Reviewerly/exHarmony huggingface dataset:
Deriving Near Real-Time Cloud Cover at Photovoltaic Plants Using Geostationary Satellite Observations

Clouds can block solar radiation from reaching the surface, so timely and effective cloud cover test and forecasting is critical to the operation and economic efficiency of photovoltaic (PV) plants. Traditional cloud cover algorithms based on meteorological satellite observation require lots of auxiliary data and computing resources, which are hard to be implemented or transplanted for the application at PV plants. In this study, a portable and fast cloud mask algorithm (FCMA) is developed to provide near real-time (NRT) spatial-temporally matched cloud cover products for PV plants. The geostationary satellite imager data from the Advanced Himawari Imager aboard Himawari-8 and the related operational cloud mask algorithm (OCMA) are employed as the benchmark for comparison and validation. Besides, the ground-based manually observed cloud cover data at seven quintessential stations at 08:00 and 14:00 BJT (Beijing Time) in 2017 are employed to verify the accuracy of cloud cover data derived from FCMA and OCMA. The results show a high consistency with the ground-based data, and the average correlation coefficient ( R ) is close to 0.85. Remarkably, the detection accuracy of FCMA is slightly higher than that of OCMA, demonstrating the feasibility of FCMA for providing NRT cloud cover at PV plants.
Vanets Technology for Improving Safe Driving Behavior

The disobedience of traffic rules is a public health problem. There is an absence of understanding driving behavior from Zizekian psychoanalysis. The general scope of this research is based on the interdisciplinary approach in a Transformative Social Marketing. The theoretical implication presents a new view of human behavior inspired from Zizekian psychoanalysis . Three groups (Pleasure, Care, Refuge) emerge from the Real-Symbolic-Imaginary Triad that orients behaviors to obey or disobey traffic rules. The methodological implication is the «psychoanalytic map» as a new methodological tool for more understanding the complexity of driving behavior. The practical implication is an authentic act, emerged from the driver, to choose a personalized message which changes after a period using Vehicular Ad-Hoc Network (VANET).
Machine Learning Models for Mapping Potentially Toxic Elements and Assessing Human Health Risk in Soils Affected by Mining Activity

Determining the levels and spatial distribution of potentially toxic elements (PTEs) is essential in designing mitigating strategies to remediate contaminated areas. Efficient prediction of PTEs is difficult due to high spatial variability and multiple sources of contamination. Therefore, the use of machine learning algorithms is an alternative for the improvement of the predictions. Objective of the study was to model the spatial distribution of PTEs in soils surrounding a tailings pile and to assess potential risks to human health. A total set of 116 soil samples was collected to a depth of 0-10 cm. The covariate database was composed of morphometric variables derived from a DEM and the creation of a tailings pile location covariate. The models tested were Cubist (CUB), Random Forest (RF), Support Vector Machine (SVM), and Linear Model (Lm). Based on the best predictive model, maps showing the spatial distribution, pollution and risk to human health were created. The SVM model had the best performance for predicting spatially the soil contents of Pb and Zn. It was possible to verify that the mining tailings pile is the main source of Pb and Zn contamination in the study area. The enrichment factor and the geoaccumulation index of Pb and Zn show that the soils close to the tailings piles are extremely contaminated, and pose risks to human health, especially for children. Therefore, new studies must be carried out with the aim of monitoring and rehabilitating the contaminated areas.
Globalization of the Research and Educational Centers’ Modern Architecture in the Processes of New Construction and Reconstruction

The key priority areas for the architectural formation of buildings and structures of the research and educational centers in the conditions of globalization processes are represented in the paper. Analyzed modern trends indicate that the new educational technologies require creation of self-regulating spaces for their full functioning and development. It is motivated by the new architectural forms of objects in scientific and educational areas. The architectural scripts for the organization of the modern research and educational centers are proposed. These scripts are important for the implementation of subsequent design, experimental and research solutions aimed at the organization and development of the objects in scientific and educational areas.
Scientific Precariat: Individualism versus Collectivism

