Reviewerly/exHarmony huggingface dataset:
Machine Learning models for the estimation of the production of large utility-scale photovoltaic plants

Photovoltaic (PV) energy development has increased in the last years mainly based on large utility-scale plants. These plants are characterised by a huge number of panels connected to high-power inverters occupying a large land area. An accurate estimation of the power production of the PV plants is needed for failure detection, identifying production deviations, and the integration of the plants into the power grid. Various studies have used Machine Learning estimation techniques developed on very small PV plants. This paper deals with large utility-scale plants and uses all the available information to represent the non-uniform radiation over the whole studied solar field. Variables measured in up to four meteorological stations and distributed across the plant are used. Three PV plants with 1, 2 and 4 meteorological stations have been used to develop Machine Learning models. The hyperparameters were systematically optimised, demonstrating the improvements by comparing with a simple model based on Multiple Linear Regression. The best results were obtained with the Random Forest technique for the three PV plants, providing a RMS error value ranging from 1.9% to 5.4%. The final models were compared with those found in the literature for tiny PV plants showing in general much better performance.
Forecasting the stock-cryptocurrency relationship: Evidence from a dynamic GAS model

The impact of cryptocurrency on other assets has become a subject of intense research, given the rise of digital currency over the last decade. However, unlike traditional assets, cryptocurrency has been subject to extreme movements in price and volatility. As a result, it has become important for investors and risk managers to model and forecast volatility and correlation between digital currency and other assets. This paper utilises a multivariate generalised autoregressive score (GAS) model to study the time-varying dependence between stock prices (S&P500, NASDAQ, Dow Jones Industrial) and cryptocurrencies (Bitcoin and Ethereum). The results show that the GAS framework outperforms the traditional DCC-GARCH model, capturing the volatility persistence and non-linearity between stock and cryptocurrency. Regarding the correlations, while we identify a time-varying relationship, the strength of this relationship is in the low-to-moderate range. In addition, our forecasting exercise shows that the GAS specification has superior forecasting ability beyond certain horizon days compared to the DCC-GARCH model.
Effects of speed, complexity and stereoscopic VR cues on cybersickness examined via EEG and self-reported measures

This study evaluated the interplay between environmental cues in virtual reality (VR) and cybersickness as experienced by users of head-mounted displays (HMDs). Utilizing electroencephalogram (EEG) data and self-reported discomfort measures, the effects of three major VR cues – speed, scene complexity, and stereoscopic rendering – on cybersickness were examined, with the latter being of particular interest as it had not previously been studied explicitly in the context of VR-HMDs. Self-reported discomfort was assessed through in-VR single-item queries and post-VR simulator sickness questionnaires, accounting for both immediate and persistent cybersickness, respectively, and over three experiment sessions, accounting for the effects of accumulation. Analysis revealed connections that indicate a relationship between EEG data and the presence of cybersickness for all three cue types. Significant differences were observed in EEG relative power changes between the trials where cybersickness was and was not reported. EEG relative power changes were also linked to both immediate and persistent cybersickness, especially in the theta and gamma frequency bands. The increase in immediate discomfort with the stereoscopic rendering cues over successive sessions suggests a decrease in tolerance to these effects over time.
Knowledge-Based Visual Question Answering Using Multi-Modal Semantic Graph

The field of visual question answering (VQA) has seen a growing trend of integrating external knowledge sources to improve performance. However, owing to the potential incompleteness of external knowledge sources and the inherent mismatch between different forms of data, current knowledge-based visual question answering (KBVQA) techniques are still confronted with the challenge of effectively integrating and utilizing multiple heterogeneous data. To address this issue, a novel approach centered on a multi-modal semantic graph (MSG) is proposed. The MSG serves as a mechanism for effectively unifying the representation of heterogeneous data and diverse types of knowledge. Additionally, a multi-modal semantic graph knowledge reasoning model (MSG-KRM) is introduced to perform reasoning and deep fusion of image–text information and external knowledge sources. The development of the semantic graph involves extracting keywords from the image object detection information, question text, and external knowledge texts, which are then represented as symbol nodes. Three types of semantic graphs are then constructed based on the knowledge graph, including vision, question, and the external knowledge text, with non-symbol nodes added to connect these three independent graphs and marked with respective node and edge types. During the inference stage, the multi-modal semantic graph and image–text information are embedded into the feature semantic graph through three embedding methods, and a type-aware graph attention module is employed for deep reasoning. The final answer prediction is a blend of the output from the pre-trained model, graph pooling results, and the characteristics of non-symbolic nodes. The experimental results on the OK-VQA dataset show that the MSG-KRM model is superior to existing methods in terms of overall accuracy score, achieving a score of 43.58, and with improved accuracy for most subclass questions, proving the effectiveness of the proposed method.
Quantum Advantages of Teleportation and Dense Coding Protocols in an Open System

