Reviewerly/exHarmony huggingface dataset:
Estimating half-hourly solar radiation over the Continental United States using GOES-16 data with iterative random forest

To reduce carbon emissions, using more solar energy is a feasible solution. Many meteorological-based models can estimate global downward solar radiation (DSR), but they are with limited applications due to the point-based estimation and low temporal resolution. Satellite remote sensing-based models can estimate DSR with better spatial coverage. However, most previous models are restricted to estimate clear-sky or monthly scale DSR at several sites, limiting the solar energy monitoring of nationwide scale. In this study, using high spatiotemporal resolution Geostationary Operational Environmental Satellites (GOES)-16 satellite data, an iterative random forest (RF) model was developed to estimate and map half-hourly DSR at 1-km spatial resolution over the Continental United States (CONUS). The results show that the iterative RF model performed better than multiple linear regression (MLR) and traditional RF models. The accuracy of estimating half-hourly DSR is that R2 = 0.95, root-mean-square-error (RMSE) = 66.92 W/m2, and mean-bias-error (MBE) = 0.06 W/m2. Half-hourly and daily DSR with spatial resolution 1-km over the CONUS were mapped. The GOES-16 estimated DSR showed the similar spatial patterns with the results from the Clouds and the Earth's Radiant Energy System (CERES) DSR product. This study demonstrated the potential of GOES-16 data for mapping DSR over the CONUS, and hence can be further used in solar energy related applications.
Extreme Low-Light Environment-Driven Image Denoising over Permanently Shadowed Lunar Regions with a Physical Noise Model

Recently, learning-based approaches have achieved impressive results in the field of low-light image denoising. Some state of the art approaches employ a rich physical model to generate realistic training data. However, the performance of these approaches ultimately depends on the realism of the physical model, and many works only concentrate on everyday photography. In this work we present a denoising approach for extremely low-light images of permanently shadowed regions (PSRs) on the lunar surface, taken by the Narrow Angle Camera on board the Lunar Reconnaissance Orbiter satellite. Our approach extends existing learning-based approaches by combining a physical noise model of the camera with real noise samples and training image scene selection based on 3D ray tracing to generate realistic training data. We also condition our denoising model on the camera’s environmental metadata at the time of image capture (such as the camera’s temperature and age), showing that this improves performance. Our quantitative and qualitative results show that our method strongly outperforms the existing calibration routine for the camera and other baselines. Our results could significantly impact lunar science and exploration, for example by aiding the identification of surface water-ice and reducing uncertainty in rover and human traverse planning into PSRs.
LRDNet: A lightweight and efficient network with refined dual attention decorder for real-time semantic segmentation

Most of the current popular semantic segmentation convolutional networks are focus on accuracy and require large amount of computation, which is using complex models. In order to realize real-time performance in practical applications, such as embedded systems and mobile devices, lightweight semantic segmentation has become a new need, where the network model should keep good accuracy in very limited computing budget. In this paper, we propose a lightweight network with the refined dual attention decorder (termed LRDNet) for better balance between computational speed and segmentation accuracy. In the encoding part of LRDNet, we offer an asymmetric module based on the residual network for lightweight and efficiency. In this module, a combination of decomposition convolution and deep convolution is used to improve the efficiency of feature extraction. In the decoding part of LRDNet, we use a refined dual attention mechanism to reduce the complexity of the entire network. Our network attained precise real-time segmentation results on Cityscapes, CamVid datasets. Without additional processing and pretraining, the LRDNet model achieves 70.1 Mean IoU in the Cityscapes test set. With a parameter value below 0.66 M, it can be up to 77 FPS.
Assessment and Modeling of Household-Scale Solar Water Heater Application in Zambia: Technical, Environmental, and Energy Analysis

Solar water heaters (SWHs) are one of the most effective plans for general and easy use of solar energy to supply hot water in domestic and industrial sectors. This paper gives the first-ever attempts to assess the optimal localization of SWHs across 22 major cities in Zambia, as well as determine the possibility of hot water generation and model the greenhouse gas (GHG) emission saving. The climate data used is extracted by using the MeteoSyn software which is modeled in TSOL™. Results show the high potential of GHG emission reduction due to nonconsumption of fossil fuels owing to the deployment of SWHs, and three cities Kabwe, Chipata, and Mbala had the highest GHG mitigation by 1552.97 kg/y, 1394.8 kg/y, and 1321.39 kg/y, respectively. On average, SWHs provide 62.47% of space heating and 96.05% of the sanitary hot water requirement of consumers. The findings have shown the potential for the deployment of SWHs in Zambia. The techno-enviro study in this paper can be used by the policymakers of Zambia and countries with similar climates.
A Hybrid LSTM-Based Genetic Programming Approach for Short-Term Prediction of Global Solar Radiation Using Weather Data