The article is a reply to Ilya T. Kasavin’s “Creativity as a social phenomenon” and is devoted to the phenomenon of the scientific precariat. A systematic analysis of the relations between the scientific precariat and the academic community as a dialectical opposition of the individual and the collective is undertaken. The method of critical analysis is aimed at rethinking the stable ideas that have developed in science about the collectivity of scientific work. The concepts of labor and employment in science are considered. It is concluded that the global development of digital technologies has led to the disappearance of the boundaries between physical and intellectual labor, against which there is an elevation of creative activity. The availability of information on the Internet, opening up incredible opportunities for research, destroys the monopoly of professional scientific communities on the possession of scientific knowledge. Scientific precarious loudly declare themselves in the public space, demonstrating the boldness and unusual nature of the ideas expressed. Inspired seekers of scientific truth embark on a free voyage through the vastness of the unknown. Traditional scientific communications, while retaining their significance, are enriched with new, non-standard ideas of precarious scientists who, ignoring rules and hierarchies, bring the creative spirit of freedom into modern science. However, the activities of such scientists may have an ambiguous assessment: a precarious scientist completely loses touch with the existing methods and approaches of classical science, and flight from work standards instead of expanding the horizons of scientific creativity turns into new problems caused by “multi-task” and instability of the labor activity of a “free” scientist. Despite the fact that in the conditions of the development of modern society and technology, the opposition of the pair of individual and collective is leveled, many scientists need common structures that determine the development of science, which at the present stage of the development of scientific knowledge are rather represented not by a social organization, but by an intellectual, linguistic and methodological unity focused on the creative development of the world.
Berry-Esseen bound for smooth estimator of distribution function under length-biased data

In this paper, by using a sampling procedure, subjected to length-bias, the distribution function F is estimated by the kernel-type estimator Fns, and also a Berry-Esseen type bound for the smoothed estimator is established in this setting. Further, it is shown that the rate of the normal approximation is O(n−1/6) under some appropriate conditions.
Safety Certification with the Open Source Microkernel-Based Operating System L4Re

We report on recent efforts to certify the open-source operating system framework L4Re [2] and its commercial variant EB corbos Hypervisor [1]. Certification is carried out in adherence to ISO 26262 and targets an Automotive Safety Integrity Level B (ASIL-B). Unlike existing work on OS verification [3], the presented work discusses how a complete software system can be taken to certification. The paper identifies challenges arising from the re-use of open-source legacy software in a safety context and provides strategies for its certification without re-implementing major parts of the system. To achieve this, the paper introduces a new safety architecture based on the L4 style of “system-call forwarding”, hierarchical memory management and configuration-based setup of inter-process communication relations. Collectively, the proposed innovations isolate safety applications from hidden errors in components not developed in adherence to the ISO 26262, in this case the feature-rich software stack implementing the L4Re userland.
Spatial Perception Assessment in Immersive Virtual Environments. A Methodological Approach

This paper raises a methodological proposal to evaluate spatial perception in Immersive Virtual Environments (IVEs). It proposes a study case to be carried out in the classrooms of the polytechnic building school in Barcelona and is part of a more ambitious project that aims to delve into the Physical Modeling and Simulation of real spaces to evaluate the user experience based on educational and physiological data analysis. From the construction of a digital twin of the proposed space, the parameters of spatial perception that should be evaluated are detailed, as well as the procedure to be followed. In addition, a spatial perception indicator construction is proposed to allow other factors comparison. The results of the three proposed tests should serve as a basis for user perception evaluation in a realistic digitally simulated immersive environment, compared to a real space. A high correlation between the test results should indicate that these virtual interfaces are accurate tools for representing real environments, concluding that they could be used as architectural simulation tools.
VPL for HBIM: Algorithmic Generative Processes for the Thematic Mapping of Information Models

Reality-based tools for architectural surveys allow the acquisition of multiple integrated data (morphological, material, colorimetric). The number and heterogeneity of the acquired data represent an advantage for information gathering; yet, at the same time, this calls for new considerations regarding big data management and output. Following these premises, this article presents the first results of an ongoing research activity aimed to the definition and testing of parametric tools (VPL for interoperability – BIM/H-BIM) for RGB-based, semi-automated thematic representations of the conservation state of architectural façades. Specifically, the contribution proposes an algorithmic generative definition for decay phenomena mapping. This was experimented on the façades of the Church of the Archconfraternity of Santa Maria delle Grazie e Sant’Andrea Apostolo (Naples, Italy), surveyed through digital terrestrial photogrammetry and thermographic cameras. Orthophotos and thermographic mosaics constitute the types of RGB mapping used to validate the designed tool. This process is aimed to the visualization/modeling of data related to complex phenomena and to the rapid production of graphs for documentary and preventive purposes.
Comparison of Quantum Computing Versus Traditional Computing and Use of Python in Implementation of Quantum Computing

Quantum Computing is hybridization of Quantum Mechanics, Mathematics, Information Technology and Computer Science. Actually Quantum Mechanics is a branch of Physics, in which concept of quantum is explained.Researcher wants to study a quantum computing. Points discussed in this research papers are as follows What is Quantum computing? Why do we need quantum computing? Various Applications of Quantum Computers What is exactly Quantum Information Science and Technology? How the Quantum Programming is done? Use of Python (QisKit) for implementation of Quantum Computing?Initially researcher started from Quantum. Then he has tried to search relation in Quantum and Bit. i.e. QUBIT. Apart from Quantum computing, there is a quantum sensors, quantum communication, quantum simulation and quantum Materials. Finally he has explained future of Quantum and the applications of quantum till today.
Public Policies and MSME Growth in Côte d’Ivoire and Senegal: COVID-19 as a Pebble in the Wheel?

