Reviewerly/exHarmony huggingface dataset:
Bi-LSTM, GRU and 1D-CNN models for short-term photovoltaic panel efficiency forecasting case amorphous silicon grid-connected PV system

Authors:
raw_author_name: Abdellatif Ait Mansour
raw_affiliation_string: Research Team in Thermal and Applied Thermodynamics (2.T.A.), Mechanics, Energy Efficiency and Renewable Energies Laboratory (L.M.3.E.R.), Department of Physics, Faculty of Sciences and Techniques Errachidia, Moulay Ismaïl University of Meknès, B.P. 509, Boutalamine, Errachidia, Morocco
raw_author_name: Amine Tilioua
raw_affiliation_string: Research Team in Thermal and Applied Thermodynamics (2.T.A.), Mechanics, Energy Efficiency and Renewable Energies Laboratory (L.M.3.E.R.), Department of Physics, Faculty of Sciences and Techniques Errachidia, Moulay Ismaïl University of Meknès, B.P. 509, Boutalamine, Errachidia, Morocco
raw_author_name: Mohammed Touzani
raw_affiliation_string: Research Team in Thermal and Applied Thermodynamics (2.T.A.), Mechanics, Energy Efficiency and Renewable Energies Laboratory (L.M.3.E.R.), Department of Physics, Faculty of Sciences and Techniques Errachidia, Moulay Ismaïl University of Meknès, B.P. 509, Boutalamine, Errachidia, Morocco

Abstract: Photovoltaic (PV) panels stand as a prominent solution to meet the world's growing energy demands, due to their resistance to hard climate conditions, low-cost maintenance, and long lifetime. Nonetheless, the integration of PV power into electrical grids poses a significant challenge due to its inherent intermittency. This study aims to forecast future PV power based on historical records using Bidirectional Long Short-Term Memory (Bi-LSTM), One-Dimensional Convolutional Neural Network (1D-CNN), and Gated Recurrent Unit (GRU). Various performance metrics have been used to evaluate and compare the accuracy of the three models, including mean squared error, root mean squared error, mean absolute error, max error and R-squared for evaluation. The prediction of power photovoltaic based on the values registered in the last hour was carried out. Two scenarios have been investigated, with and without nighttime values to fit the models. Results reveal that the three forecasting models provide exceptional accuracy, achieving a correlation coefficient range of 96.9–97.2% for daytime PV power prediction in both scenarios, indicating a promising potential for these DNNs forecasters for optimizing energy production and improving overall PV system efficiency. The GRU, and BiLSTM-based forecasters showed identical results in terms of RMSE, MSE and MAE in both scenarios, while the 1D-CNN based forecaster showed accurate results in the second scenario, However, despite this improvement, it still falls behind forecasters based on Bi-LSTM or GRU in both scenarios.
Governance in the exploration of global and regional determinants of ICT development

Authors:
raw_author_name: Charles S. Saba
raw_affiliation_string: School of Economics, University of Johannesburg, Johannesburg, South Africa
raw_author_name: Simplice A. Asongu
raw_affiliation_string: Department of Economic & Data Science, New Uzbekistan University, 54 Mustaqillik Avenue, Tashkent, 100007, Uzbekistan; School of Economics, University of Johannesburg, Johannesburg, South Africa
raw_author_name: Nicholas Ngepah
raw_affiliation_string: School of Economics, University of Johannesburg, Johannesburg, South Africa
raw_author_name: Yolande E. Ngoungou
raw_affiliation_string: University of Yaoundé 2, Soa, Cameroon

Abstract: The present study assesses how governance affects information and communication technology (ICT) at the global level contingent on macroeconomic policy factors such as trade, foreign investment (FDI), manufacturing value added, and agricultural value added. The study focuses on 183 countries from 2003 to 2021, and the empirical evidence is based on the generalized method of moments (GMM). The following main findings are established. For the full sample, governance unconditionally promotes ICT development, while trade openness (industrial added value) moderates governance to promote (dampen) ICT development. In sub-Saharan Africa, only trade openness effectively moderates governance to induce an overall positive effect on ICT, while in the Middle East and North Africa (MENA) region, all policy variables moderate governance for an overall positive incidence on ICT sector development. The findings for the MENA region are confirmed in the Europe and Central Asia (ECA) region, with the exception of the moderating role of industrial added value, which engenders an overall negative effect. In the East and South Asia and the Pacific (ESAP) countries, one overall positive incidence is apparent in the role of trade openness, while net negative effects are established from the moderating roles of industrial added value and agricultural added value. In the American sub-sample, a positive (negative) net effect is apparent from the role of industrial added value (trade) in moderating the incidence of governance on ICT sector development. Finally, policy implications are discussed.
Collaborative threat intelligence: Enhancing IoT security through blockchain and machine learning integration