The integration of solar energy in smart grids and other utilities is continuously increasing due to its economic and environmental benefits. However, the uncertainty of available solar energy creates challenges regarding the stability of the generated power the supply-demand balance’s consistency. An accurate global solar radiation (GSR) prediction model can ensure overall system reliability and power generation scheduling. This article describes a nonlinear hybrid model based on Long Short-Term Memory (LSTM) models and the Genetic Programming technique for short-term prediction of global solar radiation. The LSTMs are Recurrent Neural Network (RNN) models that are successfully used to predict time-series data. We use these models as base predictors of GSR using weather and solar radiation (SR) data. Genetic programming (GP) is an evolutionary heuristic computing technique that enables automatic search for complex solution formulas. We use the GP in a post-processing stage to combine the LSTM models’ outputs to find the best prediction of the GSR. We have examined two versions of the GP in the proposed model: a standard version and a boosted version that incorporates a local search technique. We have shown an improvement in terms of performance provided by the proposed hybrid model. We have compared its performance to stacking techniques based on machine learning for combination. The results show that the suggested method provides significant improvement in terms of performance and consistency.
Revisiting Knowledge Distillation: An Inheritance and Exploration Framework

Knowledge Distillation (KD) is a popular technique to transfer knowledge from a teacher model or ensemble to a student model. Its success is generally attributed to the privileged information on similarities/consistency between the class distributions or intermediate feature representations of the teacher model and the student model. However, directly pushing the student model to mimic the probabilities/features of the teacher model to a large extent limits the student model in learning undiscovered knowledge/features. In this paper, we propose a novel inheritance and exploration knowledge distillation framework (IE-KD), in which a student model is split into two parts - inheritance and exploration. The inheritance part is learned with a similarity loss to transfer the existing learned knowledge from the teacher model to the student model, while the exploration part is encouraged to learn representations different from the inherited ones with a dis-similarity loss. Our IE-KD framework is generic and can be easily combined with existing distillation or mutual learning methods for training deep neural networks. Extensive experiments demonstrate that these two parts can jointly push the student model to learn more diversified and effective representations, and our IE-KD can be a general technique to improve the student network to achieve SOTA performance. Furthermore, by applying our IE-KD to the training of two networks, the performance of both can be improved w.r.t. deep mutual learning.
Solution of physics-based Bayesian inverse problems with deep generative priors

Inverse problems are ubiquitous in nature, arising in almost all areas of science and engineering ranging from geophysics and climate science to astrophysics and biomechanics. One of the central challenges in solving inverse problems is tackling their ill-posed nature. Bayesian inference provides a principled approach for overcoming this by formulating the inverse problem into a statistical framework. However, it is challenging to apply when inferring fields that have discrete representations of large dimensions (the so-called "curse of dimensionality") and/or when prior information is available only in the form of previously acquired solutions. In this work, we present a novel method for efficient and accurate Bayesian inversion using deep generative models. Specifically, we demonstrate how using the approximate distribution learned by a Generative Adversarial Network (GAN) as a prior in a Bayesian update and reformulating the resulting inference problem in the low-dimensional latent space of the GAN, enables the efficient solution of large-scale Bayesian inverse problems. Our statistical framework preserves the underlying physics and is demonstrated to yield accurate results with reliable uncertainty estimates, even in the absence of information about underlying noise model, which is a significant challenge with many existing methods. We demonstrate the effectiveness of proposed method on a variety of inverse problems which include both synthetic as well as experimentally observed data.
What is your Distributed (Hyper)Ledger?

Since the launch of Bitcoin in 2008, blockchain technology has enjoyed increasing popularity, and many implementations have followed. Some of these efforts go beyond cryptocurrencies as they aim at organizational and business innovation in supply chains, healthcare, agri-food, large-scale distribution, and public administration. Consequently, there is a great demand for projects that use blockchains for solutions that meet specific business objectives, and the problem has arisen of finding the most appropriate blockchain technology for the project needs. In a previous study, Garriga et al. proposed a methodology that exposes the interplay between technological decisions (such as consensus protocols and support for smart contracts) and architectural features (such as cost and decentralization) for the most popular public blockchains to support blockchain stakeholders to identify an adequate blockchain implementation for their business needs. This work aims at making a step further up by extending the framework to private and consortium blockchains with a focus on HyperLedger, the leading software ecosystem supporting the collaborative development of customized distributed ledgers. We showcase our extended methodology through four real-world case studies based on the most relevant Hyperledger distributed ledgers.
Automatic recognition system of pointer meters based on lightweight CNN and WSNs with on-sensor image processing

