.5%). Since this method does not utilize the shared structure between classes, the question remains as to whether interclass transfer methods can increase the accuracy to the level of human performance (90%). We suggest that a labeled benchmark of the type provided, containing a large number of related classes is crucial for the development and evaluation of classification methods which make efficient use of interclass transfer.     

From task parameters to motor synergies: A hierarchical framework for approximately-optimal control of redundant manipulators

We present a hierarchical framework for approximately-optimal control of redundant manipulators. The plant is augmented with a low-level feedback controller, designed to yield input-output behavior that captures the task-relevant aspects of plant dynamics but has reduced dimensionality. This makes it possible to reformulate the optimal control problem in terms of the augmented dynamics, and optimize a high-level feedback controller without running into the curse of dimensionality. The resulting control hierarchy compares favorably to existing methods in robotics. Furthermore we demonstrate a number of similarities to (non-hierarchical) optimal feedback control. Besides its engineering applications, the new framework addresses a key unresolved problem in the neural control of movement. It has long been hypothesized that coordination involves selective control of task parameters via muscle synergies, but the link between these parameters and the synergies capable of controlling them has remained elusive. Our framework provides this missing link.     

Data mining for quality control: Burr detection in the drilling process

Drilling process is one of the most important operations in aeronautic industry. It is performed on the wings of the aeroplanes and its main problem lies with the burr generation. At present moment, there is a visual inspection and manual burr elimination task subsequent to the drilling and previous to the riveting to ensure the quality of the product. These operations increase the cost and the resources required during the process. The article shows the use of data mining techniques to obtain a reliable model to detect the generation of burr during high speed drilling in dry conditions on aluminium Al 7075-T6. It makes possible to eliminate the unproductive operations in order to optimize the process and reduce economic cost. Furthermore, this model should be able to be implemented later in a monitoring system to detect automatically and on-line when the generated burr is out of tolerance limits or not. The article explains the whole process of data analysis from the data preparation to the evaluation and selection of the final model.     

QuesTInSitu: From tests to routes for assessment in situ activities

Test-based assessment tools are mostly focused on the use of computers. However, advanced Information and Communication Technologies, such as handheld devices, opens up the possibilities of creating new assessment scenarios, increasing the teachers’ choices to design more appropriate tests for their subject areas. In this paper we use the term Computing-Based Testing (CBT) instead of Computer-Based Testing, as it captures better the emerging trends. Within the CBT context, the paper is centered on proposing an approach for “Assessment in situ” activities, where questions have to be answered in front of a real space/location (situ). In particular, we present the QuesTInSitu software implementation that includes both an editor and a player based on the IMS Question and Test Interoperability specification and GoogleMaps. With QuesTInSitu teachers can create geolocated questions and tests (routes), and students can answer the tests using mobile devices with GPS when following a route. Three illustrating scenarios and the results from the implementation of one of them in a real educational situation show that QuesTInSitu enables the creation of innovative, enriched and context-aware assessment activities. The results also indicate that the use of mobile devices and location-based systems in assessment activities facilitates students to put explorative and spatial skills into practice and fosters their motivation, reflection and personal observation.     

From participatory design to participating problem solving: Enhancing system adaptability through user modelling

The issue on the role of users in knowledge-based systems can be investigated from two aspects: the design aspect and the functionality aspect. Participatory design is an important approach for the first aspect while system adaptability supported by user modelling is crucial to the second aspect. In the article, we discuss the second aspect. We view a knowledge-based computer system as the partner of users' problem-solving process, and we argue that the system functionality can be enhanced by adapting the behaviour of the system to fit the needs of users with different profiles. We emphasise that the notion of user modelling is crucial to realise such kind of flexibility. User modelling will be beneficial to the user, not only through adaptive interfaces, but also through the enhanced system adaptability. In a knowledge-based system, by incorporating user models, searching can be reduced to a smaller portion in the knowledge-base, thus enhancing system functionality. In other words, user modelling is incorporated to realise flexible inference control to achieve system adaptability. An example is provided, and a general conceptual model is sketched. We conclude this paper by emphasising that the design aspect and functionality aspect are complementary. Achieving enhanced functionality through joint efforts of computers and human users indicates a kind of participatory execution of computerised problem-solving or participatory problem-solving.     

Fast arithmetic for triangular sets: From theory to practice

We study arithmetic operations for triangular families of polynomials, concentrating on multiplication in dimension zero. By a suitable extension of fast univariate Euclidean division, we obtain theoretical and practical improvements over a direct recursive approach; for a family of special cases, we reach quasi-linear complexity. The main outcome we have in mind is the acceleration of higher-level algorithms, by interfacing our low-level implementation with languages such as AXIOM or Maple. We show the potential for huge speed-ups, by comparing two AXIOM implementations of van Hoeij and Monagan’s modular GCD algorithm.     

Fusion of learning automata theory and granular inference systems: ANLAGIS. Applications to pattern recognition and machine learning

In this paper the fusion of artificial neural networks, granular computing and learning automata theory is proposed and we present as a final result ANLAGIS, an adaptive neuron-like network based on learning automata and granular inference systems. ANLAGIS can be applied to both pattern recognition and learning control problems. Another interesting contribution of this paper is the distinction between pre-synaptic and post-synaptic learning in artificial neural networks. To illustrate the capabilities of ANLAGIS some experiments on knowledge discovery in data mining and machine learning are presented. The main, novel contribution of ANLAGIS is the incorporation of Learning Automata Theory within its structure; the paper includes also a novel learning scheme for stochastic learning automata.     

Attuning speech-enabled interfaces to user and context for inclusive design: technology, methodology and practice

This paper presents a methodology to apply speech technology for compensating sensory, motor, cognitive and affective usage difficulties. It distinguishes (1) an analysis of accessibility and technological issues for the identification of context-dependent user needs and corresponding opportunities to include speech in multimodal user interfaces, and (2) an iterative generate-and-test process to refine the interface prototype and its design rationale. Best practices show that such inclusion of speech technology, although still imperfect in itself, can enhance both the functional and affective information and communication technology-experiences of specific user groups, such as persons with reading difficulties, hearing-impaired, intellectually disabled, children and older adults.

Evolutionary multi-feature construction for data reduction: A case study

Real-world data are often prepared for purposes other than data mining and machine learning and, therefore, are represented by primitive attributes. When data representation is primitive, preprocessing data before looking for patterns becomes necessary. The low-level primitive representation of real-world problems facilitates the existence of complex interactions among attributes. If lack of domain experts prevents traditional methods to uncover patterns in data due to complex attribute interactions, then the use of soft computing techniques such as genetic algorithms becomes necessary. This article introduces MFE3/GA^DR, a data reduction method derived from the learning preprocessing system MFE3/GA. The method restructures the primitive data representation by capturing and compacting hidden information into new features in order to highlight regularities to the learner. We thoroughly analyze the empirical results obtained on the poker hand data set. The results show that this approach successfully compacts the set of low-level primitive attributes into a smaller set of highly informative features which outline patterns to the learner; thus, the new approach provides data reduction and yields learning a smaller and more accurate classifier.