Authors:
raw_author_name: Ahsan Nazir
raw_affiliation_string: Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
raw_author_name: Jingsha He
raw_affiliation_string: Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
raw_author_name: Nafei Zhu
raw_affiliation_string: Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
raw_author_name: Ahsan Wajahat
raw_affiliation_string: Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
raw_author_name: Faheem Ullah
raw_affiliation_string: Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
raw_author_name: Sirajuddin Qureshi
raw_affiliation_string: Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
raw_author_name: Xiangjun Ma
raw_affiliation_string: Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
raw_author_name: Muhammad Salman Pathan
raw_affiliation_string: Department of Computer Science, Maynooth University, Ireland

Abstract: Ensuring robust security in the Internet of Things (IoT) landscape is of paramount importance. This research article presents a novel approach to enhance IoT security by leveraging collaborative threat intelligence and integrating blockchain technology with machine learning (ML) models. The iOS application acts as a central control centre, facilitating the reporting and sharing of detected threats. The shared threat data is securely stored on a blockchain network, enabling ML models to access and learn from a diverse range of threat scenarios. The research focuses on implementing Random Forest, Decision Tree classifier, Ensemble, LSTM, and CNN models on the IoT23 dataset within the context of a Collaborative Threat Intelligence Framework for IoT Security. Through an iterative process, the models’ accuracy is improved by reducing false negatives through the collaborative threat intelligence system. The article investigates the implementation details, privacy considerations, and the seamless integration of ML-based techniques for continuous model improvement. Experimental evaluations on the IoT23 dataset demonstrate the effectiveness of the proposed system in enhancing IoT security and mitigating potential threats. The research contributes to the advancement of collaborative threat intelligence and blockchain technology in the context of IoT security, paving the way for more secure and reliable IoT deployments.
Estimating quantum mutual information through a quantum neural network

Authors:
raw_author_name: Junseo Lee
raw_affiliation_string: School of Electrical and Electronic Engineering, Yonsei University, Seoul, 03722, Korea
raw_author_name: Kabgyun Jeong
raw_affiliation_string: Research Institute of Mathematics, Seoul National University, Seoul, 08826, Korea

Abstract: Abstract We propose a method of quantum machine learning called quantum mutual information neural estimation (QMINE) for estimating von Neumann entropy and quantum mutual information, which are fundamental properties in quantum information theory. The QMINE proposed here basically utilizes a technique of quantum neural networks (QNNs), to minimize a loss function that determines the von Neumann entropy, and thus quantum mutual information, which is believed more powerful to process quantum datasets than conventional neural networks due to quantum superposition and entanglement. To create a precise loss function, we propose a quantum Donsker-Varadhan representation (QDVR), which is a quantum analog of the classical Donsker-Varadhan representation. By exploiting a parameter shift rule on parameterized quantum circuits, we can efficiently implement and optimize the QNN and estimate the quantum entropies using the QMINE technique. Furthermore, numerical observations support our predictions of QDVR and demonstrate the good performance of QMINE.
Media Forensic Considerations of the Usage of Artificial Intelligence Using the Example of DeepFake Detection

Authors:
raw_author_name: Dennis Siegel
raw_affiliation_string: Department of Computer Science, Otto-von-Guericke University, 39106 Magdeburg, Germany
raw_author_name: Christian Kraetzer
raw_affiliation_string: Department of Computer Science, Otto-von-Guericke University, 39106 Magdeburg, Germany
raw_author_name: Stefan Seidlitz
raw_affiliation_string: Department of Computer Science, Otto-von-Guericke University, 39106 Magdeburg, Germany
raw_author_name: Jana Dittmann
raw_affiliation_string: Department of Computer Science, Otto-von-Guericke University, 39106 Magdeburg, Germany

Abstract: In recent discussions in the European Parliament, the need for regulations for so-called high-risk artificial intelligence (AI) systems was identified, which are currently codified in the upcoming EU Artificial Intelligence Act (AIA) and approved by the European Parliament. The AIA is the first document to be turned into European Law. This initiative focuses on turning AI systems in decision support systems (human-in-the-loop and human-in-command), where the human operator remains in control of the system. While this supposedly solves accountability issues, it includes, on one hand, the necessary human–computer interaction as a potential new source of errors; on the other hand, it is potentially a very effective approach for decision interpretation and verification. This paper discusses the necessary requirements for high-risk AI systems once the AIA comes into force. Particular attention is paid to the opportunities and limitations that result from the decision support system and increasing the explainability of the system. This is illustrated using the example of the media forensic task of DeepFake detection.
Hebbian dreaming for small datasets

