Reviewerly/exHarmony huggingface dataset:
A new geographical information system approach based on best worst method and analytic hierarchy process for site suitability and technical potential evaluation for large-scale CSP on-grid plant: An application for Algeria territory

For future energy planning and sustainable development, the share of renewable energy in the national power production must be increased. For this reason, energy managers and deciders would impose models and strategies for future renewable energy planning development. The site selection of new renewable energy projects constitutes a huge challenge, due to the contributions of several factors in the decision-making process. This study has two main objectives, the first is to develop an alternative framework for site selection of large-scale parabolic trough concentrating solar power on-grid plants as an essential future solar power in Algeria. A Geographic Information System (GIS) based analysis combined with Multi Criteria Decision Making techniques to develop a high-resolution map that identify and classify the suitable locations for setting up these projects. Several criteria are used in GIS analysis process, a map of direct normal solar irradiation and sunshine duration were developed with high resolution of (92×92 m/pixel) and grid power map was presented in its updatable form. The prioritization degree of the available siting zones is presented through three scenarios where distinct opinions of different energy related groups are considered by using different weighting methods; EQual Weighted (EQW), Analytic Hierarchy Process(AHP) and Best Worst Method (BWM). Second, the paper assesses the theoretical and technical potential of energy generation using concentrating solar power plants. A sensitivity analysis is applied for the suitable lands and finally, the results show that approximately 11% of the study area is considered as available lands for concentrating solar power energy generation with yearly electricity generation 34,453 TWh. The provinces of Bechar, Naama, Elbayadh, Laghouat, Gherdaïa, and Ouargla presenting the large lands of most suitable class (approximately 15,384.73 km2 and 15,986.34 km2 in AHP and BWM scenario respectively. Where the energy generation can reach more than 2,100 TWh/year, which can cover 38 time the national electricity demand.
TMO-Net: A Parameter-Free Tone Mapping Operator Using Generative Adversarial Network, and Performance Benchmarking on Large Scale HDR Dataset

Currently published tone mapping operators (TMO) are often evaluated on a very limited test set of high dynamic range (HDR) images. Thus, the resulting performance index is highly subject to extensive hyperparameter tuning, and many TMOs exhibit sub-optimal performance when tested on a broader spectrum of HDR images. This indicates that there are deficiencies in the generalizable applicability of these techniques. Finally, it is a challenge developing parameter-free tone mapping operators using data-hungry advanced deep learning methods due to the paucity of large scale HDR datasets. In this paper, these issues are addressed through the following contributions: a) a large scale HDR image benchmark dataset (LVZ-HDR dataset) with multiple variations in sceneries and lighting conditions is created to enable performance evaluation of TMOs across a diverse conditions and scenes that will also contribute to facilitate the development of more robust TMOs using state-of-the-art deep learning methods; b) a deep learning-based tone mapping operator (TMO-Net) is presented, which offers an efficient and parameter-free method capable of generalizing effectively across a wider spectrum of HDR content; c) finally, a comparative analysis, and performance benchmarking of 19 state-of-the-art TMOs on the new LVZ-HDR dataset are presented. Standard metrics including the Tone Mapping Quality Index (TMQI), Feature Similarity Index for Tone Mapped images (FSITM), and Natural Image Quality Evaluator (NIQE) are used to qualitatively evaluate the performance index of the benchmarked TMOs. Experimental results demonstrate that the proposed TMO-Net qualitatively and quantitatively outperforms current state-of-the-art TMOs.
Senior Consumer Motivations and Perceived Value of Robot Service Restaurants in Korea

This research empirically tested a theoretical model by defining senior customers’ intentions to use robot service restaurants emerging in South Korea. Non-face-to-face services have become increasingly important for seniors. Therefore, restaurant marketers should cater to senior customers’ needs by sustaining robot service restaurants. The study analyzed 243 questionnaires to verify the reliability and validity of the measurement items. The research hypotheses were examined using structural equation modeling (SEM). The suggested model comprised three stages: motivated consumer innovativeness (MCI), perceived value, and planned behavior (attitude, intention to use). The results revealed that senior customers’ perceived values positively influenced attitude and were enhanced by hedonically MCI (hMCI) and socially MCI. Moreover, the hedonic and social elements of motivation improved the attitude and usage intentions of robot service restaurants for senior customers. However, these relationships differed in terms of the income level of the customer groups. For the low-income senior-level group, hMCI was more influential on the perceived value. This study is meaningful because it analyzes the effect of MCI of seniors on the perceived value of robot service restaurants, which are growing rapidly in South Korea. It has empirically proved the moderating effect of different income groups, providing practical implications.
A re-optimized deep auto-encoder for gas turbine unsupervised anomaly detection

