Reviewerly/exHarmony huggingface dataset:
CRYPTO-CURRENCIES TRADING AND ENERGY CONSUMPTION

This study empirically investigates the effects of crypto-currencies trading on the energy consumption as an important consequence of blockchain technology on climate change. In this article, we use the data of Bitcoin trading volume as well as all crypto-currencies trading volumes for the period going from 2014M1 to 2017M12 to investigate the effects on the primary energy consumption. Our empirical results show a positive correlation between crypto-currencies trading volumes and the energy consumption. Moreover, the crypto-currencies trading volume has a Granger-causality to energy consumption in the period of study indicating that these two variables have a long-run co-integration. In other words, our findings show a significant positive (and increasing) influence of cryptocurrency activities on the energy consumption in both short-run and long-run. This study investigates one step further in examining the effects of residuals of the crypto-currencies trading volume on the residuals in energy consumption to confirm that a higher trading volume in cryptocurrencies might cause a higher energy consumption. Our findings show a negative influence of the trading of crypto-currencies - precisely, the higher the crypto-currency activities are, the higher the energy consumption is, affecting therefore the environment.Keywords: Crypto-currencies, Environment; Energy consumption; Innovation.JEL Classifications: Q40, Q51, Q54, Q55, Q56DOI: https://doi.org/10.32479/ijeep.9258
Single Satellite Optical Imagery Dehazing using SAR Image Prior Based on conditional Generative Adversarial Networks

Satellite image dehazing aims at precisely retrieving the real situations of the obscured parts from the hazy remote sensing (RS) images, which is a challenging task since the hazy regions contain both ground features and haze components. Many approaches of removing haze focus on processing multi-spectral or RGB images, whereas few of them utilize multi-sensor data. The multi-sensor data fusion is significant to provide auxiliary information since RGB images are sensitive to atmospheric conditions. In this paper, a dataset called SateHaze1k is established and composed of 1200 pairs clear Synthetic Aperture Radar (SAR), hazy RGB, and corresponding ground truth images, which are divided into three degrees of the haze, i.e. thin, moderate, and thick fog. Moreover, we propose a novel fusion dehazing method to directly restore the haze-free RS images by using an end-to-end conditional generative adversarial network(cGAN). The proposed network combines the information of both RGB and SAR images to eliminate the image blurring. Besides, the dilated residual blocks of the generator can also sufficiently improve the dehazing effects. Our experiments demonstrate that the proposed method, which fuses the information of different sensors applied to the cloudy conditions, can achieve more precise results than other baseline models.
Twin-Incoherent Self-Expressive Locality-Adaptive Latent Dictionary Pair Learning for Classification

The projective dictionary pair learning (DPL) model jointly seeks a synthesis dictionary and an analysis dictionary by extracting the block-diagonal coefficients with an incoherence-constrained analysis dictionary. However, DPL fails to discover the underlying subspaces and salient features at the same time, and it cannot encode the neighborhood information of the embedded coding coefficients, especially adaptively. In addition, although the data can be well reconstructed via the minimization of the reconstruction error, useful distinguishing salient feature information may be lost and incorporated into the noise term. In this article, we propose a novel self-expressive adaptive locality-preserving framework: twin-incoherent self-expressive latent DPL (SLatDPL). To capture the salient features from the samples, SLatDPL minimizes a latent reconstruction error by integrating the coefficient learning and salient feature extraction into a unified model, which can also be used to simultaneously discover the underlying subspaces and salient features. To make the coefficients block diagonal and ensure that the salient features are discriminative, our SLatDPL regularizes them by imposing a twin-incoherence constraint. Moreover, SLatDPL utilizes a self-expressive adaptive weighting strategy that uses normalized block-diagonal coefficients to preserve the locality of the codes and salient features. SLatDPL can use the class-specific reconstruction residual to handle new data directly. Extensive simulations on several public databases demonstrate the satisfactory performance of our SLatDPL compared with related methods.
Benchmarking a High-Fidelity Mixed-Species Entangling Gate

We implement a two-qubit logic gate between a Ca43+ hyperfine qubit and a Sr88+ Zeeman qubit. For this pair of ion species, the S–P optical transitions are close enough that a single laser of wavelength 402 nm can be used to drive the gate but sufficiently well separated to give good spectral isolation and low photon scattering errors. We characterize the gate by full randomized benchmarking, gate set tomography, and Bell state analysis. The latter method gives a fidelity of 99.8(1)%, comparable to that of the best same-species gates and consistent with known sources of error.Received 4 May 2020Accepted 8 July 2020DOI:https://doi.org/10.1103/PhysRevLett.125.080504© 2020 American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum benchmarkingQuantum gatesQuantum information with trapped ionsPhysical SystemsTrapped ionsQuantum InformationAtomic, Molecular & Optical
The use of digital learning tools in the teachers’ professional activities to ensure sustainable development and democratization of education in European countries

