Reviewerly/exHarmony huggingface dataset:
A literature review of blockchain technology applications in supply chains: A comprehensive analysis of themes, methodologies and industries

In the past few years, blockchain, the underlying technology of Bitcoin, has received considerable attention from academia and industry. It is widely accepted that blockchain technology causes disruptive changes in supply chain operations that can overcome supply chain difficulties encountered in realizing information sharing, maintaining traceability in the entire process and improving operational efficiency. However, the application of blockchain technology in the supply chain field is still in its infancy, which limits an understanding of its potential. This article uses descriptive and content analysis to review publications related to blockchain-based supply chains between 2017 and 2020 inclusive. To fully explore research on blockchain-based supply chains, four well-designed questions are proposed and addressed, namely, the value of blockchain in supply chains, the attraction of scholars to particular supply chain themes, the development of research methodologies and illustration types in adopting blockchain in supply chains, and the types of industries involved in blockchain-based supply chains. The results reveal that there is growing interest in applying blockchain technology to supply chain operations. A detailed analysis of findings is provided to identify the future opportunities of blockchain-based supply chains, including prospects for tertiary industries and concerted efforts that are necessary to explore sustainability themes. This article provides valuable information to help scholars and practitioners better determine the relevant research topics to accelerate the development of blockchain-based supply chains.
A performance comparison of supervised machine learning models for Covid-19 tweets sentiment analysis

The spread of Covid-19 has resulted in worldwide health concerns. Social media is increasingly used to share news and opinions about it. A realistic assessment of the situation is necessary to utilize resources optimally and appropriately. In this research, we perform Covid-19 tweets sentiment analysis using a supervised machine learning approach. Identification of Covid-19 sentiments from tweets would allow informed decisions for better handling the current pandemic situation. The used dataset is extracted from Twitter using IDs as provided by the IEEE data port. Tweets are extracted by an in-house built crawler that uses the Tweepy library. The dataset is cleaned using the preprocessing techniques and sentiments are extracted using the TextBlob library. The contribution of this work is the performance evaluation of various machine learning classifiers using our proposed feature set. This set is formed by concatenating the bag-of-words and the term frequency-inverse document frequency. Tweets are classified as positive, neutral, or negative. Performance of classifiers is evaluated on the accuracy, precision, recall, and F1 score. For completeness, further investigation is made on the dataset using the Long Short-Term Memory (LSTM) architecture of the deep learning model. The results show that Extra Trees Classifiers outperform all other models by achieving a 0.93 accuracy score using our proposed concatenated features set. The LSTM achieves low accuracy as compared to machine learning classifiers. To demonstrate the effectiveness of our proposed feature set, the results are compared with the Vader sentiment analysis technique based on the GloVe feature extraction approach.
Deep Learning for Geophysics: Current and Future Trends

Abstract Recently deep learning (DL), as a new data‐driven technique compared to conventional approaches, has attracted increasing attention in geophysical community, resulting in many opportunities and challenges. DL was proven to have the potential to predict complex system states accurately and relieve the “curse of dimensionality” in large temporal and spatial geophysical applications. We address the basic concepts, state‐of‐the‐art literature, and future trends by reviewing DL approaches in various geosciences scenarios. Exploration geophysics, earthquakes, and remote sensing are the main focuses. More applications, including Earth structure, water resources, atmospheric science, and space science, are also reviewed. Additionally, the difficulties of applying DL in the geophysical community are discussed. The trends of DL in geophysics in recent years are analyzed. Several promising directions are provided for future research involving DL in geophysics, such as unsupervised learning, transfer learning, multimodal DL, federated learning, uncertainty estimation, and active learning. A coding tutorial and a summary of tips for rapidly exploring DL are presented for beginners and interested readers of geophysics.
Integrated digital twin and blockchain framework to support accountable information sharing in construction projects

Efficient collaboration among various stakeholders is important for the successful completion of a construction project. However, stakeholders in construction are fragmented, which in turn hinders accountable information sharing. To address this issue, the authors aim to develop and test an integrated digital twin and blockchain framework for traceable data communication. The digital twin updates building information modeling in near real-time using internet of things sensors, while the blockchain authenticates and adds confidence to all data transactions to the digital twin. The authors tested the framework with a case project where virtual positioning data from a prefabricated brick is transmitted to digital twin in near real-time, recorded on the blockchain with time stamps. The results show that the integrated digital twin and blockchain framework makes all data transactions traceable. This paper's primary contribution is the development of a framework that realizes accountable project-related information sharing across stakeholders.
Ternary radial harmonic Fourier moments based robust stereo image zero-watermarking algorithm