The use of hidden features or reconstruction errors extracted by deep auto-encoder (DAE) is becoming popular to discriminate anomalies from normal. Nevertheless, the fact that the existing methods only involving one of these two aspects loss the useful information from the other one motivates this investigation of method to combine reconstruction errors with hidden features. More importantly, anomalies are not removed in the training set when optimizing the traditional DAE, which weakens the discrimination of the reconstruction error. Aiming at these two problem, a new deep learning method, the so-called Re-optimized Deep Auto-Encoder (R-DAE), is developed to improve the detective performance for gas turbine unsupervised anomaly detection. First, the developed R-DAE takes the hidden features and the induced reconstruction errors as the final features. Second, to make the reconstruction error more discriminative, a sample selection mechanism is designed to attempt to remove anomalies from original unannotated training set, which makes the R-DAE almost unaffected by abnormal samples during training. Third, to effectively process time series, isolation forest is used to detect the obtained final features. Experiments on the real-life operation data of a gas turbine sample fleet validate the excellent detective performance of the proposed method.
A deep learned type-2 fuzzy neural network: Singular value decomposition approach

The main objective of this study is to present a novel dynamic fractional-order deep learned type-2 fuzzy logic system (FDT2-FLS) with improved estimation capability. The proposed FDT2-FLS is constructed based on the criteria of singular value decomposition and uncertainty bounds type-reduction. The upper and the lower singular values of the set of inputs are estimated by a simple filter and the output is obtained by fractional-order integral of the uncertainty bounds type-reduction. Using stability criteria of fractional-order systems, the adaptation rules of the consequent parameters are extracted such that the globally Mittag-Leffler stability is achieved. The proposed FDT2-FLS is employed for online dynamic identification of a hyperchaotic system, online prediction of chaotic time series and online prediction of glucose level in type-1 diabetes patients and its performance is compared with other well-known methods. It is shown that the proposed mechanism results in significantly better prediction and estimation performance with less tunable parameters in just one learning epoch.
Mass Testing With Contact Tracing Compared to Test and Trace for the Effective Suppression of COVID-19 in the United Kingdom: Systematic Review

Making testing available to everyone and tracing contacts might be the gold standard to control COVID-19. Many countries including the United Kingdom have relied on the symptom-based test and trace strategy in bringing the COVID-19 pandemic under control. The effectiveness of a test and trace strategy based on symptoms has been questionable and has failed to meet testing and tracing needs. This is further exacerbated by it not being delivered at the point of care, leading to rising cases and deaths. Increases in COVID-19 cases and deaths in the United Kingdom despite performing the highest number of tests in Europe suggest that symptom-based testing and contact tracing might not be effective as a control strategy. An alternative strategy is making testing available to all.The primary objective of this review was to compare mass testing and contact tracing with the conventional test and trace method in the suppression of SARS-CoV-2 infections. The secondary objective was to determine the proportion of asymptomatic COVID-19 cases reported during mass testing interventions.Literature in English was searched from September through December 2020 in Google Scholar, ScienceDirect, Mendeley, and PubMed. Search terms included "mass testing," "test and trace," "contact tracing," "COVID-19," "SARS-CoV-2," "effectiveness," "asymptomatic," "symptomatic," "community screening," "UK," and "2020." Search results were synthesized without meta-analysis using the direction of effect as the standardized metric and vote counting as the synthesis metric. A statistical synthesis was performed using Stata 14.2. Tabular and graphical methods were used to present findings.The literature search yielded 286 articles from Google Scholar, 20 from ScienceDirect, 14 from Mendeley, 27 from PubMed, and 15 through manual search. A total of 35 articles were included in the review, with a sample size of nearly 1 million participants. We found a 76.9% (10/13, 95% CI 46.2%-95.0%; P=.09) majority vote in favor of the intervention under the primary objective. The overall proportion of asymptomatic cases among those who tested positive and in the tested sample populations under the secondary objective was 40.7% (1084/2661, 95% CI 38.9%-42.6%) and 0.0% (1084/9,942,878, 95% CI 0.0%-0.0%), respectively.There was low-level but promising evidence that mass testing and contact tracing could be more effective in bringing the virus under control and even more effective if combined with social distancing and face coverings. The conventional test and trace method should be superseded by decentralized and regular mass rapid testing and contact tracing, championed by general practitioner surgeries and low-cost community services.
Geographic Named Entity Recognition and Disambiguation in Mexican News using word embeddings