Authors:
raw_author_name: Elena Agliari
raw_affiliation_string: Department of Mathematics of Sapienza Università di Roma, Rome, Italy
raw_author_name: Francesco Alemanno
raw_affiliation_string: Department of Mathematics and Physics of Università del Salento, Lecce, Italy
raw_author_name: Miriam Aquaro
raw_affiliation_string: Department of Mathematics of Sapienza Università di Roma, Rome, Italy
raw_author_name: Adriano Barra
raw_affiliation_string: Department of Mathematics and Physics of Università del Salento, Lecce, Italy
raw_author_name: Fabrizio Durante
raw_affiliation_string: Department of Economic Sciences of Università del Salento, Lecce, Italy
raw_author_name: Ido Kanter
raw_affiliation_string: Department of Physics of Bar-Ilan University, Ramat Gan, Israel

Abstract: The dreaming Hopfield model constitutes a generalization of the Hebbian paradigm for neural networks, that is able to perform on-line learning when "awake" and also to account for off-line "sleeping" mechanisms. The latter have been shown to enhance storing in such a way that, in the long sleep-time limit, this model can reach the maximal storage capacity achievable by networks equipped with symmetric pairwise interactions. In this paper, we inspect the minimal amount of information that must be supplied to such a network to guarantee a successful generalization, and we test it both on random synthetic and on standard structured datasets (i.e., MNIST, Fashion-MNIST and Olivetti). By comparing these minimal thresholds of information with those required by the standard (i.e., always "awake") Hopfield model, we prove that the present network can save up to ∼90% of the dataset size, yet preserving the same performance of the standard counterpart. This suggests that sleep may play a pivotal role in explaining the gap between the large volumes of data required to train artificial neural networks and the relatively small volumes needed by their biological counterparts. Further, we prove that the model Cost function (typically used in statistical mechanics) admits a representation in terms of a standard Loss function (typically used in machine learning) and this allows us to analyze its emergent computational skills both theoretically and computationally: a quantitative picture of its capabilities as a function of its control parameters is achieved and consistency between the two approaches is highlighted. The resulting network is an associative memory for pattern recognition tasks that learns from examples on-line, generalizes correctly (in suitable regions of its control parameters) and optimizes its storage capacity by off-line sleeping: such a reduction of the training cost can be inspiring toward sustainable AI and in situations where data are relatively sparse.
Quantum field theory for coherent photons: isomorphism between stokes parameters and spin expectation values

Authors:
raw_author_name: Shinichi Saito
raw_affiliation_string: Center for Exploratory Research Laboratory, Research and Development Group, Hitachi, Ltd., Tokyo, Japan

Abstract: Stokes parameters ( S ) on the Poincaré sphere are very useful values to describe the polarisation state of photons. However, the fundamental principle on the nature of polarisation is not completely understood, yet, because we have no concrete consensus on how to describe spin of photons, quantum-mechanically. Here, we have considered a monochromatic coherent ray of photons, described by a many-body coherent state, and established a fundamental basis to describe the spin state of photons, in connection with a classical description based on Stokes parameters. We show that a spinor description of the coherent state is equivalent to Jones vector for polarisation states, and obtain the spin operators <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m1"><mml:mrow><mml:mo stretchy="false">(</mml:mo><mml:mrow><mml:mover accent="true"><mml:mrow><mml:mi mathvariant="bold">S</mml:mi></mml:mrow><mml:mo stretchy="false">̂</mml:mo></mml:mover></mml:mrow><mml:mo stretchy="false">)</mml:mo></mml:mrow></mml:math> of all components based on rotators in an SU(2) group theory. Polarisation controllers such as phase-shifters and rotators are also obtained as quantum-mechanical field operators to change the phase of the wavefunction for polarisation states. We show that the Stokes parameters are quantum-mechanical average of the spin operators, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m2"><mml:mi mathvariant="bold">S</mml:mi><mml:mo>=</mml:mo><mml:mrow><mml:mo stretchy="false">⟨</mml:mo><mml:mrow><mml:mover accent="true"><mml:mrow><mml:mi mathvariant="bold">S</mml:mi></mml:mrow><mml:mo stretchy="false">̂</mml:mo></mml:mover></mml:mrow><mml:mo stretchy="false">⟩</mml:mo></mml:mrow></mml:math> .
Individual differences in emoji comprehension: Gender, age, and culture

Authors:
raw_author_name: Yihua Chen
raw_affiliation_string: School of Psychology, University of Nottingham, University Park, Nottingham, United Kingdom
raw_author_name: Xingchen Yang
raw_affiliation_string: School of Psychology, University of Nottingham, University Park, Nottingham, United Kingdom
raw_author_name: Hannah Howman
raw_affiliation_string: School of Psychology, University of Nottingham, University Park, Nottingham, United Kingdom
raw_author_name: Ruth Filik
raw_affiliation_string: School of Psychology, University of Nottingham, University Park, Nottingham, United Kingdom