With the development and popularization of computer network technology, the copyright protection of stereo images is a serious problem to be solved. Based on ternary number theory and radial harmonic Fourier moments (RHFM), ternary radial harmonic Fourier moments (TRHFM) is proposed to deal with stereo images in a holistic manner, and based on this moment, this paper proposes a robust stereo image zero-watermarking algorithm. We first compute the TRHFM of the original stereo image, and we randomly select TRHFMs using logistic mapping; then, we obtain a binary feature image using the magnitudes of the selected TRHFMs, and finally, we apply a bitwise exclusive-or operation on permuted logo image and binary feature image to obtain the zero-watermark image. Experimental results indicate that the proposed stereo image zero-watermarking algorithm is strongly robust to various asymmetric and symmetric attacks and has superiority compared with other zero-watermarking algorithms.
A fast image encryption algorithm based on compressive sensing and hyperchaotic map

In this paper, we propose a new two-dimensional Sine improved Logistic iterative chaotic map with infinite collapse (ICMIC) modulation map (2D-SLIM) based on the improved two-dimensional closed-loop modulation coupling model. The performance analysis results show that it has a large parameter space, large Lyapunov exponents and high complexity. Combining this map with compressive sensing, a fast image encryption algorithm is proposed. In this algorithm, a plain image is measured by two circular measurement matrices from two directions, and then the image is re-encrypted by employing the row and column encryption to simultaneously achieve permutation and diffusion. In addition, the SHA-512 hash values of the plain image are generated to calculate the initial conditions of the chaotic map with secret keys, which greatly improve the ability to withstand the known plaintext and chosen plaintext attacks. Simulation and performance analysis verify that this algorithm has acceptable compression, high security and low time complexity.
Real-Time Self-Adaptive Deep Stereo

Deep convolutional neural networks trained end-to-end are the state-of-the-art methods to regress dense disparity maps from stereo pairs. These models, however, suffer from a notable decrease in accuracy when exposed to scenarios significantly different from the training set (e.g., real vs synthetic images, etc.). We argue that it is extremely unlikely to gather enough samples to achieve effective training/tuning in any target domain, thus making this setup impractical for many applications. Instead, we propose to perform unsupervised and continuous online adaptation of a deep stereo network, which allows for preserving its accuracy in any environment. However, this strategy is extremely computationally demanding and thus prevents real-time inference. We address this issue introducing a new lightweight, yet effective, deep stereo architecture, Modularly ADaptive Network(MADNet), and developing a Modular ADaptation (MAD) algorithm, which independently trains sub-portions of the network. By deploying MADNet together with MAD we introduce the first real-time self-adaptive deep stereo system enabling competitive performance on heterogeneous datasets. Our code is publicly available at https://github.com/CVLAB-Unibo/Real-time-self-adaptive-deep-stereo.
STanford EArthquake Dataset (STEAD): A Global Data Set of Seismic Signals for AI

Seismology is a data rich and data-driven science. Application of machine learning for gaining new insights from seismic data is a rapidly evolving sub-field of seismology. The availability of a large amount of seismic data and computational resources, together with the development of advanced techniques can foster more robust models and algorithms to process and analyze seismic signals. Known examples or labeled data sets, are the essential requisite for building supervised models. Seismology has labeled data, but the reliability of those labels is highly variable, and the lack of high-quality labeled data sets to serve as ground truth as well as the lack of standard benchmarks are obstacles to more rapid progress. In this paper we present a high-quality, large-scale, and global data set of local earthquake and non-earthquake signals recorded by seismic instruments. The data set in its current state contains two categories: (1) local earthquake waveforms (recorded at “local” distances within 350 km of earthquakes) and (2) seismic noise waveforms that are free of earthquake signals. Together these data comprise ~1.2 million time series or more than 19,000 hours of seismic signal recordings. Constructing such a large-scale database with reliable labels is a challenging task. Here, we present the properties of the data set, describe the data collection, quality control procedures, and processing steps we undertook to insure accurate labeling, and discuss potential applications. We hope that the scale and accuracy of STEAD presents new and unparalleled opportunities to researchers in the seismological community and beyond.
Time series forecasting of solar power generation for large-scale photovoltaic plants

Accurate solar power forecasting is essential for grid-connected photovoltaic (PV) systems especially in case of fluctuating environmental conditions. The prediction of PV power output is critical to secure grid operation, scheduling and grid energy management. One of the key elements in PV output prediction is time series analysis especially in locations where the historical solar radiation measurements or other weather parameters have not been recorded. In this work, several time series prediction methods including the statistical methods and those based on artificial intelligence are introduced and compared rigorously for PV power output prediction. Moreover, the effect of prediction time horizon variation for all the algorithms is investigated. Hourly solar power forecasting is carried out to verify the effectiveness of different models. The data utilized in the current work comprises 3640 h of operation data taken from a 20 MW grid-connected PV station in China.
Overview of the HASOC track at FIRE 2019

