Reviewerly/exHarmony huggingface dataset:
Climate Change Impact Assessment for Future Wind and Solar Energy Installations in India

Solar and wind assets are climate-dependent and changes in climate will result in variations in their generation and intermittency. Developers of solar and wind parks in India have observed changes in climate conditions and variability in solar irradiation and wind profiles at the seasonal and year-to-year timescales. Future climate change perturbations, including monsoon shifts, could lead to lower-than-predicted wind and solar energy production and affect the economics of solar and wind assets. Regional climate models (RCMs) are the basis of climate impact assessments and the most trusted source of information to extract knowledge about future trends in climate variables. However, RCM projections are tainted with variability and uncertainty about the future trends. For India as a case study, we use the RCMs generated by the Coordinated Regional Climate Downscaling Experiment West Asia project (CORDEX WAS) to calculate individual wind, radiation, and temperature trends at selected sites; estimate wind and solar PV energy time series; and embed them in portfolio methods to test the impact of combining wind and solar assets on the variability of the total production and the uncertainty about the predicted production. We include a comparison of CORDEX RCMs with the ERA5 reanalysis dataset and conclude that all available RCMs reasonably simulate the main annual and seasonality features of wind speed, surface solar radiation, and temperature in India. The analysis demonstrates that the uncertainty about the portfolio return can be reduced by optimizing the combination of wind and solar assets in a producer portfolio, thus mitigating the economic impact of climate change. We find that the reduction obtained with a mixed portfolio ranges from 33 to 50% compared to a wind only portfolio, and from 30 to 96% compared to a solar only portfolio.
Research on Enterprise Financial Accounting Information Security Model Based on Big Data

Powered by the rapid development of the information age, big data technology has had a great impact on China’s socialist economic development. Big data technology is integrated into all fields of nowadays society, especially in the accounting industry of enterprises. Accounting computerization quickly replaces the traditional manual bookkeeping, realizes the timeliness and accuracy of accounting information data processing, and improves the work efficiency and quality of accounting staff to a great extent, but at the same time, the security of financial accounting information is also very prominent. To tackle this problem, this paper proposes a hybrid encryption algorithm based on double chaotic system and improved AES encryption algorithm. The improved AES algorithm uses affine transformation pairs (A7 and 6F) to generate new S-boxes. The double four-dimensional hyperchaotic system is transformed from two three-dimensional chaotic systems, and then, the transformed hyperchaotic system is used to generate chaotic sequences, and a block encryption scheme is designed. On Hadoop big data platform, double hyperchaotic encryption scheme and improved AES algorithm are combined. Simulation test results show that this method can safely transmit enterprise financial accounting information data packets. With the increase of computing cluster nodes, its encryption transmission efficiency continues to improve. This scheme not only solves the problem of enterprise financial accounting data security encryption but also realizes the parallel transmission of encrypted data in the information age, forming a double guarantee of data transmission security and efficiency.
Automatic Modulation Classification with Low-Cost Attention Network for Impaired OFDM Signals

In this paper, we propose a deep learning (DL)-based method to automatically identify the modulations of orthogonal frequency-division multiplexing (OFDM) signals in wireless communication systems. In particular, a cost-efficient OFDM modulation classification convolutional neural network (COM-ConvNet) is principally designed with grouped convolutional layers to reduce computing complexity significantly. Remarkably, reconstructing the high-dimensional data array of OFDM signals allows our deep network to learn the underlying sample correlations within every symbol and among different symbols sufficiently. We leverage residual connection and attention connection with element-wise addition and element-wise multiplication layers in specific-designed processing blocks to enhance the pattern learning efficiency. For performance evaluation, we test the proposed method on a synthetic six-modulation OFDM signal dataset under impaired channel conditions and conduct diverse simulations, such as ablation study, parameter investigation, and complexity analysis. COM-ConvNet achieves cost efficiency (i.e., small network size and low computational cost) while maintaining an acceptable accuracy when compared with other DL models.
Harvesting the Aggregate Computing Power of Commodity Computers for Supercomputing Applications

