Patterns for self-adaptive systems: agent-based simulations

Self-adaptive systems are distributed computing systems that can adapt their behavior and structure to different kinds of conditions. This adaptation does not concern the single components only, but the entire system. In a previous work we have identified several patterns for self-adaptation, classifying them by means of a taxonomy, which aims at being a support for developers of self-adaptive systems. Starting from that theoretical work, we have simulated the described self-adaptation patterns, in order to better understand the concrete and real features of each pattern. The contribution of this paper is to report about the simulation work of three patterns as examples, detailing how it was carried out, in order to provide a further support for the developers

Encouraging persons to visit cultural sites through mini-games

Gamification has been recently proposed as a technique to improve user engagement in different activities, including visits to cultural sites and cultural tourism in general. We present the design, development and initial validation of the NEPTIS Poleis system, which consists of a mobile application and a Web interface for curators, allowing the definition, and subsequent fruition by users, of different minigames suitable for open-air assets

Accessible and usable websites and mobile applications for people with autism spectrum disorders: A comparative study

Accessibility, usability and inclusion represent desirable challenges of current research in the field of universal design: in some cases, these features require adaptive behaviours and specialised customisations, while, in general, it is possible to identify common and hareable guidelines. We focus our attention on hildren with autism spectrum disorders. Many studies show the positive impact of using computer technologies for supporting the lives of these users. Despite that, just a restricted part of the current websites and apps is accessible and usable for people with ASD. In this paper, we present general and shared guidelines and best practices for accessibility and usability for all; and we propose specialised guidelines for designers and developers of websites and mobile applications for users with ASD. We then present a review of many of the existing websites and applications, in order to check which comply with all, or parts of these guidelines

An Ambient Assisted Living Technology Platform for Informal Carers of the Elderly - iCarer

For most families with elderly relatives, care within their own home is by far the most preferred option -both for the elderly and their carers. However, frequently these carers are the partners of the person with long-term care needs, and themselves are elderly and in need of support to cope with the burdens and stress associated with these duties. When it becomes too much for them, they may have to rely on professional care services, or even use residential care for a respite. In order to support the carers as well as the elderly person, an ambient assisted living platform has been developed. The system records information about the activities of daily living using unobtrusive sensors within the home, and allows the carers to record their own wellbeing state. By providing facilities to schedule and monitor the activities of daily care, and providing orientation and advice to improve the care given and their own wellbeing, the system helps to reduce the burden on the informal carers

Adaptive Boltzmann Medical Dataset Machine Learning

The RBM is a stochastic energy-based model of an unsupervised neural network (RBM). RBM is a key pre-training for Deep Learning. Structure of RBM includes weights and coefficients for neurons. Better network structure allows us to examine data more thoroughly, which is good. We looked at the variance of parameters in learning on demand to fix the problem. To determine why RBM's energy function fluctuates, we'll look at its parameter variance. A neuron generation and annihilation algorithm is smeared with an adaptive RBM learning method to determine the optimal number of hidden neurons for attribute imputation during training. When the energy function isn't converged and parameter variance is high, a hidden neuron is generated. If the neuron doesn't disrupt learning, it'll destroy the hidden neuron. In this study, some yardstick PIMA data sets were tested

Performance Evaluation of Spatial Modulation and QOSTBC for MIMO Systems

YesMultiple-input multiple-output (MIMO) systems require simplified architectures that can maximize design parameters without sacrificing system performance. Such architectures may be used in a transmitter or a receiver. The most recent example with possible low cost architecture in the transmitter is spatial modulation (SM). In this study, we evaluate the SM and quasi-orthogonal space time block codes (QOSTBC) schemes for MIMO systems over a Rayleigh fading channel. QOSTBC enables STBC to be used in a four antenna design, for example. Standard QO-STBC techniques are limited in performance due to self-interference terms; here a QOSTBC scheme that eliminates these terms in its decoding matrix is explored. In addition, while most QOSTBC studies mainly explore performance improvements with different code structures, here we have implemented receiver diversity using maximal ratio combining (MRC). Results show that QOSTBC delivers better performance, at spectral efficiency comparable with SM