The article deals with the revealing and analysis of the experience of the teachers’ use of the digital tools in their professional activities in the European countries (Germany, Italy, Netherland, Denmark, Estonia, Croatia, Sweden, Finland, Macedonia, Norway and others). The online recourses ensuring the creation of the sustainable environment for teachers and students including key competencies areas: entrepreneurship, citizenship, civic education and STEM are revealed in the article. The presented recourses allow to creative use of digital technologies, identify teachers’ and students’ needs and finding technological answers, solve technical problems, identifying gaps in digital competence. Using the analysed resources teachers become aware of the need to improve and update their digital competence; ability to support others in developing their digital competence; search for opportunities for self-development and awareness of digital evolution that is proclaimed by the UN ‘2030 Agenda for Sustainable Development’ adopted in 2015. The objective is to present the examples of the use of digital tools by European teachers, which are aimed at building the digital environment, developing the digital competence, and introducing innovations in the learning process. The critical use of presented experience in the comparative perspective can improve domestic teaching practice and teachers’ professional development.
Digital Learning Demand for Future Education 4.0—Case Studies at Malaysia Education Institutions

The rapid growth of the Industrial Revolution (IR) 4.0 has prompted the Malaysian Education Institution to transform the current education system into the future education system 4.0. The impact of IR 4.0 has opened a new paradigm for the Malaysian Educational Institution to ensure that all lecturers are capable of using information and communication technologies (ICT) in teaching and learning. However, there is a challenge in identifying appropriate digital learning platforms and tools to engage students in learning at their own pace. In this paper, we aimed to investigate the demand for digital learning platforms and tools according to the needs of students in Polytechnic Malaysia. The study was conducted randomly among 320 students from various fields of study in selected polytechnics. The analysis method used in this study was a quantitative method using questionnaires as an instrument. The results of our study indicated that e-learning platforms were the highest demand students’ preferred compared to other learning platforms and tools. Hence, the implications of this study could be useful as a guideline to assist Malaysian Polytechnic lecturers in strengthening the practice of using digital learning and develop digital proficiency for enabling education 4.0 in the future.
A Novel Document-Level Relation Extraction Method Based on BERT and Entity Information

Document-level relation extraction aims to extract the relationship among the entities in a paragraph of text. Compared with sentence-level, the text in document-level relation extraction is much longer and contains many more entities. It makes the document-level relation extraction a harder task. The number and complexity of entities make it necessary to provide enough information about the entities for the models in document-level relation extraction. To solve this problem, we put forward a document-level entity mask method with type information (DEMMT), which masks each mention of the entities by special tokens. By using this entity mask method, the model can accurately obtain every mention and type of the entities. Based on DEMMT, we propose a BERT-based one-pass model, through which we can predict the relationships among the entities by processing the text once. We test the proposed model on the DocRED dataset, which is a large scale open-domain document-level relation extraction dataset. The results on the manually annotated part of DocRED show that our approach obtains 6% F1 improvement compared with the state-of-the-art models that do not use pre-trained models and has 2% F1 improvement than BERT which does not use the DEMMT. On the distant supervision generated part of DocRED, the improvement of F1 is 2% compared with no pre-trained models, and 5% compared with pure BERT.
Bending recognition based on the analysis of fiber specklegrams using deep learning

Since the curvature induced variations of mode interference in multimode fiber (MMF) can be well represented by the fiber specklegrams, a bending recognition scheme based on the analysis of MMF specklegrams is proposed and verified. Amounts of specklegrams from the facet of MMF under different bendings were detected and used for training and testing in an image recognition algorithm based on deep learning. Good recognition results are provided by the specklegrams from MMF with diameter being 105 and 200 μm, for which the average accuracies of bending status recognition are respectively 92.8% and 96.6%. Because specklegram can represent the status of the whole section of MMF, such scheme indicates the capability to distinguish the specklegrams even when the MMF is under complicated bending. In this sense, the scheme presents the potential of a single MMF being used as a bending indicator or status monitor independently, which may find applications in distinguishing the status of certain structures, such as robotic arms, mechanical fingers and some disabled auxiliary equipments.
Artificial bee colony algorithm based on knowledge fusion