The identification of Hate Speech in Social Media is of great importance and receives much attention in the text classification community. There is a huge demand for research for languages other than English. The HASOC track intends to stimulate development in Hate Speech for Hindi, German and English. Three datasets were developed from Twitter and Facebook and made available. Binary classification and more fine-grained subclasses were offered in 3 subtasks. For all subtasks, 321 experiments were submitted. The approaches used most often were LSTM networks processing word embedding input. The performance of the best system for identification of Hate Speech for English, Hindi, and German was a Marco-F1 score of 0.78, 0.81 and 0.61, respectively.
Temporal convolutional neural (TCN) network for an effective weather forecasting using time-series data from the local weather station

Abstract Non-predictive or inaccurate weather forecasting can severely impact the community of users such as farmers. Numerical weather prediction models run in major weather forecasting centers with several supercomputers to solve simultaneous complex nonlinear mathematical equations. Such models provide the medium-range weather forecasts, i.e., every 6 h up to 18 h with grid length of 10–20 km. However, farmers often depend on more detailed short-to medium-range forecasts with higher-resolution regional forecasting models. Therefore, this research aims to address this by developing and evaluating a lightweight and novel weather forecasting system, which consists of one or more local weather stations and state-of-the-art machine learning techniques for weather forecasting using time-series data from these weather stations. To this end, the system explores the state-of-the-art temporal convolutional network (TCN) and long short-term memory (LSTM) networks. Our experimental results show that the proposed model using TCN produces better forecasting compared to the LSTM and other classic machine learning approaches. The proposed model can be used as an efficient localized weather forecasting tool for the community of users, and it could be run on a stand-alone personal computer.
Prediction of daily global solar radiation using different machine learning algorithms: Evaluation and comparison

The prediction of global solar radiation for the regions is of great importance in terms of giving directions of solar energy conversion systems (design, modeling, and operation), selection of proper regions, and even future investment policies of the decision-makers. With this viewpoint, the objective of this paper is to predict daily global solar radiation data of four provinces (Kırklareli, Tokat, Nevşehir and Karaman) which have different solar radiation distribution in Turkey. In the study, four different machine learning algorithms (support vector machine (SVM), artificial neural network (ANN), kernel and nearest-neighbor (k-NN), and deep learning (DL)) are used. In the training of these algorithms, daily minimum and maximum ambient temperature, cloud cover, daily extraterrestrial solar radiation, day length and solar radiation of these provinces are used. The data is supplied from the Turkish State Meteorological Service and cover the last two years (01.01.2018–31.12.2019). To decide on the success of these algorithms, seven different statistical metrics (R2, RMSE, rRMSE, MBE, MABE, t-stat, and MAPE) are discussed in the study. The results shows that R2, MABE, and RMSE values of all algorithms are ranging from 0.855 to 0.936, from 1.870 to 2.328 MJ/m2, from 2.273 to 2.820 MJ/m2, respectively. At all cases, k-NN exhibited the worst result in terms of R2, RMSE, and MABE metrics. Of all the models, DL was the only model that exceeded the t-critic value. In conclusion, the present paper is reporting that all machine learning algorithms tested in this study can be used in the prediction of daily global solar radiation data with a high accuracy; however, the ANN algorithm is the best fitting algorithm among all algorithms. Then it is followed by DL, SVM and k-NN, respectively.
Deep and machine learning techniques for medical imaging-based breast cancer: A comprehensive review

Breast cancer is the second leading cause of death for women, so accurate early detection can help decrease breast cancer mortality rates. Computer-aided detection allows radiologists to detect abnormalities efficiently. Medical images are sources of information relevant to the detection and diagnosis of various diseases and abnormalities. Several modalities allow radiologists to study the internal structure, and these modalities have been met with great interest in several types of research. In some medical fields, each of these modalities is of considerable significance. This study aims at presenting a review that shows the new applications of machine learning and deep learning technology for detecting and classifying breast cancer and provides an overview of progress in this area. This review reflects on the classification of breast cancer utilizing multi-modalities medical imaging. Details are also given on techniques developed to facilitate the classification of tumors, non-tumors, and dense masses in various medical imaging modalities. It first provides an overview of the different approaches to machine learning, then an overview of the different deep learning techniques and specific architectures for the detection and classification of breast cancer. We also provide a brief overview of the different image modalities to give a complete overview of the area. In the same context, this review was performed using a broad variety of research databases as a source of information for access to various field publications. Finally, this review summarizes the future trends and challenges in the classification and detection of breast cancer.
End-to-end capacities of a quantum communication network