The COVID-19 pandemic has heavily affected Micro, small and medium enterprises (MSMEs). Using data from the World Bank Enterprise Surveys and other national statistics, this study aims to analyze the potential impact of MSMEs-related public policies in Côte d’Ivoire and Senegal over the last decade, including the context of COVID-19. Both countries have adopted specific policies dedicated to MSMEs, strengthened institutions, and mobilized financial and non-financial instruments. To mitigate the negative impact of COVID-19, Cote d’Ivoire and Senegal decided to allocate CFAF 150 billion and CFAF 100 billion respectively to support MSMEs. Despite the differences between the two countries, we find a strong positive association between the MSME funds implemented and the number of MSMEs supported and jobs saved, suggesting a significant positive impact on businesses and employment, particularly in Cote d’Ivoire. Public policies seem to be decisive, but not sufficient. They should be strengthened to help MSME emerge from the crisis and become competitive.
Bug Triaging Automation using Text Processing and Machine Learning Techniques

For well-known software methods, the number of routinely offered bug reports is extreme. Triaging these successful reports is a period-absorbing task. Part of the bug sort is the appointment of a report to a planner, accompanying the appropriate knowledge. In view of this paper, we do suggest at which point, as a matter of typical matter, plan construction workers have the appropriate information for directing a bug report, fixing the acknowledged component identified in the short letter of the bug report. Our work is the first to test the orderly list of architects for forecast truth in bug assignments. Automated categorization and prioritisation of bug reports have been examined bymany analysts. However, limited progress has been made in this area. In this work, we present bug triaging by way of NLP and machine intelligence.
Analysis of Cyclic Fault Propagation via ASP

Analyzing the propagation of faults is part of the preliminary safety assessment for complex safety-critical systems. A recent work proposes an smt-based approach to deal with propagation of faults in presence of circular dependencies. The set of all the fault configurations that cause the violation of a property, also referred to as the set of minimal cut sets, is computed by means of repeated calls to the smt solver, hence enumerating all minimal models of an smt formula. Circularity is dealt with by imposing a strict temporal order, using the theory of difference logic. In this paper, we explore the use of Answer-Set Programming to tackle the same problem. We propose two encodings, leveraging the notion of stable model. The first approach deals with cycles in the encoding, while the second relies on asp Modulo Acyclicity (aspma). We experimentally evaluate the three approaches on a comprehensive set of benchmarks. The first asp-based encoding significantly outperforms the smt-based approach; the aspma-based encoding, on the other hand, does not yield the expected performance gains.
Knowledge-Based Support for Adhesive Selection

This work presents a real-life application developed to assist adhesive experts in the selection of suitable adhesives. As the popularity of adhesive joints in industry increases, so does the need for tools to support the selection process. While such tools already exist, they are either too limited in scope, or offer too little flexibility in use. In this work, we first extract experts’ knowledge about this domain and formalize it in a Knowledge Base (KB). The IDP-Z3 reasoning system can then be used to derive the necessary functionality from this KB. Together with a user-friendly interactive interface, this creates an easy-to-use tool capable of assisting the adhesive experts. The experts are positive about the tool, stating that it will help save time and find more suitable adhesives.
Representing Abstract Dialectical Frameworks with Binary Decision Diagrams

Abstract dialectical frameworks (ADFs) are a well-studied generalisation of the prominent argumentation frameworks due to Phan Minh Dung. In this paper we propose to use reduced ordered binary decision diagrams (roBDDs) as a suitable representation of the acceptance conditions of arguments within ADFs. We first show that computational complexity of reasoning on ADFs represented by roBDDs is milder than in the general case, with a drop of one level in the polynomial hierarchy. Furthermore, we present a framework to systematically define heuristics for search space exploitation, based on easily retrievable properties of roBDDs and the recently proposed approach of weighted faceted navigation for answer set programming. Finally, we present preliminary experiments of an implementation of our approach showing promise both when compared to state-of-the-art solvers and when developing heuristics for reasoning.
Interlinking Logic Programs and Argumentation Frameworks

