Reviewerly/exHarmony huggingface dataset:
A combination of supervised dimensionality reduction and learning methods to forecast solar radiation

Abstract Machine learning is routinely used to forecast solar radiation from inputs, which are forecasts of meteorological variables provided by numerical weather prediction (NWP) models, on a spatially distributed grid. However, the number of features resulting from these grids is usually large, especially if several vertical levels are included. Principal Components Analysis (PCA) is one of the simplest and most widely-used methods to extract features and reduce dimensionality in renewable energy forecasting, although this method has some limitations. First, it performs a global linear analysis, and second it is an unsupervised method. Locality Preserving Projection (LPP) overcomes the locality problem, and recently the Linear Optimal Low-Rank (LOL) method has extended Linear Discriminant Analysis (LDA) to be applicable when the number of features is larger than the number of samples. Supervised Nonnegative Matrix Factorization (SNMF) also achieves this goal extending the Nonnegative Matrix Factorization (NMF) framework to integrate the logistic regression loss function. In this article we try to overcome all these issues together by proposing a Supervised Local Maximum Variance Preserving (SLMVP) method, a supervised non-linear method for feature extraction and dimensionality reduction. PCA, LPP, LOL, SNMF and SLMVP have been compared on Global Horizontal Irradiance (GHI) and Direct Normal Irradiance (DNI) radiation data at two different Iberian locations: Seville and Lisbon. Results show that for both kinds of radiation (GHI and DNI) and the two locations, SLMVP produces smaller MAE errors than PCA, LPP, LOL, and SNMF, around 4.92% better for Seville and 3.12% for Lisbon. It has also been shown that, although SLMVP, PCA, and LPP benefit from using a non-linear regression method (Gradient Boosting in this work), this benefit is larger for PCA and LPP because SMLVP is able to perform non-linear transformations of inputs.
Ship Target Detection in Optical Remote Sensing Images Based on Multiscale Feature Enhancement

Due to the multiscale characteristics of ship targets in ORSIs (optical remote sensing images), ship target detection in ORSIs based on depth learning is still facing great challenges. Aiming at the low accuracy of multiscale ship target detection in ORSIs, this paper proposes a ship target detection algorithm based on multiscale feature enhancement based on YOLO v4. Firstly, an improved mixed convolution is introduced into the IRes (inverted residual block) to form an MIRes (mixed inverted residual block). The MIRes are used to replace the Res (residual block) in the deep CSP module of the backbone network to enhance the multiscale feature extraction capability of the backbone network. Secondly, for different scale feature maps' perception fields, feature information, and the scale of the detected objects, the multiscale feature enhancement modules-SFEM (small scale feature enhancement module) and MFEM (middle scale feature enhancement module)-are proposed to enhance the feature information of the middle- and low-level feature maps, respectively, and then the enhanced feature maps are sent to the detection head for detection. Finally, experiments were implemented on the LEVIR-ship dataset and the NWPU VHR-10 dataset. The accuracy of the proposed algorithm in ship target detection reached 79.55% and 90.70%, respectively, which is improved by 3.25% and 3.56% compared with YOLO v4.
Solving TSP by using combinatorial Bees algorithm with nearest neighbor method

Abstract Bees Algorithm (BA) is a popular meta-heuristic method that has been used in many different optimization areas for years. In this study, a new version of combinatorial BA is proposed and explained in detail to solve Traveling Salesman Problems (TSPs). The nearest neighbor method was used in the population generation section of BA, and the Multi-Insert function was added to the local search section instead of the Swap function. To see the efficiency of the proposed method, 24 different TSPs were used in experimentation and the obtained results were compared with both classical combinatorial BA and other successful meta-heuristic methods. After detailed analyses and experimental studies on different problems, it has been observed that the proposed method performs well for TSPs and competes well with other methods.
Augmented human, extended machine

How can we trigger the process of digital embodiment and corporeality in human-robot collaboration through extended reality and digitally enhanced environments?
Safety compliance checking of construction behaviors using visual question answering

Unsafe construction behavior, one of the leading factors of accidents and casualties, can be reduced by strengthening construction inspection. However, current methods use either manual inspection or inefficient cross-modal models based on multiple backbone networks. To alleviate the problems, a "rule-question" transformation and annotation system is formulated, and the unsafe behavior detection is turned into a visual reasoning task: visual question answering (VQA). The VQA model is developed based on a vision-and-language Transformer, and the unsafe behavior could be identified based on the output answers. A dataset containing 16 safety rules and 2386 related construction images is used to fine-tune and validate the VQA model. The results show that the developed VQA model achieves an average recall of 0.81 at a faster reasoning speed. Finally, an applet for safety report generation is implemented to demonstrate the feasibility and practicability of the safety compliance checking based on VQA.
A deep encoder-decoder neural network model for total organic carbon content prediction from well logs

