Reviewerly/exHarmony huggingface dataset:
FPV for Sustainable Electricity Generation in a Large European City

There is a huge interest worldwide related to continuously increasing the use of renewable energy sources (RES) for electricity generation. Romania, at this moment, even though it has to attain a share of 30.7% of RES from total energy consumption by 2030, does not have any major investment project of this kind in the near future. Photovoltaic (PV) is one of the most promising technologies, with decreasing prices for PV panels but with the disadvantage of large, needed surfaces. This study presents a solution to install PV panels where there is a need for electricity, in a large city, by using the surface of a reservoir. “Lacul Morii” Reservoir in Bucharest is the choice for the case study. The insolation and the possibility to install floating PV, as well as electricity generation, benefits for water quality and carbon dioxide (CO2) emissions reduction are assessed, and even the installation of charging stations for electric bikes and cars. The results are very promising, and the main conclusion is that, after its realization, the floating photovoltaic (FPV) plant on “Lacul Morii” Reservoir will be a source of clean electricity and a demonstration project on how to benefit from solar energy to produce electricity in large cities where there are reservoirs.
Compositional baseline assessments to address soil pollution: An application in Langreo, Spain

Potentially Toxic Elements (PTEs) are contaminants with high toxicity and complex geochemical behaviour and, therefore, high PTEs contents in soil may affect ecosystems and/or human health. However, before addressing the measurement of soil pollution, it is necessary to understand what is meant by pollution-free soil. Often, this background, or pollution baseline, is undefined or only partially known. Since the concentration of chemical elements is compositional, as the attributes vary together, here we present a novel approach to build compositional indicators based on Compositional Data (CoDa) principles. The steps of this new methodology are: 1) Exploratory data analysis through variation matrix, biplots or CoDa dendrograms; 2) Selection of geological background in terms of a trimmed subsample that can be assumed as non-pollutant; 3) Computing the spread Aitchison distance from each sample point to the trimmed sample; 4) Performing a compositional balance able to predict the Aitchison distance computed in step 3.Identifying a compositional balance, including pollutant and non-pollutant elements, with sparsity and simplicity as properties, is crucial for the construction of a Compositional Pollution Indicator (CI). Here we explored a database of 150 soil samples and 37 chemical elements from the contaminated region of Langreo, Northwestern Spain. There were obtained three Cis: the first two using elements obtained through CoDa analysis, and the third one selecting a list of pollutants and non-pollutants based on expert knowledge and previous studies. The three indicators went through a Stochastic Sequential Gaussian simulation. The results of the 100 computed simulations are summarized through mean image maps and probability maps of exceeding a given threshold, thus allowing characterization of the spatial distribution and variability of the CIs. A better understanding of the trends of relative enrichment and PTEs fate is discussed.
Deciphering anomalous Ag enrichment recorded by galena in Dayingezhuang Au(-Ag) deposit, Jiaodong Peninsula, Eastern China

Texture, geochemistry, and in-situ Pb isotope of galena were investigated to probe the origin of anomalous Ag enrichment in the DayingezhuangAu(-Ag) deposit. Silver enrichment postdates the main Au mineralization and occurs in the south of the Dayingezhuang deposit. It is primarily associated with galena and the exsolution of Ag-rich sulfosalts (e.g., matildite) in distal vein-ores related to steeply dipping brittle fractures. Silver-rich galena is characterized by the least radiogenic Pb isotope signature (206Pb/204Pb 17.195–17.258 and 208Pb/204Pb 37.706–37.793), possibly indicating a metasomatized lithospheric mantle or modified lower crustal source for Pb and Ag. Both of these mafic and ultramafic source regions have been previously suggested as Au reservoirs for other Jiaodong Au deposits, implying that the metal reservoir has only a weak control on the uneven Ag-enrichment. Since the Ag-enrichment areas are located in the footwalls of both the Dayingezhuang and Zhaoping faults, the enrichment was most likely dominated by local rotational stress during coeval movements of the two faults in a NE–SW compression and NW−SE extension regime. This work highlights the shallow-crust structural deformation responsible for controlling the flow of late ore-forming fluid resulting in local anomalous metal enrichment.
Post-Quantum and Code-Based Cryptography—Some Prospective Research Directions

Cryptography has been used from time immemorial for preserving the confidentiality of data/information in storage or transit. Thus, cryptography research has also been evolving from the classical Caesar cipher to the modern cryptosystems, based on modular arithmetic to the contemporary cryptosystems based on quantum computing. The emergence of quantum computing poses a major threat to the modern cryptosystems based on modular arithmetic, whereby even the computationally hard problems which constitute the strength of the modular arithmetic ciphers could be solved in polynomial time. This threat triggered post-quantum cryptography research to design and develop post-quantum algorithms that can withstand quantum computing attacks. This paper provides an overview of the various research directions that have been explored in post-quantum cryptography and, specifically, the various code-based cryptography research dimensions that have been explored. Some potential research directions that are yet to be explored in code-based cryptography research from the perspective of codes is a key contribution of this paper.
Deep learning-based defect detection of metal parts: evaluating current methods in complex conditions