Abstract: Emoji are an important substitute for non-verbal cues (such as facial expressions) in online written communication. So far, however, little is known about individual differences regarding how they are perceived. In the current study, we examined the influence of gender, age, and culture on emoji comprehension. Specifically, a sample of 523 participants across the UK and China completed an emoji classification task. In this task, they were presented with a series of emoji, each representing one of six facial emotional expressions, across four commonly used platforms (Apple, Android, WeChat, and Windows). Their task was to choose from one of six labels (happy, sad, angry, surprised, fearful, disgusted) which emotion was represented by each emoji. Results showed that all factors (age, gender, and culture) had a significant impact on how emojis were classified by participants. This has important implications when considering emoji use, for example, conversation with partners from different cultures.
Mathematics, word problems, common sense, and artificial intelligence

Authors:
raw_author_name: Ernest Davis
raw_affiliation_string: Department of Computer Science, New York University, New York, New York 10012

Abstract: The paper discusses the capacities and limitations of current artificial intelligence (AI) technology to solve word problems that combine elementary mathematics with commonsense reasoning. No existing AI systems can solve these reliably. We review three approaches that have been developed, using AI natural language technology: outputting the answer directly, outputting a computer program that solves the problem, and outputting a formalized representation that can be input to an automated theorem verifier. We review some benchmarks that have been developed to evaluate these systems and some experimental studies. We discuss the limitations of the existing technology at solving these kinds of problems. We argue that it is not clear whether these kinds of limitations will be important in developing AI technology for pure mathematical research, but that they will be important in applications of mathematics, and may well be important in developing programs capable of reading and understanding mathematical content written by humans.
The Role of Financial Sanctions and Financial Development Factors on Central Bank Digital Currency Implementation

Authors:
raw_author_name: Carmen De-Pablos-Heredero
raw_affiliation_string: Department of Business Economics (Administration, Management, and Organization), Applied Economics II and Fundamentals of Economic Analysis, Universidad Rey Juan Carlos, Paseo de los Artilleros s/n, 28032 Madrid, Spain

Abstract: This study investigates the influence of a country’s financial access and stability and the adoption of retail central bank digital currencies (CBDCs) across 71 countries. Using an ordinal logit model, we examine how individual financial access, the ownership of credit cards, financing accessibility by firms, offshore loans, financial sanctions, and the ownership structure of financial institutions influence the probability of CBDC adoption in nations. These findings reveal that nations facing financial sanctions and those with substantial offshore bank loans are more inclined to adopt CBDCs. Furthermore, a significant relationship is observed in countries where many people have restricted financial access, indicating heightened interest in CBDC adoption. Interestingly, no statistically significant relationship was found between the adoption of CBDCs and the percentage of foreign-owned banks in each country. The results show that countries with low financial stability and financial access adopt CBDCs faster. This study expands our knowledge of how a nation’s financial situation influences its adoption of CBDCs. The results provide important and relevant insights into the current discussion of the direction of global finance.
A Blockchain-Based Scalability Solution with Microgrids Peer-to-Peer Trade

Authors:
raw_author_name: Ameni Boumaiza
raw_affiliation_string: Qatar Environment and Energy Research Institute, Hamad Bin Khalifa University, Qatar Foundation, Doha P.O. Box 34110, Qatar

Abstract: In recent years, advancements in rooftop solar panel technology have sparked a revolution in the electricity markets. This has given rise to a new concept of energy exchange—the ability for consumers and producers to trade localized energy. This concept has been made possible by the emergence of blockchain technology, which has gained significant traction in the energy markets. Its unique ability to facilitate peer-to-peer (P2P) energy transactions has made it a promising solution for the trilemma of scalability, security, and decentralization. However, while blockchain technology has shown great potential, it is still in its early stages of development and has yet to reach its full potential. To fully understand the potential of blockchain technology in P2P energy trading, it is important to explore the trilemma in depth. This study proposes a blockchain-based scalability solution with a focus on P2P energy trading. This strategy is supported by empirical modeling, utilizing data gathered from a trial case study. The results of this study demonstrate that the suggested technique outperforms base-layer models in terms of scalability while still maintaining the essential elements of security and decentralization. This proposed solution not only has the potential to revolutionize the energy markets but also has broader implications. By providing a more secure and decentralized platform for P2P energy trading, it has the potential to address issues of energy distribution inequality and promote the adoption of renewable energy. With blockchain technology, individuals and communities have the opportunity to take control of their energy usage and production, reducing their reliance on traditional centralized energy systems. This not only has the potential to lower energy costs for consumers but also contributes to the overall goal of reducing carbon emissions and mitigating the effects of climate change. The combination of rooftop solar panel technology and blockchain applications has the potential to create a significant shift toward decentralized and sustainable energy systems. This will not only benefit individuals and communities but also have a positive impact on the environment and the global energy market. However, for this transition to occur, it is crucial that governments and energy companies continue to support and invest in these advancements.