Distributed supercomputing is becoming common in different companies and academia. Most of the parallel computing researchers focused on harnessing the power of commodity processors and even internet computers to aggregate their computation powers to solve computationally complex problems. Using flexible commodity cluster computers for supercomputing workloads over a dedicated supercomputer and expensive high-performance computing (HPC) infrastructure is cost-effective. Its scalable nature can make it better employed to the available organizational resources, which can benefit researchers who aim to conduct numerous repetitive calculations on small to large volumes of data to obtain valid results in a reasonable time. In this paper, we design and implement an HPC-based supercomputing facility from commodity computers at an organizational level to provide two separate implementations for cluster-based supercomputing using Hadoop and Spark-based HPC clusters, primarily for data-intensive jobs and Torque-based clusters for Multiple Instruction Multiple Data (MIMD) workloads. The performance of these clusters is measured through extensive experimentation. With the implementation of the message passing interface, the performance of the Spark and Torque clusters is increased by 16.6% for repetitive applications and by 73.68% for computation-intensive applications with a speedup of 1.79 and 2.47 respectively on the HPDA cluster. We conclude that the specific application or job could be chosen to run based on the computation parameters on the implemented clusters.
Forensics chain for evidence preservation system: An evidence preservation forensics framework for internet of things‐based smart city security using blockchain

Abstract In recent times, the new revolution of IoT facilitates communication and information sharing among people in different domains like a smart city. This revolution came with a risk of cyber‐attacks that target devices and shared data. The digital evidence resulting from the digital forensics process applied to IoT devices must be kept safe for later analysis. Preserving digital evidence on a centralized server raises the risk of a single point of failure. Evidence preserving on cloud servers raises the tampering risk with the evidence or even sharing them with malicious third parties. Therefore, this paper presents a novel framework called Forensics Chain for Evidence Preservation System for IoT ‐based smart city security. The proposed framework aims to integrate blockchain with digital forensics to overcome the problems faced by forensic investigators; single point of failure and/or evidence modifications and enhance the security of preserving digital evidence via applying blockchain. Applying blockchain guarantees the immutability and data integrity of the preserved evidence. Furthermore, preserving the digital evidence among the forensic participant nodes eliminates the possibility of the single‐point failure of a centralized storage server. The results provided a Proof of Concept for forensic evidence preservation based on blockchain and evaluated its performance.
Retail System Scenario Modeling Using Fuzzy Cognitive Maps

A retail business is a network of similar-format grocery stores with a sole proprietor and a well-established logistical infrastructure. The retail business is a stable market, with low growth, limited customer revenues, and intense competition. On the system level, the retail industry is a dynamic system that is challenging to represent due to uncertainty, nonlinearity, and imprecision. Due to the heterogeneous character of retail systems, direct scenario modeling is arduous. In this article, we propose a framework for retail system scenario planning that allows managers to analyze the effect of different quantitative and qualitative factors using fuzzy cognitive maps. Previously published fuzzy retail models were extended by adding external factors and combining expert knowledge with domain research results. We determined the most suitable composition of fuzzy operators for the retail system, highlighted the system’s most influential concepts, and how the system responds to changes in external factors. The proposed framework aims to support senior management in conducting flexible long-term planning of a company’s strategic development, and reach its desired business goals.
Mining Residues Characterization and Sentinel-2A Mapping for the Valorization and Efficient Resource Use by Multidisciplinary Strategy

Raw materials are essential for all sectors of the economy as well as for all innovative technologies. Currently, industry is largely dependent on imports and consumption of these materials, and in the future, following the global energy transition, this trend will drastically increase. For this reason, it is necessary to develop new strategies to meet the supply–demand of raw materials by strategic sectors and technologies. To this end, mining residues are turning into viable raw materials sources as they represent reliable access to valuable resource supply. This work aims to validate a methodology providing more in-depth quali-quantitative information about 100 samples of mining residues collected in the Joda West mine (India) by multidisciplinary strategy in order to valorize and promote efficient resource use. In this paper, physicochemical and mineralogical characterization and hyperspectral signatures have been integrated with satellite Sentinel-2A data. In particular, the X-ray powder diffraction (XRPD), X-ray fluorescence (XRF), and scanning electron microscopy with energy dispersive spectroscopy (SEM-EDS) analyses indicated the presence of mining residues samples rich in raw materials that were possible to spectrally distinguish in order to use them as input for the object-oriented classification of a satellite image. The satellite resulting map highlighted four different classes of mining residues with mean concentrations of around 36% for hematite, 23.5% for quartz, 9% for kaolinite and 5% for pyrolusite. These results represent the possibility of transforming an environmental problem (mining residues) into a resource potentially exploitable by industries knowing their composition and position in the study area. Moreover, by doing so, it will be possible to ensure sustainable consumption of the raw materials and to build resilient economies and societies, minimizing environmental degradation.
Efficiency of blended learning of calculus content during the Covid19 crisis