Anomaly detection (AD) plays a key role in automated quality analysis in industrial production. Recent AD methods have shown great potential for the detection of visual defects in several real-world applications. Most of the datatsets used in AD research (e.g. MVTec AD) are composed mainly of images from the laboratory environment with a monochromatic background. Each image contains only one object, which is centred, and its distance and spatial orientation to the camera do not change significantly. However, these conditions cannot be achieved in many realworld manufacturing processes and production lines. In order to test the performance of state-of-the-art (SOTA) AD methods under conditions of variable spatial orientation, position and distance of multiple objects concerning the camera at different light intensities and with a non-homogeneous background, it is necessary to create a new dataset. In this paper, we introduce a new dataset focused specifically on the issue of defect detection during painted metal parts fabrication. Next, we evaluate the performance of current SOTA AD methods on the proposed dataset. Our results show that some SOTA AD methods, which perform well on the standard industrial anomaly detection datatset – MVTec AD, show significantly different performance on our dataset. AUROC image-level difference is up to 23.12%. If we average the scores for all methods on each dataset, we observe the difference of 15.24%. Our experiment shows that for further development and improvement of AD methods, it is necessary to test these methods on datasets based on specific real-world applications.
High-Security Image Encryption Based on a Novel Simple Fractional-Order Memristive Chaotic System with a Single Unstable Equilibrium Point

Fractional-order chaotic systems have more complex dynamics than integer-order chaotic systems. Thus, investigating fractional chaotic systems for the creation of image cryptosystems has been popular recently. In this article, a fractional-order memristor has been developed, tested, numerically analyzed, electronically realized, and digitally implemented. Consequently, a novel simple three-dimensional (3D) fractional-order memristive chaotic system with a single unstable equilibrium point is proposed based on this memristor. This fractional-order memristor is connected in parallel with a parallel capacitor and inductor for constructing the novel fractional-order memristive chaotic system. The system’s nonlinear dynamic characteristics have been studied both analytically and numerically. To demonstrate the chaos behavior in this new system, various methods such as equilibrium points, phase portraits of chaotic attractor, bifurcation diagrams, and Lyapunov exponent are investigated. Furthermore, the proposed fractional-order memristive chaotic system was implemented using a microcontroller (Arduino Due) to demonstrate its digital applicability in real-world applications. Then, in the application field of these systems, based on the chaotic behavior of the memristive model, an encryption approach is applied for grayscale original image encryption. To increase the encryption algorithm pirate anti-attack robustness, every pixel value is included in the secret key. The state variable’s initial conditions, the parameters, and the fractional-order derivative values of the memristive chaotic system are used for contracting the keyspace of that applied cryptosystem. In order to prove the security strength of the employed encryption approach, the cryptanalysis metric tests are shown in detail through histogram analysis, keyspace analysis, key sensitivity, correlation coefficients, entropy analysis, time efficiency analysis, and comparisons with the same fieldwork. Finally, images with different sizes have been encrypted and decrypted, in order to verify the capability of the employed encryption approach for encrypting different sizes of images. The common cryptanalysis metrics values are obtained as keyspace = 2648, NPCR = 0.99866, UACI = 0.49963, H(s) = 7.9993, and time efficiency = 0.3 s. The obtained numerical simulation results and the security metrics investigations demonstrate the accuracy, high-level security, and time efficiency of the used cryptosystem which exhibits high robustness against different types of pirate attacks.
Syntactic data generation for handwritten mathematical expression recognition

This paper proposes tree-based decomposition and sub-expression interchange for generating new syntactically valid handwritten mathematical expressions (HMEs) from a given set of HMEs to train an HME recognition model and a mathematical language model (LM). The recognition model is dual trained using weakly supervised learning and encoder-decoder attention loss on the generated samples. Recognition experiments indicate that the proposed data generation method is superior to other such methods for offline HMEs. The HME recognition model increases the expression recognition rates by 1.47, 2.88, and 2.67 points on the Competition on Recognition of Online Handwritten Mathematical Expressions (CROHME) 2014, 2016, and 2019 testing sets, respectively. The LM also increases them by 8.92, 6.88, and 2.59 points on the testing sets. Further adding extra LaTeX sequences is cost effective in strengthening the LM for the expression recognition rates being 2.54, 2.8, and 1.25 points higher than without them on the CROHME testing sets, respectively. Among academic systems, the trained HME recognition system achieves the best performance with 64.60% and 66.08% expression recognition rates on the CROHME 2014 and 2016 testing sets and a comparable expression recognition rate of 58.72% on the CROHME 2019 testing set, respectively. Comparison with top systems from companies on CROHME 2019 suggests that more real and/or generated HME patterns will improve the performance of HME recognition models as well as mathematical language models.
A case study in the tropical region to evaluate univariate imputation methods for solar irradiance data with different weather types