In recent years, dense word embeddings for text representation have been widely used since they can model complex semantic and morphological characteristics of language, such as meaning in specific contexts and applications. Contrary to sparse representations, such as one-hot encoding or frequencies, word embeddings provide computational advantages and improvements on the results in many natural language processing tasks, similar to the automatic extraction of geospatial information. Computer systems capable of discovering geographic information from natural language involve a complex process called geoparsing. In this work, we explore the use of word embeddings for two NLP tasks: Geographic Named Entity Recognition and Geographic Entity Disambiguation, both as an effort to develop the first Mexican Geoparser. Our study shows that relationships between geographic and semantic spaces arise when we apply word embedding models over a corpus of documents in Mexican Spanish. Our models achieved high accuracy for geographic named entity recognition in Spanish.
A trustworthy industrial data management scheme based on redactable blockchain

Industrial data plays a key role in the industrial internet, and its secure collection problem has been highly valued by researchers. As Industrial Internet of Things (IIoT) devices are geographically dispersed and difficult to link, blockchain technology is usually introduced to solve the security management problem of industrial data. Unfortunately, the IIoT device is not stable, and it may leave incorrect messages in the blockchain, which will be permanently stored with potentially catastrophic consequences. As an effective solution, the redactable blockchain technology can allow people to modify the data on the blockchain. However, the existing redactable blockchain cannot guarantee industrial data security in the industrial internet due to requirements of trusted third parties, large overheads or lack of accountability mechanism. In this paper, we propose a trustworthy industrial data management scheme based on redactable blockchain in the industrial internet. To avoid additional burdens on industrial blockchain systems, a double-blockchain architecture is established to separate trapdoor management transactions. Distributed chameleon hash parameter generation and trapdoor recovery methods can avoid the security problems faced by the centralized organization. The fault-tolerant trapdoor recovery mechanism based on verifiable secret sharing technology as an alternative enhances the security of the system. The blockchain will record various information in the trapdoor management process and use it as evidence for accountability when disputes arise. The theoretical analysis and experiments show that the approach can effectively deal with malicious behaviors and has acceptable overhead.
Controlling Teleportation-Based Locomotion in Virtual Reality with Hand Gestures: A Comparative Evaluation of Two-Handed and One-Handed Techniques

Virtual Reality (VR) technology offers users the possibility to immerse and freely navigate through virtual worlds. An important component for achieving a high degree of immersion in VR is locomotion. Often discussed in the literature, a natural and effective way of controlling locomotion is still a general problem which needs to be solved. Recently, VR headset manufacturers have been integrating more sensors, allowing hand or eye tracking without any additional required equipment. This enables a wide range of application scenarios with natural freehand interaction techniques where no additional hardware is required. This paper focuses on techniques to control teleportation-based locomotion with hand gestures, where users are able to move around in VR using their hands only. With the help of a comprehensive study involving 21 participants, four different techniques are evaluated. The effectiveness and efficiency as well as user preferences of the presented techniques are determined. Two two-handed and two one-handed techniques are evaluated, revealing that it is possible to move comfortable and effectively through virtual worlds with a single hand only.
A multi-code 3D measurement technique based on deep learning

Benefiting from the merits of low cost, high accuracy and high resolution, fringe projection profilometry has been developing rapidly over the past decades. However, recovering the absolute phase with high accuracy and robustness effectively has always been significant challenge in fringe projection profilometry. In this paper, an intelligent Multi-code Deep Learning (MCDL) technique is developed to solve the high-slope absolute phase from only two patterns with high accuracy and robustness. Two sub-networks are designed for obtaining the wrapped phase and the fringe order. Specially, the proposed MCDL method can solve the high-level fringe orders by only a special multi-code pattern itself through a cooperative multi-connected convolutional neural network. By training the deep network with numerous datasets, the principle of unwrapping phase can be learned by the MCDL approach. Experiments demonstrate that the proposed method has the abilities of high robustness, efficiency and accuracy (measurement error: 0.0189mm, FOV: 250 mm × 200 mm), indicating potential applications for high-speed and high-accuracy three dimensional optical measurement.
Mitosis detection techniques in H&amp;E stained breast cancer pathological images: A comprehensive review