Total organic carbon (TOC) content is an important geochemical parameter for evaluating the hydrocarbon generation potential of unconventional oil and gas resources. The TOC content of source rocks significantly affects the responses of well logs so, in principle, well logs can be used for source rock appraisal. However, the complex relationships between TOC content and well logs involve nonlinear mapping with many parameters. It is sometimes difficult to obtain continuous and accurate TOC content values using conventional methods such as the ΔLogR method. In this study, we propose a TOC prediction model using a deep encoder-decoder neural network (DEDNN) based on mining and mapping of multiscale features of logging curves. The prediction performance of the model is validated by a series of tests using data from four exploration wells in the Longmaxi black shale in the Dingshan area of the Sichuan Basin. The TOC content prediction results confirm that the proposed DEDNN is more accurate than either the ΔLogR method or CNN, which is a state-of-the-art convolutional neural network. Furthermore, a saliency map derived from the DEDNN results shows the relative importance of different well logs to TOC contents.
An Extensive Framework Focused on Smart Agriculture Based Out of IoT

The world population is predicted to rise to more than 100 million, with agriculture food supply nearly projected to be 60% greater by 2060 than today. For the same materials, farmers have to produce more and supply a high volume of vegetables and fruits. Because of this pressure, one of the major aims of manufacturers is to find ways to maximise production while maintaining profitability. Agriculture is faced with a rejuvenation problem. Production has declined, but climatic change has also hindered data collection, storage, security, and sharing. In conventional agri-food systems, there is no link between the farmer and the consumer, meaning rises in input prices, including energy consumption restrictions. According to current IoT-based agriculture systems, these systems have a centralised structure and are not linked. This safeguards data integrity by ensuring there are still unaddressed issues and critical concerns, such as security and manipulation, as well as single points of failure. This study from our research group describes a future IoT using a blockchain paradigm to address these problems. Furthermore, it also offers new ways to encourage creativity. Very energy-efficient cluster assignment integrated approach to IoT agriculture that cuts energy usage and promotes network reliability. Poor-energy adoption clustering levels (PEACL) produce good outcomes. The findings of the simulation demonstrate that the suggested protocol network stability is 24% greater than PEACL as the first node to die in the event of PEACL failure, which occurs at around 158. IoT-based agriculture begins with the first node’s death occurring after 432 cycles. Still, the energy usage of IoT-based agriculture protocols is 78%, and PEACL’s is lower than that. An extension of the network life to additional rounds is also demonstrated, as well as an improvement in network lifetime.
The Health ChatBots in Telemedicine: Intelligent Dialog System for Remote Support

The objective of the Health Bot is to analyze and elaborate on the implementation of an intelligent system that can support telemedicine services using natural language processing (NLP) technology. Health Bot is a robust, modularized, and user-friendly platform that aims to improve the patient’s interactions with the health care system. Using NLP and speech recognition algorithms, the platform can analyze and classify free text and voice input data to symptoms. The classified data are used in the machine learning (ML) training process of the artificial intelligence (AI) models in order to predict the probability of the patient suffering from a specific disease and get alerted in case of disorders. The Health Bot is acting as a medical virtual agent in order to collect any information needed in the sense of a medical interview and provide medical assessments, close appointments with the doctors, and monitor/record the patient’s health condition. In this paper, two models have been trained for the Covid-19 and heart disease cases with 98.3% and 82% accuracy, respectively.
Mobile learning: Pre-service teachers’ perceptions of integrating iPads into future teaching

This study investigated pre-service teachers' perceptions of using iPads in teaching, with a focus on motivation to adopt iPads, iPad-integration self-efficacy, and intention to adopt iPads for future teaching. Changes of pre-service teachers' perceptions of using iPads over time as well as the relationships of motivation, self-efficacy, and intention for iPad adoption were examined. Participants were pre-service teachers from a university in the northeastern United States. Data were collected using online pre- and post-surveys. Quantitative and qualitative approaches were performed to analyze the data. Results indicated that there were significant changes in pre-service teachers' motivation and intention to adopt iPads before and after their participation in a mobile learning project. Motivation and iPad-integration self-efficacy were significant predictors of pre-service teachers' intention for future adoption. iPad ownership and prior experience with iPad-integrated lesson plans were potential factors that had a significant impact on pre-service teachers' perceptions of using iPads in future teaching. Pre-service teachers' perceived advantages and disadvantages of using iPads were reported and discussed. The findings of this study not only contribute to the understanding of iPad integration among pre-service teachers, but also provide an evidence on the positive influence of iPad-integrated activities on pre-service teachers' perceptions of using iPads for future teaching.
Full-field through-the-thickness strain distribution study of hybrid multi-bolted/bonded single-lap composite joints using digital image correlation