Abstract Artificial bee colony (ABC) algorithm is one of the branches of swarm intelligence. Several studies proved that the original ABC has powerful exploration and weak exploitation capabilities. Therefore, balancing exploration and exploitation is critical for ABC. Incorporating knowledge in intelligent optimization algorithms is important to enhance the optimization capability. In view of this, a novel ABC based on knowledge fusion (KFABC) is proposed. In KFABC, three kinds of knowledge are chosen. For each kind of knowledge, the corresponding utilization method is designed. By sensing the search status, a learning mechanism is proposed to adaptively select appropriate knowledge. Thirty-two benchmark problems are used to validate the optimization capability of KFABC. Results show that KFABC outperforms nine ABC and three differential evolution algorithms.
Analysis of Digital Image Watermarking Techniques through Hybrid Methods

Digital image watermarking is an attractive research area since it protects the multimedia data from unauthorized access. For designing an efficient and robust digital image watermarking system, the trade-off among imperceptibility, robustness, capacity, and security must be maintained. Various studies regarding this concern have been performed to ensure these requirements by hybridizing different domains, such as spatial and transform domains. In this paper, we have presented an analytical study of the existing hybrid digital image watermarking methods. At first, we have given a standard framework for designing a hybrid method that ensures the basic design requirements of watermarking for various applications. After a brief literature review, we compared and analyzed the complexity of several existing hybrid methods in a tabular form. The limitations and applications of these methods are also highlighted. Finally, we summarized the challenges of the existing methods and concluded the study by giving future research directions.
Science teachers’ integration of digital resources in education: A survey in rural areas of one Indonesian province

Digital resources can be implemented to support the quality of education, including for teachers teaching science. Studies have explored technology integration for schools in cities and urban areas; however, few studies focused on the technology integration in education of rural areas. Therefore, this study aimed at elaborating factors predicting Indonesian science teachers' integration of digital resources in education, especially for teaching activities in rural areas. Besides, demographic information was addressed to understand the differences regarding the integration of digital resources in education. We collected data from 217 respondents who are science teachers of senior and junior high schools in one province located in Sumatra, Indonesia. Using Smart PLS 3.0, we analyzed the data to understand the relationship between exogenous and endogenous variables. In addition, t-test was used to elaborate on the differences regarding the integration of digital resources based on demographic information. Findings from the path analysis show that attitude was the strongest driver predicting intention to use digital resources in education perceived by teachers in rural areas. Meanwhile, self-efficacy was reported to be insignificant for the intention to use. The only predictor that is positively significant for actual behavior was intention to use. Other factors (facilitating condition and knowledge and skills) did not affect actual behavior. In addition, the difference test informed that Mean values between participants' teaching experience was significantly different regarding the integration of digital resources of schools in rural areas. No significant differences were reported based on gender and level of school. The technology integration for schools in rural areas is different compared to the schools in cities and urban areas. With limited accesses and resources, this study might provide practical implications and recommendations for further research, especially on technology integration in schools of rural area.
Incomplete Descriptor Mining With Elastic Loss for Person Re-Identification

In this paper, we propose a novel person Re-ID model, Consecutive Batch DropBlock Network (CBDB-Net), to capture the attentive and robust person descriptor for the person Re-ID task. The CBDB-Net contains two novel designs: the Consecutive Batch DropBlock Module (CBDBM) and the Elastic Loss (EL). In the Consecutive Batch DropBlock Module (CBDBM), we firstly conduct uniform partition on the feature maps. And then, we independently and continuously drop each patch from top to bottom on the feature maps, which can output multiple incomplete feature maps. In the training stage, these multiple incomplete features can better encourage the Re-ID model to capture the robust person descriptor for the Re-ID task. In the Elastic Loss (EL), we design a novel weight control item to help the Re-ID model adaptively balance hard sample pairs and easy sample pairs in the whole training process. Through an extensive set of ablation studies, we verify that the Consecutive Batch DropBlock Module (CBDBM) and the Elastic Loss (EL) each contribute to the performance boosts of CBDB-Net. We demonstrate that our CBDB-Net can achieve the competitive performance on the three standard person Re-ID datasets (the Market-1501, the DukeMTMC-Re-ID, and the CUHK03 dataset), three occluded Person Re-ID datasets (the Occluded DukeMTMC, the Partial-REID, and the Partial iLIDS dataset), and a general image retrieval dataset (In-Shop Clothes Retrieval dataset).
Graph Neural Network-Based Diagnosis Prediction