Quantum teleportation and dense coding are well-known quantum protocols that have been widely explored in the field of quantum computing. In this paper, the efficiency of quantum teleportation and dense coding protocols is examined in two-level atoms with two-photon transitions via the Stark shift effect, where each atom is separately coupled to a dissipative reservoir at zero temperature. Our results show that non-Markovianity and Stark shift can play constructive roles in restoring the quantum advantages of these protocols after they are diminished. These findings could offer a potential solution to preserving the computational and communicative advantages of quantum technologies.
Is there any market state-dependent contribution from Blockchain-enabled solutions to ESG investments? Evidence from conventional and Islamic ESG stocks

We examined the relationship between three Blockchain-based platforms (Advanced Internet Blocks (AIB), Blocktix (TIX), and BlockCDN (BCDN)), and the prominent global ESG-based indexes (S&P Global 1200 ESG Index (ESG) and its shariah-screened counterpart (SESG)).Based on the results of the research conducted between 3/5/2018 and 3/30/2022 reveal that the coherence of BCDN with ESG and SESG investments, strictly higher than AIB and TIX, decreases both by moving from a higher to a lower quantile and from a long-term to a short-term perspective. Additionally, it would be able to illustrate that in a pairwise directional connectedness analysis that excludes AIB-SESG pairs, all Blockchain-enabled innovations make significant contributions to both ESG-screened businesses at normal and bullish market conditions. According to this outcome, by analyzing the entire network of them, only BCDN and AIB could provide the network-transmitting function, respectively, in a bull market and a normal market, demonstrating the market state-dependency of Blockchain-based innovation spillovers. According to these findings, it would be an illusion to expect BCDN (a decentralized CDN trading market) or AIB (a platform that facilitates the use of advanced smart applications) to play a significant role in contributing to the creation of ESG markets and achieving sustainable objectives without putting a particular focus on market conditions. The concluding remarks could be useful for those who work in the field of environmental policy, investors who care about ESG factors, and market regulators.
Sources and Risk Characteristics of Heavy Metals in Plateau Soils Predicted by Geo-Detectors

The ecological environment of the remote plateaus has been a challenge plaguing many countries due to global warming, increased human activities, and frequent occurrence of various heavy metal (HM) pollutants. This paper analyzes the primary factors and potential susceptible regions’ characteristics related to soil HMs (As, Cd, Pb, Cr, Ni, and Zn) in the southern Tibet Plateau (TP) using Geo-detectors and a set of multi-source data from remote sensing and geographic and geological information. The geoaccumulation index showed that the As pollution was the most serious. The precipitation, pH, geological age (GA), and rock type (RT) were the most critical factors affecting HMs. Additionally, soil HMs were relatively unaffected by land use and clay. Based on the risk detector, the most key impact types (range) of the predominant factors of HM’s sources were identified. For example, precipitation (100–200 mm), pH (7–7.5), DEM (&gt;5500 m), RT (ophiolite), and GA (Ordovician) had the highest average concentration of As in each type (ranges) of the predominant factors. This work provides new data on the extent of soil HM contamination in the southern TP and predicts vulnerable areas of HM contamination, providing an important scientific basis for monitoring and managing HM in remote areas at high altitudes.
Metallogenic prediction based on geological-model driven and data-driven multisource information fusion: A case study of gold deposits in Xiong’ershan area, Henan Province, China

The traditional metallogenic prediction based on the geological model has been extensively used in prospecting, and the emerging machine learning method has also been successfully applied to mineral prospectivity prediction. In this study, we proposed a method based on the combination of model-driven and data-driven approaches, which was applied to the prospective area of the gold deposits in Xiong'ershan area. In this method, the geological prospecting model was taken as the prior knowledge of the data-driven model, and the metallogenic prediction elements of the prospecting model were constructed as the characteristic variables of the data-driven model, which improved the prediction accuracy of prospecting based on the geological model and reduced the uncertainty of outcome prediction based on data-driven. The XGBoost and LightGBM decision tree algorithms were selected for the data-driven method to create MPM. Then the SHAP framework was used to analyze the characteristic variables of the model, thus showing that geochemical prospecting could be carried out at the intersection of faults in the study area. Finally, the performance indicators based on machine learning and the legacy geological data were used to verify the robustness of the model. The AUC values of the final model were >0.95, and the new exploration targets were delineated in the high anomaly area, indicating the direction for subsequent exploration work.
Discriminative semi-supervised learning via deep and dictionary representation for image classification