Internet traffic prediction using recurrent neural networks

Network traffic prediction (NTP) represents an essential component in planning large-scale networks which are in general unpredictable and must adapt to unforeseen circumstances. In small to medium-size networks, the administrator can anticipate the fluctuations in traffic without the need of using forecasting tools, but in the scenario of large-scale networks where hundreds of new users can be added in a matter of weeks, more efficient forecasting tools are required to avoid congestion and over provisioning. Network and hardware resources are however limited; and hence resource allocation is critical for the NTP with scalable solutions. To this end, in this paper, we propose an efficient NTP by optimizing recurrent neural networks (RNNs) to analyse the traffic patterns that occur inside flow time series, and predict future samples based on the history of the traffic that was used for training. The predicted traffic with the proposed RNNs is compared with the real values that are stored in the database in terms of mean squared error, mean absolute error and categorical cross entropy. Furthermore, the real traffic samples for NTP training are compared with those from other techniques such as auto-regressive moving average (ARIMA) and AdaBoost regressor to validate the effectiveness of the proposed method. It is shown that the proposed RNN achieves a better performance than both the ARIMA and AdaBoost regressor when more samples are employed

The Immersive Education Laboratory: understanding affordances, structuring experiences, and creating constructivist, collaborative processes, in mixed-reality smart environments

In this paper we describe how the iClassroom and other technologies are providing the testbed through which we are able to design, develop, and research future intelligent environments. We describe the process of distinguishing between the technical and pedagogical aspects of immersive learning environments, while simultaneously considering both in the redefinition of effective intelligent learning spaces. This paper describes how our laboratory is working on specific projects that increase our understanding of the distinct advantages of technical design elements, like immersive visual displays, and pedagogical design elements that need to be in place as we go through the process of structuring learning situations that create constructivist, collaborative experiences. We describe specific technologies and their design across these multiple dimensions and the ways in which they are helping us better understand how to maximize technological affordances for increased positive learning outcomes. Finally, through this design research process, as we begin to better understand the affordances and iteratively create design guidelines, our hope is that eventually a prescriptive framework emerges that informs both the practice of embedded technology development and the deliberate incorporation of technical attributes into both the educational space and the pedagogy through which students learn

Advancing performability in playable media : a simulation-based interface as a dynamic score

When designing playable media with non-game orientation, alternative play scenarios to gameplay scenarios must be accompanied by alternative mechanics to game mechanics. Problems of designing playable media with non-game orientation are stated as the problems of designing a platform for creative explorations and creative expressions. For such design problems, two requirements are articulated: 1) play state transitions must be dynamic in non-trivial ways in order to achieve a significant level of engagement, and 2) pathways for players’ experience from exploration to expression must be provided. The transformative pathway from creative exploration to creative expression is analogous to pathways for game players’ skill acquisition in gameplay. The paper first describes a concept of simulation-based interface, and then binds that concept with the concept of dynamic score. The former partially accounts for the first requirement, the latter the second requirement. The paper describes the prototype and realization of the two concepts’ binding. “Score” is here defined as a representation of cue organization through a transmodal abstraction. A simulation based interface is presented with swarm mechanics and its function as a dynamic score is demonstrated with an interactive musical composition and performance

Analysis of Improved Particle Swarm Algorithm in Wireless Sensor Network Localization

WSN localization occupies an important position in the practical application of WSN. To complete WSN localization efficiently and accurately, the article constructs the objective function based on the target node location constraints and maximum likelihood function. It avoids premature convergence through the PSO algorithm based on chaos search and backward learning. Based on linear fitting, the node-flipping fuzzy detection method is proposed to perform the judgment of node flipping fuzzy phenomenon. And the detection method is combined with the localization algorithm, and the final WSN localization algorithm is obtained after multi-threshold processing. After analysis, it is found that compared with other PSO algorithms, the MTLFPSO algorithm used in the paper has better performance with the highest accuracy of 83.1%. Different threshold values will affect the favorable and error detection rates of different WSNs. For type 1 WSNs, the positive detection rate of the 3-node network is the highest under the same threshold value, followed by the 4-node network; when the threshold value is 7.5 (3 ), the positive detection rate of the 3-node network is 97.8%. Different numbers of anchor nodes and communication radius will have specific effects on the number of definable nodes and relative localization error, in which the lowest relative localization error of the MTLFPSO algorithm is 3.4% under different numbers of anchor nodes; the lowest relative localization error of MTLFPSO algorithm is 2.5% under different communication radius. The article adopts the method to achieve accurate and efficient localization of WSNs