This study proposes a digital image correlation (DIC) technique for through-the-thickness deformations of multi-bolted single-lap (SL) shear hybrid bolted/bonded joints made of carbon/epoxy laminates. Through-the-thickness peel strains are obtained throughout the overlap region during the entire tensile test. It is observed that real-time monitoring of the strain in the overlap region informs on the location of the failure before the load reaches 50% of its ultimate value. The peel strain-load curves for both the overlap ends superimpose perfectly up to about 40% − 50% of the applied tensile load and diverge considerably beyond this level. Similar behavior occurs for shear strain and is confirmed for quasi-isotropic (QI) and cross-ply (CP) laminate stacking sequences. It is also found that peel and shear strains, as well as joint rotation in CP, are higher than those of QI. These results can be useful for the design of this type of joint.
Enhancing entanglement with the generalized elephant quantum walk from localized and delocalized states

Recently, it was introduced a generalization of a nonstandard step operator named the elephant quantum walk (EQW). With proper statistical distribution for the steps, that generalized EQW (gEQW) can be tuned to exhibit a myriad of dynamical scaling behavior ranging from standard diffusion to %and superdiffusion to ballistic and hyperballistic spreading. In this work, we study the influence of the statistics of the step size and the delocalization of the initial states on the entanglement entropy of the coin. Our results show that the gEQW generates maximally entangled states for almost all initial coin states and coin operators considering initially localized walkers and for the delocalized ones, taking the proper limit, the same condition is guaranteed. Differently from all the previous protocols that produce highly entangled states via QWs, this model is not upper-bounded by ballistic spreading and hence opens novel prospects for applications of dynamically disordered QWs as a robust maximal entanglement generator in programmable setups that ranges from slower-than-ballistic to faster-than ballistic.
A CNN Based Approach for the Point-Light Photometric Stereo Problem

Reconstructing the 3D shape of an object using several images under different light sources is a very challenging task, especially when realistic assumptions such as light propagation and attenuation, perspective viewing geometry and specular light reflection are considered. Many of works tackling Photometric Stereo (PS) problems often relax most of the aforementioned assumptions. Especially they ignore specular reflection and global illumination effects. In this work, we propose a CNN-based approach capable of handling these realistic assumptions by leveraging recent improvements of deep neural networks for far-field Photometric Stereo and adapt them to the point light setup. We achieve this by employing an iterative procedure of point-light PS for shape estimation which has two main steps. Firstly we train a per-pixel CNN to predict surface normals from reflectance samples. Secondly, we compute the depth by integrating the normal field in order to iteratively estimate light directions and attenuation which is used to compensate the input images to compute reflectance samples for the next iteration. Our approach sigificantly outperforms the state-of-the-art on the DiLiGenT real world dataset. Furthermore, in order to measure the performance of our approach for near-field point-light source PS data, we introduce LUCES the first real-world 'dataset for near-fieLd point light soUrCe photomEtric Stereo' of 14 objects of different materials were the effects of point light sources and perspective viewing are a lot more significant. Our approach also outperforms the competition on this dataset as well. Data and test code are available at the project page.
Facilitating Serverless Match-based Online Games with Novel Blockchain Technologies

Applying peer-to-peer (P2P) architecture to online video games has already attracted both academic and industrial interests, since it removes the need for expensive server maintenance. However, there are two major issues preventing the use of a P2P architecture, namely how to provide an effective distributed data storage solution, and how to tackle potential cheating behaviors. Inspired by emerging blockchain techniques, we propose a novel consensus model called Proof-of-Play (PoP) to provide a decentralized data storage system that incorporates an anti-cheating mechanism for P2P games, by rewarding players that interact with the game as intended, along with consideration of security measures to address the Nothing-at-stake Problem and the Long-range Attack. To validate our design, we utilize a game-theory model to show that under certain assumptions, the integrity of the PoP system would not be undermined due to the best interests of any user. Then, as a proof-of-concept, we developed a P2P game ( Infinity Battle ) to demonstrate how a game can be integrated with PoP in practice. Finally, experiments were conducted to study PoP in comparison with Proof-of-Work (PoW) to show its advantages in various aspects.
Ultrasonographic pathological grading of prostate cancer using automatic region-based Gleason grading network