A complete dataset of ground-measured Global Horizontal Solar Irradiance (GHI) is vital for the design and performance assessment of a photovoltaic (PV) system. Hence, imputing the missing values of the dataset can be a cost-effective solution for a site that already has ground-based measurement devices installed when satellite-derived data cannot offer the required accuracy for the applications. Nevertheless, using one imputation method for all weather types may lead to inaccurate predictions, which may underestimate the investment risk, especially in the tropical region, where the solar irradiance often fluctuates unpredictably. This article investigated 11 univariate imputation methods for Clear Sunny, Sunny, Intermittent, and Cloudy/Overcast weathers, with missing ratios ranging from 10% to 50%. The performances of these imputation methods were evaluated using standard error metrics. Three significance test indicators were further used to rank the imputation methods. Finally, the imputation methods were categorized into three groups. The results found that methods in Spline and Linear families work well for all weather types with Mean Bias Error of Daily Solar Irradiation of less than 0.2%, except Intermittent weather where SMA (k = 2 or 4) often performs the best. Meanwhile, Bezier is only suitable for conservative estimation to minimize the investment risk.
Space-Time Effect Prediction of Blasting Vibration Based on Intelligent Automatic Blasting Vibration Monitoring System

The vibration produced by blasting excavation in urban underground engineering has a significant influence on the surrounding environment, and the strength of vibration intensity involves many influencing factors. In order to predict the space-time effects of blasting vibration more accurately, an automatic intelligent monitoring system is constructed based on the rough set fuzzy neural network blasting vibration characteristic parameter prediction model and the network blasting vibrator (TC-6850). By setting up the regional monitoring network of monitoring points, the obtained monitoring data are analyzed. An artificial intelligence model is used to predict the influence of stratum condition, excavation hole, and high-rise building on blasting vibration velocity and frequency propagation. The results show that the artificial intelligence prediction model based on a rough set fuzzy neural network can accurately reflect the formation attenuation effect, hollow effect, and building amplification effect of blasting vibration by effectively fuzzing and standardizing the influencing factors. The propagation of blasting vibration in a soil–rock composite stratum is closely related to the surrounding rock conditions with a noticeable elastic modulus effect. The hollow effect is regional, which has a significant influence on the surrounding ground and buildings. Besides, the blasting vibration of the excavated area is stronger than that of the unexcavated area. The propagation of blasting vibration on high-rise buildings was complicated, of which the peak vibration velocity is maximum at the lower level of the building and decreased with the rise of the floor gradually. The whip sheath effect appears at the top floor, which is related to the blasting vibration frequency and the building’s natural vibration frequency.
DepthGrasp: Depth Completion of Transparent Objects Using Self-Attentive Adversarial Network with Spectral Residual for Grasping

Transparent objects with unique visual properties often make depth cameras fail to scan their reflective and refractive surfaces. Recent studies on depth completion of transparent objects have leveraged a linear system based on the geometric constraints to predict the missing depth, which is hard to be employed in an end-to-end framework and achieve joint optimization. In this paper, we propose DepthGrasp - a deep learning approach for depth completion of transparent objects from a raw RGB-D image. More specifically, we use a generative adversarial network, which utilizes the generator to complete the depth maps by predicting the missing or inaccurate depth values, and use discriminator to guide the completed depth maps against the groundtruth. In the generator, we devise spectral residual blocks (SRB) with spectral normalization for network stability, and residual block to pass the attention map in order to capture the structure information and distinguish the geometric shape of transparent objects. In the discriminator, we use a patch-based convolutional network to adapt the data distributions of the predicted depth maps according to groundtruth. Extensive experiments conducted on ClearGrasp dataset show the effectiveness and generalization of the DepthGrasp for depth completion, and the deployed robotic picking system makes significant improvement on the performance of grasping on transparent objects.
Can Noise on Qubits Be Learned in Quantum Neural Network? A Case Study on QuantumFlow (Invited Paper)

