Abstract
Software is a critical factor in the reliability of computer systems. While the development of hardware is assisted by mature science and engineering disciplines, software science is still in its infancy. This situation is likely to worsen in the future with quantum computer systems. Actually, if quantum computing is quickly coming of age, with potential groundbreaking impacts on many different fields, such benefits come at a price: quantum programming is hard and finding new quantum algorithms is far from straightforward. Thus, the need for suitable formal techniques in quantum software development is even bigger than in classical computation. A lack of reliable approaches to quantum computer programming will put at risk the expected quantum advantage of the new hardware. This position paper argues for the need for a proper quantum software engineering discipline benefiting from precise foundations and calculi, capable of supporting algorithm development and analysis. © 2020 ACM.

Abstract
This file is a public data set about literature problems and contributions on learning in environments where students move.

Abstract
Consuming 360 audiovisual content using a Head-Mounted Display (HMD) has become a standard feature for Immersive Virtual Reality (IVR). However, most applications rely only on visual and auditory feedback whereas other senses are often disregarded. The main goal of this work was to study the effect of tactile and olfactory stimuli on participants' sense of presence and cybersickness while watching a 360 video using an HMD-based IVR setup. An experiment with 48 participants and three experimental conditions (360 video, 360 video with olfactory stimulus, and 360 video with tactile stimulus) was performed. Presence and cybersickness were reported via post-test questionnaires. Statistical analysis showed a significant difference in presence between the control and the olfactory conditions. From the control to the tactile condition, mean values were higher but failed to show statistical significance. Thus, results suggest that adding an olfactory stimulus increases presence significantly while the addition of a tactile stimulus only shows a positive effect. Regarding cybersickness, no significant differences were found across conditions. We conclude that an olfactory stimulus contributes to higher presence and that a tactile stimulus, delivered in the form of cutaneous perception of wind, has no influence in presence. We further conclude that multisensory cues do not affect cybersickness.

Abstract
Background As of today, cancer is still one of the most prevalent and high-mortality diseases, summing more than 9 million deaths in 2018. This has motivated researchers to study the application of machine learning-based solutions for cancer detection to accelerate its diagnosis and help its prevention. Among several approaches, one is to automatically classify tumor samples through their gene expression analysis. Methods In this work, we aim to distinguish five different types of cancer through RNA-Seq datasets: thyroid, skin, stomach, breast, and lung. To do so, we have adopted a previously described methodology, with which we compare the performance of 3 different autoencoders (AEs) used as a deep neural network weight initialization technique. Our experiments consist in assessing two different approaches when training the classification model - fixing the weights after pre-training the AEs, or allowing fine-tuning of the entire network - and two different strategies for embedding the AEs into the classification network, namely by only importing the encoding layers, or by inserting the complete AE. We then study how varying the number of layers in the first strategy, the AEs latent vector dimension, and the imputation technique in the data preprocessing step impacts the network's overall classification performance. Finally, with the goal of assessing how well does this pipeline generalize, we apply the same methodology to two additional datasets that include features extracted from images of malaria thin blood smears, and breast masses cell nuclei. We also discard the possibility of overfitting by using held-out test sets in the images datasets. Results The methodology attained good overall results for both RNA-Seq and image extracted data. We outperformed the established baseline for all the considered datasets, achieving an average F(1)score of 99.03, 89.95, and 98.84 and an MCC of 0.99, 0.84, and 0.98, for the RNA-Seq (when detecting thyroid cancer), the Malaria, and the Wisconsin Breast Cancer data, respectively. Conclusions We observed that the approach of fine-tuning the weights of the top layers imported from the AE reached higher results, for all the presented experiences, and all the considered datasets. We outperformed all the previous reported results when comparing to the established baselines.

Abstract
In critical industrial monitoring and control applications, dependability evaluation will be usually required. For wireless sensor networks deployed in industrial plants, dependability evaluation can provide valuable information, enabling proper preventive or contingency measures to assure their correct and safe operation. However, when employing sensor nodes equipped with cameras, visual coverage failures may have a deep impact on the perceived quality of industrial applications, besides the already expected impacts of hardware and connectivity failures. This article proposes a comprehensive mathematical model for dependability evaluation centered on the concept of Quality of Monitoring (QoM), processing availability, reliability and effective coverage parameters in a combined way. Practical evaluation issues are discussed and simulation results are presented to demonstrate how the proposed model can be applied in wireless industrial sensor networks when assessing and enhancing their dependability.

Abstract
Ultra-short-term photovoltaic (PV) power forecasting can support the real-time dispatching of power grid and the optimal operation of PV power station itself. However, due to various meteorological factors, the photovoltaic power has great fluctuations. To improve the refined ultra-short-term forecasting technology of PV power, this paper proposes an ultra-short-term forecasting model of PV power based on optimal frequency-domain decomposition and deep learning. First, the amplitude and phase of each frequency sine wave is obtained by fast Fourier decomposition. As the frequency demarcation point is different, the correlation between the decomposition component and the original data is analyzed. By minimizing the square of the difference that the correlation between low-frequency components and raw data is subtracted from the correlation between high-frequency components and raw data, the optimal frequency demarcation points for decomposition components are obtained. Then convolutional neural network is used to predict low-frequency component and high-frequency component, and final forecasting result is obtained by addition reconstruction. Finally, the paper compares forecasting results of the proposed model and the non-spectrum analysis model in the case of predicting the 1 hour, 2 hours, 3 hours, and 4 hours. The results fully show that the proposed model improves forecasting accuracy.