The pointer meter is widely used in the modern industrial process. This paper proposes a novel pointer meter recognition method based on wireless sensor networks (WSNs) and a lightweight convolutional neural network (CNN), which completes image preprocessing, CNN classification, and reading calculation on the WSN end node, and then only transmits the recognized result in the WSN to reduce its payload transmission data. Meanwhile, a lightweight CNN classifier model with a simple structure and small size is designed for embedding in the resource-constrained WSN node. A set of experiments have been carried out on the fabricated prototype to verify the feasibility and adaptability of the proposed method. Experimental results have shown that the maximum error of the recognized results for real-world applications is around 0.27%, while the payload transmission data of the WSN decrease from 112.5 kB to 5 bytes.
Combining 3D Image and Tabular Data via the Dynamic Affine Feature Map Transform

Prior work on diagnosing Alzheimer's disease from magnetic resonance images of the brain established that convolutional neural networks (CNNs) can leverage the high-dimensional image information for classifying patients. However, little research focused on how these models can utilize the usually low-dimensional tabular information, such as patient demographics or laboratory measurements. We introduce the Dynamic Affine Feature Map Transform (DAFT), a general-purpose module for CNNs that dynamically rescales and shifts the feature maps of a convolutional layer, conditional on a patient's tabular clinical information. We show that DAFT is highly effective in combining 3D image and tabular information for diagnosis and time-to-dementia prediction, where it outperforms competing CNNs with a mean balanced accuracy of 0.622 and mean c-index of 0.748, respectively. Our extensive ablation study provides valuable insights into the architectural properties of DAFT. Our implementation is available at https://github.com/ai-med/DAFT.
ComFormer: Code Comment Generation via Transformer and Fusion Method-based Hybrid Code Representation

Developers often write low-quality code comments due to the lack of programming experience, which can reduce the efficiency of developers' program comprehension. Therefore, developers hope that code comment generation tools can be developed to illustrate the functionality and purpose of the code. Recently, researchers mainly model this problem as the neural machine translation problem and tend to use deep learning-based methods. In this study, we propose a novel method ComFormer based on Transformer and fusion method-based hybrid code presentation. Moreover, to alleviate OOV (out-of-vocabulary) problem and speed up model training, we further utilize the Byte-BPE algorithm to split identifiers and Sim_SBT method to perform AST Traversal. We compare ComFormer with seven state-of-the-art baselines from code comment generation and neural machine translation domains. Comparison results show the competitiveness of ComFormer in terms of three performance measures. Moreover, we perform a human study to verify that ComFormer can generate high-quality comments.
QoE Driven VR 360° Video Massive MIMO Transmission

Massive multiple-input and multiple-output (MIMO) enables ultra-high throughput and low latency for tile-based adaptive virtual reality (VR) 360° video transmission in wireless network. In this paper, we consider a massive MIMO system where multiple users in a single-cell theater watch an identical VR 360° video. Based on tile prediction, base station (BS) deliveries the tiles in predicted field of view (FoV) to users. By introducing practical supplementary transmission for missing tiles and unacceptable VR sickness, we propose the first stable transmission scheme for VR video. we formulate an integer non-linear programming (INLP) problem to maximize users’ average quality of experience (QoE) score. Moreover, we derive the achievable spectral efficiency (SE) expression of predictive tile groups and the approximately achievable SE expression of missing tile groups, respectively. Analytically, the overall throughput is related to the number of tile groups and the length of pilot sequences. By exploiting the relationship between the structure of viewport tiles and SE expression, we propose a multi-lattice multi-stream grouping method aimed at improving the overall throughput for VR video transmission. Moreover, we analyze the relationship between QoE objective and number of predictive tile. We transform the original INLP problem into an integer linear programming problem by setting the predictive tiles groups as some constants. With variable relaxation and recovery, we obtain the optimal average QoE. Extensive simulation results validate that the proposed algorithm effectively improves QoE.
The Blockchain Solution for the Security of Internet of Energy and Electric Vehicle Interface

In this paper, an Internet of Energy (IoE) solution for providing optimal charging and control of resources in the era of electric vehicles (EVs) with energy trading and security is considered. A blockchain based security solution is presented by considering the IoE as a distributed energy system with internet of things (IoT). However, by inheriting the security concerns of IoTs, IoE also faces the security challenges and is considered here. Hierarchical key generation is used for cryptographic keys, based on lattice constructions. Along with a consensus tailored with Practical Byzantine Fault Tolerance (PBFT), an additional smart contract is also applied for utilization of energy transfer. The presented solution provides transparency, reliability, availability and failure recovery inheriting from the blockchain aspects. To the best of our knowledge, the present study is the first attempt in this direction of IoE-EV interface solutions. The experimental results, comparative analysis and security validation affirm the superiority of the proposed solution over the existing approaches.
Mask <scp>RCNN</scp> algorithm for nuclei detection on breast cancer histopathological images