ABSTRACTABSTRACTIn this paper, we present the analysis of the students’ achievements in learning calculus in a dynamic software environment during the Covid19 crisis. Two groups of students, the experimental and the control one, were monitored. Blended learning was applied to the students in the experimental group, with the help of Microsoft Teams and dynamic software GeoGebra, in autumn 2020. All students in the control group learned in the classroom without using GeoGebra in 2019. The comparison between these two groups of first-year students, regarding their calculus test results, is described in this paper. It is interesting that the results of the experimental group were significantly better than the results of students in the control group, despite the fact that the students from the experimental group learned during the Covid19 crisis.KEYWORDS: Areacalculusconstructivismdefinite integralmathematics AcknowledgmentsThe authors express their gratitude to the referees, whose valuable remarks and comments immensely helped in creating the final version of the paper.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsSlađana MitrovićSlađana Mitrović is a PhD student at Department of Mathematics and Informatics, Faculty of Sciences, University of Novi Sad. She is a senior teaching assistant at University of East Sarajevo, Faculty of Education, Bijeljina. Her research primarily includes teaching methodology and mathematics education.Radoslav BožićRadoslav Božić is a mathematics teacher at Gymnasium “Svetozar Marković”, Novi Sad and research associate at Department of Mathematics and Informatics, Faculty of Sciences, University of Novi Sad.Đurđica TakačiĐurđica Takači is a Professor at Department of Mathematics and Informatics, Faculty of Sciences, University of Novi Sad. Her research includes mathematics education.
Development and evaluation of ontology for decision support in Polish crisis management system

The article presents a summary of research and development process of Domain Ontology for Crisis Management (DO4CM). The main goal of this work is to make decisions more effective within the framework of standard procedures implemented by government and local administration in response to emerging crisis and discontinuities. Presented ontology has been validated and verified during exercises carried out by actual crisis management structures and in co-ordination with non-governmental organisations supporting the growth of Polish state strategic resilience.
L-BGNN: Layerwise Trained Bipartite Graph Neural Networks

Learning low-dimensional representations of bipartite graphs enables e-commerce applications, such as recommendation, classification, and link prediction. A layerwise-trained bipartite graph neural network (L-BGNN) embedding method, which is unsupervised, efficient, and scalable, is proposed in this work. To aggregate the information across and within two partitions of a bipartite graph, a customized interdomain message passing (IDMP) operation and an intradomain alignment (IDA) operation are adopted by the proposed L-BGNN method. Furthermore, we develop a layerwise training algorithm for L-BGNN to capture the multihop relationship of large bipartite networks and improve training efficiency. We conduct extensive experiments on several datasets and downstream tasks of various scales to demonstrate the effectiveness and efficiency of the L-BGNN method as compared with state-of-the-art methods. Our codes are publicly available at https://github.com/TianXieUSC/L-BGNN.
Implementation of Potential Leukemia Detection using Recurrent Neural Networks with Blood Cell Counting

A total of 412,000 persons are expected to be diagnosed with leukemia worldwide, with acute lymphoblastic leukemia accounting for around 12% of all cases. Thus, the early detection of leukemia can save millions of populations. This article mainly focusing on blood cell counting and detection of Leukemia using deep learning mechanisms. Initially, the images are preprocessed using median filters and segmented using RGB-Otsu's approach. Then, the features are extracted using scale-invariant feature transform (SIFT) and these features are applied to recurrent neural network (RNN) for classification. Finally, the proposed method resulted in superior performance as compared to the state of art machine learning approaches.
Development and application of a field knowledge graph and search engine for pavement engineering

Integrated, timely data about pavement structures, materials and performance information are crucial for the continuous improvement and optimization of pavement design by the engineering research community. However, at present, pavement structures, materials and performance information in China are relatively isolated and cannot be integrated and managed. This results in a waste of a large amount of effective information. One of the significant development trends of pavement engineering is to collect, analyze, and manage the knowledge assets of pavement information to realize intelligent decision-making. To address these challenges, a knowledge graph (KG) is adopted, which is a novel and effective knowledge management technology and provides an ideal technical method to realize the integration of information in pavement engineering. First, a neural network model is used based on the principle of deep learning to obtain knowledge. On this basis, the relationship between knowledge is built from siloed databases, data in textual format and networks, and the knowledge base. Second, KG-Pavement is presented, which is a flexible framework that can integrate and ingest heterogeneous pavement engineering data to generate knowledge graphs. Furthermore, the index and unique constraints on attributes for knowledge entities are proposed in KG-Pavement, which can improve the efficiency of internal retrieval in the system. Finally, a pavement information search engine based on a knowledge graph is constructed to realize information interaction and target information matching between a webpage server and graph database. This is the first successful application of knowledge graphs in pavement engineering. This will greatly promote knowledge integration and intelligent decision-making in the domain of pavement engineering.
Learning Soft Skills through Distributed Software Development