Supervised dictionary learning and deep learning have achieved promising performance in the classification task. However, in many real-world applications there usually exist very limited labeled training samples, although abundant unlabeled data is relatively easy to collect. How to effectively exploit the discrimination of unlabeled data is still an open question, hence semi-supervised learning has attracted much attention from wide fields. Semi-supervised deep feature learning has well exploited the feature discrimination from only the discriminative viewpoint, while dictionary representation-based classification has also been applied to semi-supervised learning but with shallow features. In this paper, we propose a novel discriminative semi-supervised learning via deep and dictionary representation (DSSLDDR), which jointly utilizes the discrimination of dictionary representation for data reconstruction and the distinguishing feature of each sample. To exploit the powerful discrimination of dictionary representation, class-specific dictionaries are required to discriminatively reconstruct a sample, with the reconstruction error to predict the sample's class label. To exploit the semantic information, the deep neural network extracts discriminative features by using multiple nonlinear transformations to generate the powerful descriptor. Then the class-specific dictionary learning and deep network learning are integrated together to conduct more accurate class estimation for unlabeled data and learn a more discriminative classifier, where an entropy regularization is designed to balance and control the class estimation of unlabeled data. Furthermore, we propose the DSSLDDR++, the extension model of DSSLDDR based on consistency/contrastive learning to further improve the accuracy of class estimation for unlabeled data, making a more powerful semi-supervised learning classifier. Extensive experiments on benchmark datasets show the effectiveness of the proposed methods.
On the Complexity of String Matching for Graphs

Exact string matching in labeled graphs is the problem of searching paths of a graph G=(V, E) such that the concatenation of their node labels is equal to a given pattern string P [1. m ]. This basic problem can be found at the heart of more complex operations on variation graphs in computational biology, of query operations in graph databases, and of analysis operations in heterogeneous networks. We prove a conditional lower bound stating that, for any constant ε &gt; 0, an O (| E | 1 - ε m ) time, or an O (| E | m 1 - ε )time algorithm for exact string matching in graphs, with node labels and pattern drawn from a binary alphabet, cannot be achieved unless the Strong Exponential Time Hypothesis ( SETH ) is false. This holds even if restricted to undirected graphs with maximum node degree 2—that is, to zig-zag matching in bidirectional strings , or to deterministic directed acyclic graphs whose nodes have maximum sum of indegree and outdegree 3. These restricted cases make the lower bound stricter than what can be directly derived from related bounds on regular expression matching (Backurs and Indyk, FOCS’16). In fact, our bounds are tight in the sense that lowering the degree or the alphabet size yields linear time solvable problems. An interesting corollary is that exact and approximate matching are equally hard (i.e., quadratic time) in graphs under SETH . In comparison, the same problems restricted to strings have linear time vs quadratic time solutions, respectively (approximate pattern matching having also a matching SETH lower bound (Backurs and Indyk, STOC’15)).
A Framework for Data Privacy Preserving in Supply Chain Management Using Hybrid Meta-Heuristic Algorithm with Ethereum Blockchain Technology

Blockchain is a recently developed advanced technology. It has been assisted by a lot of interest in a decentralized and distributed public ledger system integrated as a peer-to-peer network. A tamper-proof digital framework is created for sharing and storing data, where the linked block structure is utilized to verify and store the data. A trusted consensus method has been adopted to synchronize the changes in the original data. However, it is challenging for Ethereum to maintain security at all blockchain levels. As such, “public–private key cryptography” can be utilized to provide privacy over Ethereum networks. Several privacy issues make it difficult to use blockchain approaches over various applications. Another issue is that the existing blockchain systems operate poorly over large-scale data. Owing to these issues, a novel blockchain framework in the Ethereum network with soft computing is proposed. The major intent of the proposed technology is to preserve the data for transmission purposes. This new model is enhanced with the help of a new hybrid algorithm: Adaptive Border Collie Rain Optimization Algorithm (ABC-ROA). This hybrid algorithm generates the optimal key for data restoration and sanitization. Optimal key generation is followed by deriving the multi objective constraints. Here, some of the noteworthy objectives, such as information preservation (IP) rate, degree of modification (DM), false rule (FR) generation, and hiding failure (HF) rate are considered. Finally, the proposed method is successfully implemented, and its results are validated through various measures. The recommended module ensures a higher security level for data sharing.
Secure PBFT Consensus-Based Lightweight Blockchain for Healthcare Application

Recent advancement in IoT technology has boosted the healthcare domain with enormous usage of IoT devices to provide elevated services to patients with chronic disorders on a real-time basis by the incorporation of IoT sensors on patients’ bodies. However, providing services ensuring security and maintaining the privacy of patients is a challenging task. Blockchain technology promises security in a distributed environment but popular consensus algorithms such as Proof of Work (PoW) and Proof of Stake (PoS) require huge computational resources and energy by making the IoT environment inefficient. This paper introduces a secure Practical Byzantine Fault Tolerance (PBFT) consensus-based lightweight blockchain algorithm for healthcare applications. To strengthen the PBFT consensus, highly trusted nodes were allowed to participate in the consensus algorithm using the Eigen Trust model and Verifiable Random Function (VRF) to select a random primary node from a group of trusted consensus nodes. The proposed algorithm is tested in a simulated environment and evaluated against the traditional PBFT consensus algorithm considering throughput, latency, and fault tolerance.
Probing BERT for Ranking Abilities