Abstract Nuclei detection is a key step in computer assisted pathology. Due to the variability of the size, shape, appearance, and texture of breast cancer nuclei in histopathological images, automated nuclei detection has always been a difficult aspect of computer‐aided pathology research. In this article, Mask RCNN is presented for the automatic detection of nuclei on high‐resolution histopathological images of breast cancer. Mask RCNN uses the ResNet network and effectively combines modules such as feature pyramid networks (FPN), ROIAlign, and fully convolutional networks (FCN). FPN can efficiently extract features of various dimensions in images. ROIAlign can improve the accuracy of the detection model in the detection task. FCN renders the prediction results more detailed. The experiment results show that the application of this algorithm is superior to other algorithms in terms of its intuitive vision, as well as in performance indicators such as accuracy, recall, and F‐Measure.
Modelling Quantum Circuits with UML

None of the quantum computing applications imagined will ever become a reality without quantum software. Quantum programmes have, to date, been coded with ad hoc techniques. Researchers in the field of quantum software engineering are, therefore, now demanding more systematic techniques and methods with which to produce software with sufficient quality. One of the challenges and lessons learned from classic software engineering is the need for high-level, abstract and technology-independent representations with which to design software before it is coded. This paper specifically addresses this challenge for quantum software design. Since UML is a well-proven modelling language that has been widely employed by industry for some time, we propose a UML extension for the representation of quantum algorithms. Our proposal comprises the definition of a UML profile based on various stereotypes that can be applied to the existing UML activity diagrams in order to represent quantum circuits. The advantage of this representation is that UML quantum circuits can be interrelated with other UML elements and diagrams, which will make it possible to represent various concerns and viewpoints of the so-called hybrid information systems. This will consequently enable classical and quantum aspects to be modelled together in integrated designs in a technological-agnostic manner that is already supported by a considerable number of existing software design tools.
Revealing the landscape of privacy-enhancing technologies in the context of data markets for the IoT: A systematic literature review

IoT data markets in public and private institutions have become increasingly relevant in recent years because of their potential to improve data availability and unlock new business models. However, exchanging data in markets bears considerable challenges related to disclosing sensitive information. Despite considerable research focused on different aspects of privacy-enhancing data markets for the IoT, none of the solutions proposed so far seems to find a practical adoption. Thus, this study aims to organize the state-of-the-art solutions, analyze and scope the technologies that have been suggested in this context, and structure the remaining challenges to determine areas where future research is required. To accomplish this goal, we conducted a systematic literature review on privacy enhancement in data markets for the IoT, covering 50 publications dated up to July 2020, and provided updates with 24 publications dated up to May 2022. Our results indicate that most research in this area has emerged only recently, and no IoT data market architecture has established itself as canonical. Existing solutions frequently lack the required combination of anonymization and secure computation technologies. Furthermore, there is no consensus on the appropriate use of blockchain technology for IoT data markets and a low degree of leveraging existing libraries or reusing generic data market architectures. We also identified significant challenges remaining, such as the copy problem and the recursive enforcement problem that – while solutions have been suggested to some extent – are often not sufficiently addressed in proposed designs. We conclude that privacy-enhancing technologies need further improvements to positively impact data markets so that, ultimately, the value of data is preserved through data scarcity and users' privacy and businesses-critical information are protected.
Chronic Disease Progression Prediction: Leveraging Case‐Based Reasoning and Big Data Analytics

Physicians caring for chronically ill individuals need to predict patients' disease progression, as accurate disease projections can facilitate better treatment decisions. The power of prediction is prevention, as it is easier to prevent than to reverse. In this research, we propose a data‐driven model for accurate and fast disease trajectory prediction, using electronic health records (EHRs) from Veterans Affairs Hospitals. EHRs contain tremendous amount of frequently updated, highly dimensional and not equally spaced data in diverse formats (e.g., numeric, textual, images, and videos). We propose an intelligent case‐based reasoning (iCBR) approach to better predict kidney disease progression, which can help prevent patients' health deterioration and prolong lives. Our iCBR contributes to the literature by enhancing the automation and personalization capabilities of the conventional case‐based reasoning (CBR). Through the iCBR, we advance the utilization of patient's laboratory data, vital sign, clinic visit, and comorbidity information. We examine (1) if the number of cases chosen for predicting the new patient's disease progression should be tailored, and (2) what the best number of prediction cases should be if customization is warranted. We link the number of cases selected for disease prediction with patient's disease characteristics. By comparing the results of the iCBR and popular machine learning and statistics models adapted to our problem, we find that the iCBR outperforms other methods. While the proposed model is applied to patients with chronic kidney disease, it can be readily applied to other chronic diseases such as diabetes, due to its similar data structure.