Quantifying mitosis in pathological sections is of great significance in the pathological diagnosis of breast cancer as it is used to evaluate the aggressiveness of the tumor and to provide more comprehensive and reliable information for accurate diagnosis and treatment. In this paper, we summarized the current mainstream methods of mitosis detection and divided them into four categories, namely traditional methods, deep learning methods, methods combining deep learning with traditional methods and other methods. Each method is introduced, and the performance indicators achieved by some of these methods and their results are discussed, compared, and evaluated. We summarize some solutions to the problem of positive and negative sample imbalance in mitotic datasets. Through the review of the research methods in this field, the existing methods of mitosis research in breast cancer are summarized, and the future developments are prospected.
Waste Classification using Convolutional Neural Network

Recycling of waste from households and industries, is one of the methods that has been proposed to reduce the ever-increasing pressure on landfills. Different types of waste types warrant different management techniques and hence, proper waste segregation according to its types is essential to facilitate proper recycling. Current existing segregation method still relies on manual hand-picking process. In this paper, a method; based on deep learning and computer vision concepts, to classify wastes using their images into six different waste types (glass, metal, paper, plastic, cardboard and others) has been proposed. Multiple-layered Convolutional Neural Network (CNN) model, specifically the well-known Inception-v3 model has been used for classification of waste, with trained dataset obtained from online sources. High classification accuracy of 92.5% is achievable using the proposed method. It is envisaged that the proposed waste classification method would pave the way for the automation of waste segregation with reduced human involvement and therefore, helps with the waste recycling efforts.
Analysis and Prediction of COVID-19 using Regression Models and Time Series Forecasting

In this paper, we are predicting and forecasting the COVID-19 outbreak in India based on the machine learning approach, where we aim to determine the optimal regression model for an in-depth analysis of the novel coronavirus in India. We are implementing the two regression models namely linear and polynomial and evaluating the two using the R squared score and error values. The COVID-19 dataset for India is being used to serve the research of this paper. The model is predicting the number of confirmed, recovered, and death cases based on the data available from March 12 to October 31,2020. For forecasting the future trend of these cases, we are utilizing the time series forecasting approach of tableau. Furthermore, the time series forecasting method is being employed to forecast the total count of confirmed cases in the future.
AdaSGN: Adapting Joint Number and Model Size for Efficient Skeleton-Based Action Recognition

Existing methods for skeleton-based action recognition mainly focus on improving the recognition accuracy, whereas the efficiency of the model is rarely considered. Recently, there are some works trying to speed up the skeleton modeling by designing light-weight modules. However, in addition to the model size, the amount of the data involved in the calculation is also an important factor for the running speed, especially for the skeleton data where most of the joints are redundant or non-informative to identify a specific skeleton. Besides, previous works usually employ one fix-sized model for all the samples regardless of the difficulty of recognition, which wastes computations for easy samples. To address these limitations, a novel approach, called AdaSGN, is proposed in this paper, which can reduce the computational cost of the inference process by adaptively controlling the input number of the joints of the skeleton on-the-fly. Moreover, it can also adaptively select the optimal model size for each sample to achieve a better trade-off between the accuracy and the efficiency. We conduct extensive experiments on three challenging datasets, namely, NTU-60, NTU-120 and SHREC, to verify the superiority of the proposed approach, where AdaSGN achieves comparable or even higher performance with much lower GFLOPs compared with the baseline method.
Making MA-ABE fully accountable: A blockchain-based approach for secure digital right management

With the rapid development of the digital world, digital rights management (DRM) becomes increasingly important. Multi-Authority Attribute Based Encryption (MA-ABE) schemes provide suitable solutions for flexible fine-grained access control in DRM. However, there are two accountable problems unsolved when applying existing ABE schemes to DRM directly: illegal authorization and key disclosure. Illegal authorization means that some attribute authorities might collude with users and issue attribute keys to unauthorized users illegally. Key disclosure denotes that some users might maliciously expose their attribute keys to the public. Both the above problems could lead to unauthorized users accessing digital content, which is extremely harmful to DRM. To solve these problems, we design a novel MA-ABE scheme based on blockchain. Our scheme ensures that a relevant user could obtain the final decryption attribute key only after all attribute authorities have issued their keys to the blockchain publicly. If a malicious attribute authority issues attribute keys to inappropriate users, the auditor defined in our scheme would detect the malicious behavior. Meanwhile, once some users publish their decryption keys to the public, the auditor is able to transparently trace such users utilizing an incentive mechanism. In addition, we design and implement two smart contracts on Ethereum, called RecordContract and TraceContract, which are used to record users’ encrypted attribute keys and encourage participants to trace the owner of disclosed decryption attribute key, respectively. Finally, the security proof and efficiency simulation show that the proposed scheme can be securely and efficiently applied to DRM.