Diagnosis prediction is an important predictive task in health care that aims to predict the patient future diagnosis based on their historical medical records. A crucial requirement for this task is to effectively model the high-dimensional, noisy, and temporal electronic health record (EHR) data. Existing studies fulfill this requirement by applying recurrent neural networks with attention mechanisms, but facing data insufficiency and noise problem. Recently, more accurate and robust medical knowledge-guided methods have been proposed and have achieved superior performance. These methods inject the knowledge from a graph structure medical ontology into deep models via attention mechanisms to provide supplementary information of the input data. However, these methods only partially leverage the knowledge graph and neglect the global structure information, which is an important feature. To address this problem, we propose an end-to-end robust solution, namely Graph Neural Network-Based Diagnosis Prediction (GNDP). First, we propose to utilize the medical knowledge graph as an internal information of a patient by constructing sequential patient graphs. These graphs not only carry the historical information from the EHR but also infuse with domain knowledge. Then we design a robust diagnosis prediction model based on a spatial-temporal graph convolutional network. The proposed model extracts meaningful features from sequential graph EHR data effectively through multiple spatial-temporal graph convolution units to generate robust patients' representations for accurate diagnosis predictions. We evaluate the performance of GNDP against a set of state-of-the-art methods on two real-world medical data sets, the results demonstrate that our methods can achieve a better utilization of knowledge graph and improve the accuracy on diagnosis prediction tasks.
Cyber-attacks to critical energy infrastructure and management issues: overview of selected cases

The purpose of the paper is to analyze the vulnerabilities of Critical Energy Infrastructures' systems in the event of cyber-attack.The global tendency of cyber-attacks puts Critical Energy Infrastructures on one of the first places for targets.Critical Infrastructure Protection (CIP) has become an increasingly relevant topic in the global industrial environment, as the consequences of cyber-attacks toward ICS can result in physical disruption and loss of human lives.The analysis presented in the paper will take into consideration three different case scenarios of cyber-attacks to Critical Energy Infrastructures, and will evaluate the outcomes and the tactics used by the organizations' response and recovery.
A Blockchain-Enabled Secure Power Trading Mechanism for Smart Grid Employing Wireless Networks

The rapid growth of renewable energy has increased the requirements of the smart grid for communication and processing capabilities.As an effective solution to collect and manage information, the wireless network can improve the efficiency of renewable energy management.But due to the wireless data transmission risk and centralized power trading, the smart grid employing wireless networks cannot guarantee the security of the electricity market and the high consumption of renewable energy.Recently, as an emerging data-sharing technology, the blockchain has attracted widespread attention and is considered to have the potential to solve above problems.In this paper, we propose a blockchain-enabled secure power trading mechanism for the smart grid employing wireless networks.The blockchain is introduced to record power data collected by the wireless network, and the smart contract can make reasonable trading decisions based on this.The dual-chain structure composed of local energy trading blockchain and renewable energy trading blockchain improves the efficiency of power trading and renewable energy consumption.To strengthen the stability of renewable energy producers and expand their scale, we also design a blockchain-enabled renewable energy incentive mechanism.Finally, the modified Southern California Edison 56 bus test feeder model validated our mechanism.
Trade Facilitation, Institutional Quality, and Sustainable Environment: Renewed Evidence from Sub-Saharan African Countries

This study examines the impact of trade facilitation and institutions on sustainable environment on a panel of 41 Sub-Saharan African countries over the period 2005–2014. Six composite indicators of trade facilitation (namely: export costs, import costs, export documents, import documents, export time, and import time) with a composite index (TFindex) are employed on four indicators of environmental sustainability (viz; natural resource depletion, net forest depletion, biocapacity, and ecological footprint of consumption) together with their combined index (Envindex) using the System Generalized Method of Moments. The following findings are established: first, the indicators of TF are significant and positively correlated with environmental sustainability (ES). Second, institutional quality dampens ES for the most part of the models. Third, the adverse effects of TF measures, institutional quality, and their interactive terms seem more severe from consumption angle than production, thus suggesting that the sources of environmental pollutants are more consumption oriented than production. What is more, Environmental Kuznets Curve Thesis finds a further empirical support. Going forward, the need for a well-harmonized trade facilitation procedure within a sound institutional environment, supported by a well-implemented national environmental protection law hold the promising key to addressing the prevailing environmental issues in the continent.
Simpful: A User-Friendly Python Library for Fuzzy Logic

Many researchers have used fuzzy set theory and fuzzy logic in a variety of applications related to computer science and engineering, given the capability of fuzzy inference systems to deal with uncertainty, represent vague concepts, and connect human language to numerical data.In this work we propose Simpful, a general-purpose and user-friendly Python library designed to facilitate the definition, analysis, and interpretation of fuzzy inference systems.Simpful provides a lightweight Application Programming Interface that allows to intuitively define fuzzy sets and fuzzy rules, and to perform fuzzy inference.Worthy of note, in Simpful the fuzzy rules are specified by means of strings of text written in natural language.We provide here some practical examples to show that Simpful represents a valuable addition to the open-source software that supports fuzzy reasoning.