Contextual models like BERT are highly effective in numerous text-ranking tasks. However, it is still unclear as to whether contextual models understand well-established notions of relevance that are central to IR. In this paper, we use probing, a recent approach used to analyze language models, to investigate the ranking abilities of BERT-based rankers. Most of the probing literature has focussed on linguistic and knowledge-aware capabilities of models or axiomatic analysis of ranking models. In this paper, we fill an important gap in the information retrieval literature by conducting a layer-wise probing analysis using four probes based on lexical matching, semantic similarity as well as linguistic properties like coreference resolution and named entity recognition. Our experiments show an interesting trend that BERT-rankers better encode ranking abilities at intermediate layers. Based on our observations, we train a ranking model by augmenting the ranking data with the probe data to show initial yet consistent performance improvements (The code is available at https://github.com/yolomeus/probing-search/ ).
Railway Cyber-Security in the Era of Interconnected Systems: A Survey

Technological advances in the telecommunications industry have brought significant advantages in the management and performance of communication networks. The railway industry is among the ones that have benefited the most. These interconnected systems, however, have a wide area exposed to cyberattacks. This survey examines the cybersecurity aspects of railway systems by considering the standards, guidelines, frameworks, and technologies used in the industry to assess and mitigate cybersecurity risks, particularly regarding the relationship between safety and security. To do so, we dedicate specific attention to signaling, which fundamental reliance on computer and communication technologies allows us to explore better the multifaceted nature of the security of modern hyperconnected railway systems. With this in mind, we then move on to analyzing the approaches and tools that practitioners can use to facilitate the cyber security process. In detail, we present a view on cyber ranges as an enabling technology to model and emulate computer networks and attack-defense scenarios, study vulnerabilities' impact, and finally devise countermeasures. We also discuss several possible use cases strongly connected to the railway industry reality.
Partition-Tolerant and Byzantine-Tolerant Decision-Making for Distributed Robotic Systems with IOTA and ROS 2

With the increasing ubiquity of autonomous robotic solutions, the interest in their connectivity and in the cooperation within multi-robot systems is rising. Two aspects that are a matter of current research are robot security and secure multirobot collaboration robust to byzantine agents. Blockchain and other distributed ledger technologies (DLTs) have been proposed to address the challenges in both domains. Nonetheless, some key challenges include scalability and deployment within realworld networks. This paper presents an approach to integrating IOTA and ROS 2 for more scalable DLT-based robotic systems while allowing for network partition tolerance after deployment. This is, to the best of our knowledge, the first implementation of IOTA smart contracts for robotic systems, and the first integrated design with ROS 2. This is in comparison to the vast majority of the literature which relies on Ethereum. We present a general IOTA+ROS 2 architecture leading to partitiontolerant decision-making processes that also inherit byzantine tolerance properties from the embedded blockchain structures. We demonstrate the effectiveness of the proposed framework for a cooperative mapping application in a system with intermittent network connectivity. We show both superior performance with respect to Ethereum in the presence of network partitions, and a low impact in terms of computational resource utilization. These results open the path for wider integration of blockchain solutions in distributed robotic systems with less stringent connectivity and computational requirements.
Three-Factor Anonymous Authentication and Key Agreement Based on Fuzzy Biological Extraction for Industrial Internet of Things

With the increasing popularity and wide application of the Internet, the users (such as managers and data consumers) in the Industrial Internet of Things (IIoT) can remotely analyze and control real-time data collected by various smart sensor devices. However, there are many security and privacy issues in the process of transmitting collected data through public channels in IIoT environment. In order to against the illegal access by opponents, a novel anonymous user authentication and key agreement scheme based on hash and elliptic curve encryption is proposed in this article, which not only uses a pseudonym tuple database in control nodes to realize the functions of user dynamic joining and anonymity protection, but also resists key loss and device capture attacks through fuzzy biometric extraction technology. In addition, the formal secure analysis of the proposed scheme is carried out using the BAN logic model and ROR model, which proves the security of the proposed scheme. Meanwhile, we also prove the scheme can against the described existing attacks and meet the design goals by a detailed informal security discussion. Compared with the latest similar IIoT authentication proposals, our solution has a very obvious advantage in communication efficiency and realizes more functions. Hence, our scheme is more suitable for the IIoT environment, and can also generate greater benefits.