In the noisy intermediate-scale quantum (NISQ) era, one of the key questions is how to deal with the high noise level existing in physical quantum bits (qubits). Quantum error correction is promising but requires an extensive number (e.g., over 1,000) of physical qubits to create one ”perfect” qubit, exceeding the capacity of the existing quantum computers. This paper aims to tackle the noise issue from another angle: instead of creating perfect qubits for general quantum algorithms, we investigate the potential to mitigate the noise issue for dedicate algorithms. Specifically, this paper targets quantum neural network (QNN), and proposes to learn the errors in the training phase, so that the identified QNN model can be resilient to noise. As a result, the implementation of QNN needs no or a small number of additional physical qubits, which is more realistic for the near-term quantum computers. To achieve this goal, an application-specific compiler is essential: on the one hand, the error cannot be learned if the mapping from logical qubits to physical qubits exists randomness; on the other hand, the compiler needs to be efficient so that the lengthy training procedure can be completed in a reasonable time. In this paper, we utilize the recent QNN framework, QuantumFlow, as a case study. Experimental results show that the proposed approach can optimize QNN models for different errors in qubits, achieving up to 28% accuracy improvement compared with the model obtained by the error-agnostic training.
Data-adaptive binary neural networks for efficient object detection and recognition

Binary neural networks (BNNs) are promising for computational resource-limited devices, but the degradation of feature representation capacity stifles performance due to binarization. The reason is that existing methods fail to adapt to their input when approximating full-precision features. In this paper, we introduce the DA-BNN, a data-adaptive amplitude method based on spatial and channel attention. We generate an adaptive amplitude for a better feature approximation and minimize the gap between the real-valued and 1-bit convolution. Our adaptive amplitude introduces negligible storage but can significantly enhance the performance. Extensive experiments on object detection and recognition are conducted for the comprehensive evaluation of our methods. Our method achieves 64.0% on Pascal VOC with saving of the storage and computation by 18.62× and 15.77×, respectively. While on ImageNet, compared to the full-precision counterpart, 11.04× and 10.80× saving on storage and computation are obtained with just 3% drop on accuracy, demonstrating the effectiveness on both objective detection and recognition tasks.
High dynamic range fringe pattern acquisition based on deep neural network

Fringe projection profilometry has wide applications in three-dimensional measurement. When the fringes are projected onto the shiny surfaces with a large reflectivity, however, the saturation regions and dark regions are often generated on the surface due to the inconsistent reflection characteristics of the measured object. The saturation and dark regions make the modulation of captured fringe patterns degraded, and the accuracy of the three-dimensional measurement is significantly affected. To solve this problem, an improved network for high dynamic range fringe pattern is designed In this network, we introduce a low modulation region detection module based on U-net to obtain the saturation and dark regions in the fringe patterns. Then, the detailed fringe information in the low modulation regions can be predicted through a fringe enhancement module which takes advantage of the global distribution and detail intensity of the fringe patterns. In the experiment, a mass of simulated and actual low modulation fringe patterns are processed through the proposed network. The experimental results show that the designed network can effectively remove the noise of the captured fringe patterns and repair the low modulation regions captured from the shiny surfaces. Combined with phase calculation, the improved high dynamic range fringe patterns are used for three-dimensional data calculation. A standard metal gauge block with a height of 5 mm is measured and the root mean square error of the height is improved from 0.55 mm to 0.06 mm. This proves the proposed method can effectively improve the quality of fringe patterns projected on complex reflective surfaces.
Automatic selection of the number of clusters using Bayesian clustering and sparsity‐inducing priors

Clustering is a ubiquitous task in ecological and environmental sciences and multiple methods have been developed for this purpose. Because these clustering methods typically require users to a priori specify the number of groups, the standard approach is to run the algorithm for different numbers of groups and then choose the optimal number using a criterion (e.g., AIC or BIC). The problem with this approach is that it can be computationally expensive to run these clustering algorithms multiple times (i.e., for different numbers of groups) and some of these information criteria can lead to an overestimation of the number of groups. To address these concerns, we advocate for the use of sparsity-inducing priors within a Bayesian clustering framework. In particular, we highlight how the truncated stick-breaking (TSB) prior, a prior commonly adopted in Bayesian nonparametrics, can be used to simultaneously determine the number of groups and estimate model parameters for a wide range of Bayesian clustering models without requiring the fitting of multiple models. We illustrate the ability of this prior to successfully recover the true number of groups for three clustering models (two types of mixture models, applied to GPS movement data and species occurrence data, as well as the species archetype model) using simulated data in the context of movement ecology and community ecology. We then apply these models to armadillo movement data in Brazil, plant occurrence data from Alberta (Canada), and bird occurrence data from North America. We believe that many ecological and environmental sciences applications will benefit from Bayesian clustering methods with sparsity-inducing priors given the ubiquity of clustering and the associated challenge of determining the number of groups. Two R packages, EcoCluster and bayesmove, are provided that enable the straightforward fitting of these models with the TSB prior.