The software industry needs universities to train developers to have besides the technical skills, also strong soft skills to collaborate in globally distributed software development projects. To develop these soft skills, we organized a distributed online software development project course, during which student Scrum teams of 5–8 members from five Belarusian universities worked in industrial projects for Danish customers. The course aimed to 1) teach students the Scrum framework and soft skills, such as teamwork and communication with international customers; and 2) to give Belarusian teachers ideas for organizing similar courses in the future. Based on 20 post-course semi-structured interviews with students and stakeholders, and the analysis of 24 student learning diaries we studied the learning outcomes and challenges related to soft skills. The main reported learning outcomes were: communication, methodical use of Scrum, problem solving, organizational/planning skills, teamwork, interpersonal skills, and time management.
IoDM: A Study on a IoT-Based Organizational Deception Modeling with Adaptive General-Sum Game Competition

Moving target defense (MTD) and decoy strategies, measures of active defense, were introduced to secure both the proactive security and reactive adaptability of internet-of-things (IoT) networks that have been explosively applied to various industries without any strong security measures and to mitigate the side effects of threats. However, the existing MTD and decoy strategies are limited to avoiding the attacker’s reconnaissance and initial intrusion attempts through simple structural mutations or inducing the attackers to a static trap based on the deceptive path and lack approaches to adaptively optimize IoT in consideration of the unique characteristic information by the domain of IoT. Game theory-based and decoy strategies are other options; however, they do not consider the dynamicity and uncertainty of the decision-making stages by the organizational agent related to the IoT domains. Therefore, in this paper, we present a type of organizational deception modeling, namely IoT-based organizational deception modeling (IoDM), which considers both the dynamic topologies and organizational business fingerprints customized in the IoT domain and operational purpose. For this model, we considered the practical scalability of the existing IoT-enabled MTD and decoy concepts and formulated the partially incomplete deceptive decision-making modeling for the cyber-attack and defense competition for IoT in real-time based on the general-sum game. According to our experimental results, the efficiency of the deceptive defense of the IoT defender could be improved by 70% on average while deriving the optimal defense cost compared to the increased defense performance. The findings of this study will improve the deception performances of MTD and decoy strategies by IoT scenarios related to various operational domains such as smart home networks, industrial networks, and medical networks. To the best of our knowledge, this study has employed social-engineering IoT knowledge and general-sum game theory for the first time.
Estimating the impact of information technology on economic growth in south Asian countries: The silver lining of education

In this paper, the impact of Information and Communication Technologies (ICTs) on economic growth in South Asian countries is investigated during 1990–2014. For this purpose, the newly developed panel data estimation method Dynamic Seemingly Unrelated Regression (DSUR) is applied. According to the results, the impact of ICTs on economic growth remains positive in South Asian economies. In addition, education stimulates economic growth; however, foreign direct investment does not contribute to economic growth. The newly acquired result would guide policymakers and government officials to focus on the advancement of ICT for maintaining the sustainable economic growth. The findings imply that South Asian countries should raise the ICT level with proper technology education level to achieve the desired economic growth.
Revelations from an Agile and DevSecOps Transformation in a Large Organization: An Experiential Case Study

This paper presents the lessons learned, and the observations and insights gained, from observing a software development effort for a large, well-funded, and highly regulated program that adopted Agile and DevSecOps principles during a 12-month period of iterative software development. The program was originally set up to use the waterfall software development approach with a traditional earned value (EV) scheme. It completed several iterations of development using this structure. The program then shifted to using a combination of Agile and DevSecOps. In this paper, we describe challenges encountered during this transition that inhibited realization of some of the benefits associated with Agile and DevSecOps. Largely, these challenges were a result of poor planning, engineering, and communication. We present this advisory account to others undertaking similar DevSecOps and Agile transitions, particularly in large organizations, so that they may better strategize and prepare methods to diminish similar shortcomings and increase the odds of a successful transition.