Logic programs (LPs) and argumentation frameworks (AFs) are two declarative knowledge representation (KR) formalisms used for different reasoning tasks. The purpose of this study is interlinking two different reasoning components. To this end, we introduce two frameworks: LPAF and AFLP. The former enables to use the result of argumentation in AF for reasoning in LP, while the latter enables to use the result of reasoning in LP for arguing in AF. These frameworks are extended to bidirectional frameworks in which AF and LP can exchange information with each other. We also investigate their connection to several general KR frameworks from the literature.
Semantics for Conditional Literals via the SM Operator

Conditional literals are an expressive Answer Set Programming language construct supported by the solver clingo. Their semantics are currently defined by a translation to infinitary propositional logic, however, we develop an alternative characterization with the SM operator which does not rely on grounding. This allows us to reason about the behavior of a broad class of clingo programs/encodings containing conditional literals, without referring to a particular input/instance of an encoding. We formalize the intuition that conditional literals behave as nested implications, and prove the equivalence of our semantics to those implemented by clingo.
A Comparative Study of Three Neural-Symbolic Approaches to Inductive Logic Programming

An interesting feature that traditional approaches to inductive logic programming are missing is the ability to treat noisy and non-logical data. Neural-symbolic approaches to inductive logic programming have been recently proposed to combine the advantages of inductive logic programming, in terms of interpretability and generalization capability, with the characteristic capacity of deep learning to treat noisy and non-logical data. This paper concisely surveys and briefly compares three promising neural-symbolic approaches to inductive logic programming that have been proposed in the last five years. The considered approaches use Datalog dialects to represent background knowledge, and they are capable of producing reusable logical rules from noisy and non-logical data. Therefore, they provide an effective means to combine logical reasoning with state-of-the-art machine learning.
Reasoning About Actions with $$\mathcal EL$$ Ontologies and Temporal Answer Sets for DLTL

We propose an approach for reasoning about actions with domain descriptions including an $$\mathcal{E}\mathcal{L}^{\bot }$$ ontology in a temporal action theory. The action theory is based on a Dynamic Linear Time Temporal Logic, whose extensions are defined through temporal answer sets. The work provides conditions under which action consistency can be guaranteed with respect to an $$\mathcal{E}\mathcal{L}^{\bot }$$ ontology, by polynomially encoding an $$\mathcal{E}\mathcal{L}^{\bot }$$ knowledge base into a domain description of the temporal action theory.
ASP for Flexible Payroll Management

Payroll management is a critical business task that is subject to a large number of rules, which vary widely between companies, sectors and countries. Moreover, the rules are often complex and change regularly. Therefore, payroll management systems must be flexible in design. In this paper, we suggest an approach based on a flexible Answer Set Programming model, and an easy-to-read tabular representation based on the DMN standard. It allows HR consultants to represent complex rules without the need for a software engineer, and to ultimately design payroll systems for diverging scenarios. We show how the multi-shot solving capabilities of the clingo ASP system can be used to reach the performance that is necessary to handle real-world instances.
Epistemic Logic Programs: A Study of Some Properties

Epistemic Logic Programs (ELPs), extend Answer Set Programming (ASP) with epistemic operators. The semantics of such programs is provided in terms of world views, which are sets of belief sets. Different semantic approaches propose different characterizations of world views. Recent work has introduced semantic properties that should be met by any semantics for ELPs, like the Epistemic Splitting Property, that, if satisfied, allows to modularly compute world views in a bottom-up fashion, analogously to ‘traditional’ ASP. We analyze the possibility to change the perspective, shifting from a bottom-up to a top-down approach to splitting. Our new definition: (i) copes with concerns regarding unfoundedness of world views and subjective constraint monotonicity; (ii) is provably applicable to many of the existing semantics; (iii) operates similarly to “traditional” ASP; (iv) provably coincides with the bottom-up notion of splitting at least on the class of Epistemically Stratified Programs (which are, intuitively, those where the use of epistemic operators is stratified).
Towards Causality-Based Conflict Resolution in Answer Set Programs

Using answer set programming in real-world applications requires that the answer set program is correct and adequately represents knowledge. In this paper, we present strategies to resolve unintended contradictory statements resulting from modelling gaps and other flaws by modifying the program without manipulating the actual conflicting rules (inconsistency-causing rules with complementary head literals). We show how latent conflicts can be detected to prevent further conflicts during the resolution process or after subsequent modifications in the future. The presented approach is another step towards a general framework where professional experts who are not necessarily familiar with ASP can repair existing answer set programs and independently resolve conflicts resulting from contradictory statements in an informative way. In such a framework, conflict resolution strategies allow for generating possible solutions that consist of informative extensions and modifications of the program. In interaction with the professional expert, these solution options can then be used to obtain the solution that represents the underlying knowledge best.