The Gleason scoring system is a reliable method for quantifying the aggressiveness of prostate cancer, which provides an important reference value for clinical assessment on therapeutic strategies. However, to the best of our knowledge, no study has been done on the pathological grading of prostate cancer from single ultrasound images. In this work, a novel Automatic Region-based Gleason Grading (ARGG) network for prostate cancer based on deep learning is proposed. ARGG consists of two stages: (1) a region labeling object detection (RLOD) network is designed to label the prostate cancer lesion region; (2) a Gleason grading network (GNet) is proposed for pathological grading of prostate ultrasound images. In RLOD, a new feature fusion structure Skip-connected Feature Pyramid Network (CFPN) is proposed as an auxiliary branch for extracting features and enhancing the fusion of high-level features and low-level features, which helps to detect the small lesion and extract the image detail information. In GNet, we designed a synchronized pulse enhancement module (SPEM) based on pulse-coupled neural networks for enhancing the results of RLOD detection and used as training samples, and then fed the enhanced results and the original ones into the channel attention classification network (CACN), which introduces an attention mechanism to benefit the prediction of cancer grading. Experimental performance on the dataset of prostate ultrasound images collected from hospitals shows that the proposed Gleason grading model outperforms the manual diagnosis by physicians with a precision of 0.830. In addition, we have evaluated the lesions detection performance of RLOD, which achieves a mean Dice metric of 0.815.
Strong convergence and almost sure exponential stability of balanced numerical approximations to stochastic delay Hopfield neural networks

This paper deals with the balanced numerical schemes of the stochastic delay Hopfield neural networks. The balanced methods have a strong convergence rate of at least 12 and the balanced schemes have almost sure exponential stability under certain conditions. Under the Lipchitz and linear growth conditions, the balanced Euler methods are proved to have a strong convergence of order 12 in mean-square sense. Using the Lipchitz conditions on the various parameters of the model, based on the semimartingale convergence theorem and some reasonable assumptions, the balanced Euler methods of the stochastic delay Hopfield neural networks are proved to be almost sure exponentially stable. Numerical experiments are provided to illustrate the theoretical results which are derived in this paper. The computational efficiency of the balanced methods is demonstrated by numerical tests and compared to the Euler–Maruyama approximation scheme of the stochastic delay Hopfield neural networks. Furthermore, the obtained numerical results show that the balanced numerical methods of stochastic delay Hopfield neural networks are very efficient with the least error and have the best step size region for almost sure mean square stable.
Non-Fungible Tokens (NFTs) and Cryptocurrencies: Efficiency and Comovements

Non-fungible tokens (NFTs) are a type of digital record of ownership used in a unique way: ensuring authenticity and uniqueness. Due to these characteristics, NFTs have been used in several markets: games, arts, and sports, among others. In 2020, the volume of negotiations of the NFTs was about USD 200 million. Despite the strong interest of economic agents in operating with NFTs, there are still gaps in the literature, regarding their dynamics and price interrelation with other potentially related assets, which deserve to be studied. In this sense, the main purpose in this paper is to analyze the cross-correlation between NFTs and larger cryptocurrencies. To this end, our methodological approach is based on a Detrended Cross-Correlation Analysis correlation coefficient, with a sliding windows approach. Our main finding is that the cross-correlations are not significant, except for a few cryptocurrencies, with weak significance at some moments of time. We also carried out an analysis of the long-term memory of NFTs, which demonstrated the antipersistence of these assets, with results seemingly corroborating the market inefficiency hypothesis. Our results are particularly important for different classes of investors, due to the analysis on different time scales.
Comparing Approaches for Explaining DNN-Based Facial Expression Classifications

Classifying facial expressions is a vital part of developing systems capable of aptly interacting with users. In this field, the use of deep-learning models has become the standard. However, the inner workings of these models are unintelligible, which is an important issue when deploying them to high-stakes environments. Recent efforts to generate explanations for emotion classification systems have been focused on this type of models. In this work, an alternative way of explaining the decisions of a more conventional model based on geometric features is presented. We develop a geometric-features-based deep neural network (DNN) and a convolutional neural network (CNN). Ensuring a sufficient level of predictive accuracy, we analyze explainability using both objective quantitative criteria and a user study. Results indicate that the fidelity and accuracy scores of the explanations approximate the DNN well. From the performed user study, it becomes clear that the explanations increase the understanding of the DNN and that they are preferred over the explanations for the CNN, which are more commonly used. All scripts used in the study are publicly available.