Abstract: We examine the causal role of positive affect on creativity, the association of creativity or innovation in the ideation phase with functional emotional regulation, successful adjustment to stress and dispositional emotional creativity, as well as the predictive role of creativity for positive emotions and social adjustment. The study examines the effects of modification of positive affect on creativity. Participants write three poems, narrate an infatuation episode, answer a scale of personal growth after this episode and perform a creativity task, answer a flow scale after creativity task and fill a dispositional emotional creativity scale. High and low positive effect was induced by asking subjects to write three poems about high and low positive connotation stimuli. In a neutral condition, tasks were performed without previous affect induction. Subjects on the condition of high positive affect report more positive and less negative emotions, more personal growth (effect size r = .24) and their last poem was rated as more original by judges (effect size r = .33). Mediational analysis showed that positive emotions explain the influence of the manipulation on personal growth - positive affect correlates r = .33 to personal growth. The emotional creativity scale correlated to creativity scores of the creative task (r = .14), to the creativity of the narration of the infatuation episode (r = .21). Emotional creativity was also associated, during performing the creativity task, with flow (r = .27) and with affect balance (r = .26). The mediational analysis showed that emotional creativity predicts flow through positive affect. Results suggest that innovation in the phase of ideation is associated with a positive affect balance and satisfactory performance, as well as dispositional emotional creativity is adaptive.
Abstract: WiFi has become an essential technology that is widely used nowadays. It is famous due to its convenience to be used with mobile devices. This is especially true for Internet users worldwide that use WiFi connections. There are many location based services that are available nowadays which uses Wireless Fidelity (WiFi) signal fingerprinting. A common example that is gaining popularity in this era would be Foursquare. In this work, the WiFi signal would be used to estimate the user or client’s location. Similar to GPS, fingerprinting method needs a floor plan to increase the accuracy of location estimation. Still, the factor of inconsistent WiFi signal makes the estimation defer at different time intervals. Given so, an adaptive method is needed to obtain the most accurate signal at all times. WiFi signals are heavily distorted by external factors such as physical objects, radio frequency interference, electrical interference, and environmental factors to name a few. Due to these factors, this work uses a method of reducing the signal noise and estimation using the Nearest Neighbour based on past activities of the signal to increase the signal accuracy up to more than 80%. The repository yet increases the accuracy by using Artificial Neural Network (ANN) pattern matching. The repository acts as the server cum support of the client side application decision. Numerous previous works has adapted the methods of collecting signal strengths in the repository over the years, but mostly were just static. In this work, proposed solutions on how the adaptive method is done to match the signal received to the data in the repository are highlighted. With the said approach, location estimation can be done more accurately. Adaptive update allows the latest location fingerprint to be stored in the repository. Furthermore, any redundant location fingerprints are removed and only the updated version of the fingerprint is stored in the repository. How the location estimation of the user can be predicted would be highlighted more in the proposed solution section. After some studies on previous works, it is found that the Artificial Neural Network is the most feasible method to deploy in updating the repository and making it adaptive. The Artificial Neural Network functions are to do the pattern matching of the WiFi signal to the existing data available in the repository.
Abstract: This paper deals with the design of a periodic output
feedback controller for a flexible beam structure modeled with
Timoshenko beam theory, Finite Element Method, State space
methods and embedded piezoelectrics concept. The first 3 modes are
considered in modeling the beam. The main objective of this work is
to control the vibrations of the beam when subjected to an external
force. Shear piezoelectric sensors and actuators are embedded into
the top and bottom layers of a flexible aluminum beam structure, thus
making it intelligent and self-adaptive. The composite beam is
divided into 5 finite elements and the control actuator is placed at
finite element position 1, whereas the sensor is varied from position 2
to 5, i.e., from the nearby fixed end to the free end. 4 state space
SISO models are thus developed. Periodic Output Feedback (POF)
Controllers are designed for the 4 SISO models of the same plant to
control the flexural vibrations. The effect of placing the sensor at
different locations on the beam is observed and the performance of
the controller is evaluated for vibration control. Conclusions are
finally drawn.
Abstract: Based on the one-bit-matching principle and by turning the de-mixing matrix into an orthogonal matrix via certain normalization, Ma et al proposed a one-bit-matching learning algorithm on the Stiefel manifold for independent component analysis [8]. But this algorithm is not adaptive. In this paper, an algorithm which can extract kurtosis and its sign of each independent source component directly from observation data is firstly introduced.With the algorithm , the one-bit-matching learning algorithm is revised, so that it can make the blind separation on the Stiefel manifold implemented completely in the adaptive mode in the framework of natural gradient.
Abstract: Outlier detection in streaming data is very challenging because streaming data cannot be scanned multiple times and also new concepts may keep evolving. Irrelevant attributes can be termed as noisy attributes and such attributes further magnify the challenge of working with data streams. In this paper, we propose an unsupervised outlier detection scheme for streaming data. This scheme is based on clustering as clustering is an unsupervised data mining task and it does not require labeled data, both density based and partitioning clustering are combined for outlier detection. In this scheme partitioning clustering is also used to assign weights to attributes depending upon their respective relevance and weights are adaptive. Weighted attributes are helpful to reduce or remove the effect of noisy attributes. Keeping in view the challenges of streaming data, the proposed scheme is incremental and adaptive to concept evolution. Experimental results on synthetic and real world data sets show that our proposed approach outperforms other existing approach (CORM) in terms of outlier detection rate, false alarm rate, and increasing percentages of outliers.
Abstract: Internet computer games turn to be more and more
attractive within the context of technology enhanced learning.
Educational games as quizzes and quests have gained significant
success in appealing and motivating learners to study in a different
way and provoke steadily increasing interest in new methods of
application. Board games are specific group of games where figures
are manipulated in competitive play mode with race conditions on a
surface according predefined rules. The article represents a new,
formalized model of traditional quizzes, puzzles and quests shown as
multimedia board games which facilitates the construction process of
such games. Authors provide different examples of quizzes and their
models in order to demonstrate the model is quite general and does
support not only quizzes, mazes and quests but also any set of
teaching activities. The execution process of such models is
explained and, as well, how they can be useful for creation and
delivery of adaptive e-learning courseware.
Abstract: Sparse representation has long been studied and several
dictionary learning methods have been proposed. The dictionary
learning methods are widely used because they are adaptive. In this
paper, a new dictionary learning method for audio is proposed. Signals
are at first decomposed into different degrees of Intrinsic Mode
Functions (IMF) using Empirical Mode Decomposition (EMD)
technique. Then these IMFs form a learned dictionary. To reduce the
size of the dictionary, the K-means method is applied to the dictionary
to generate a K-EMD dictionary. Compared to K-SVD algorithm, the
K-EMD dictionary decomposes audio signals into structured
components, thus the sparsity of the representation is increased by
34.4% and the SNR of the recovered audio signals is increased by
20.9%.