Abstract In quantum mechanics, a fundamental law prevents quantum communications to simultaneously achieve high rates and long distances. This limitation is well known for point-to-point protocols, where two parties are directly connected by a quantum channel, but not yet fully understood in protocols with quantum repeaters. Here we solve this problem bounding the ultimate rates for transmitting quantum information, entanglement and secret keys via quantum repeaters. We derive single-letter upper bounds for the end-to-end capacities achievable by the most general (adaptive) protocols of quantum and private communication, from a single repeater chain to an arbitrarily complex quantum network, where systems may be routed through single or multiple paths. We analytically establish these capacities under fundamental noise models, including bosonic loss which is the most important for optical communications. In this way, our results provide the ultimate benchmarks for testing the optimal performance of repeater-assisted quantum communications.
Connecting Ansatz Expressibility to Gradient Magnitudes and Barren Plateaus

Parametrized quantum circuits serve as ansatze for solving variational problems and provide a flexible paradigm for the programming of near-term quantum computers. Ideally, such ansatze should be highly expressive, so that a close approximation of the desired solution can be accessed. On the other hand, the ansatz must also have sufficiently large gradients to allow for training. Here, we derive a fundamental relationship between these two essential properties: expressibility and trainability. This is done by extending the well-established barren plateau phenomenon, which holds for ansatze that form exact 2-designs, to arbitrary ansatze. Specifically, we calculate the variance in the cost gradient in terms of the expressibility of the ansatz, as measured by its distance from being a 2-design. Our resulting bounds indicate that highly expressive ansatze exhibit flatter cost landscapes and therefore will be harder to train. Furthermore, we provide numerics illustrating the effect of expressibility on gradient scalings and we discuss the implications for designing strategies to avoid barren plateaus.1 MoreReceived 12 February 2021Accepted 30 November 2021DOI:https://doi.org/10.1103/PRXQuantum.3.010313Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasMachine learningQuantum algorithmsQuantum computationQuantum information processingTechniquesComputational complexityQuantum InformationGeneral Physics
A stochastic configuration network based on chaotic sparrow search algorithm

Stochastic configuration network (SCN), as a novel incremental generation model with supervisory mechanism, has an excellent superiority in solving large-scale data regression and classification problems. However, the accuracy of the SCN is affected by the assignation and selection of some network parameters significantly Sparrow search algorithm (SSA) is a new meta-heuristic algorithm that simulates the foraging and anti-predation behavior of sparrow population. In this paper, a stochastic configuration network based on chaotic sparrow search algorithm is first introduced, termed as CSSA-SCN. Firstly, chaotic sparrow search algorithm (CSSA) is designed which mainly utilizes logistic mapping, self-adaptive hyper-parameters, mutation operator to enhance the global optimization capability of SSA; Secondly, as the performance of SCN is related to regularization parameter r and scale factor λ of weights and biases, then CSSA is employed to give better parameters for SCN automatically; Finally, 13 benchmark functions and several datasets are used to evaluate the performance of CSSA and CSSA-SCN respectively. Experimental results demonstrate the feasibility and validity of CSSA-SCN compared with SCN and other contrast algorithms.
Designing a Practical Degradation Model for Deep Blind Image Super-Resolution

It is widely acknowledged that single image super-resolution (SISR) methods would not perform well if the assumed degradation model deviates from those in real images. Although several degradation models take additional factors into consideration, such as blur, they are still not effective enough to cover the diverse degradations of real images. To address this issue, this paper proposes to design a more complex but practical degradation model that consists of randomly shuffled blur, downsampling and noise degradations. Specifically, the blur is approximated by two convolutions with isotropic and anisotropic Gaussian kernels; the downsampling is randomly chosen from nearest, bilinear and bicubic interpolations; the noise is synthesized by adding Gaussian noise with different noise levels, adopting JPEG compression with different quality factors, and generating processed camera sensor noise via reverse-forward camera image signal processing (ISP) pipeline model and RAW image noise model. To verify the effectiveness of the new degradation model, we have trained a deep blind ES-RGAN super-resolver and then applied it to super-resolve both synthetic and real images with diverse degradations. The experimental results demonstrate that the new degradation model can help to significantly improve the practicability of deep super-resolvers, thus providing a powerful alternative solution for real SISR applications.