Reviewerly/exHarmony huggingface dataset:
Variability index of solar resource based on data from surface and satellite

Characterizing solar resource variability is paramount for its thermal and photovoltaic applications. Cloudiness is the main parameter that modulates both the intensity and frequency of variations in solar irradiance on the ground surface. This work introduces an objective index to quantify the temporal variability of the incoming solar irradiance on the surface, defined as Cloudless Percentage Days (CPD). This index accounts for the number of days classified as clear and stable (i.e., high clearness coefficient and decreased frequency of ramps of incoming solar irradiance). Two methods were developed to quantify CPD, the first based on ground measurements and the other based on satellite data. This work compares the results achieved with the two methods to understand their consistency. Monthly and annual maps were drawn for the Brazilian territory characterizing solar resource variability. • New index to measure solar resource quality based on its variability and availability. • Nine years of irradiance data collected at stations in the tropical region. • Spatial variability based on GOES13 visible channel imagery between 2008 and 2016. • Variability mapping for the entire Brazilian territory, applicable to other regions.
UAV-enabled edge computing and blockchain based secure charging station selection for energy trading in V2G environment

Vehicle-to-Grid (V2G) networks have emerged as a promising solution in the modern electric power transmission network. By leveraging the bidirectional energy trading capabilities of V2G, demand-supply mismatch is managed in wide range of applications. However, performing energy trading decisions at cloud centers leads to increased delay and overhead. The demand to process the 3V's (variety, volume, and velocity) of data generated by V2G with low latency necessitates the usage of edge computing services. Although with the growth of edge computing resources, the trust issue of collaboration also grows among different stakeholders and service providers. Therefore, to deal with the aforementioned issues, this paper presents blockchain and UAV-enabled edge computing based energy trading services for the V2G environment. The decision of selecting a charging station (CS) by an electric vehicle (EV) is processed close to the EV movement via edge nodes. To secure and validate energy trading transactions and automation of the whole system, the blockchain technology is used in the proposal. The result section depicts that the proposed scheme has minimal computational and communicational overhead along with reduced latency in comparison to the existing schemes.
Difficult Novel Class Detection in Semisupervised Streaming Data

Streaming data mining can be applied in many practical applications, such as social media, market analysis, and sensor networks. Most previous efforts assume that all training instances except for the novel class have been completely labeled for novel class detection in streaming data. However, a more realistic situation is that only a few instances in the data stream are labeled. In addition, most existing algorithms are potentially dependent on the strong cohesion between known classes or the greater separation between novel class and known classes in the feature space. Unfortunately, this potential dependence is usually not an inherent characteristic of streaming data. Therefore, to classify data streams and detect novel classes, the proposed algorithm should satisfy: 1) it can handle any degree of separation between novel class and known classes (both easy and difficult novel class detection) and 2) it can use limited labeled instances to build algorithm models. In this article, we tackle these issues by a new framework called semisupervised streaming learning for difficult novel class detection (SSLDN), which consists of three major components: an effective novel class detector based on random trees, a classifier by using the information of nearest neighbors, and an efficient updating process. Empirical studies on several datasets validate that SSLDN can accurately handle different degrees of separation between the novel and known classes in semisupervised streaming data.
Using Three-Dimensional Logistic Equations and Glowworm Swarm Optimization Algorithm to Generate S-Box

A major component of symmetric cryptosystems is the substitution box (S-box), which is primarily utilized in existing cryptographic ciphers to ensure secure data sanctuaries.The cryptographic strength of an S-box used in the encryption directly correlates to the amount of data protection that a cipher offers.This work developed a suitable configuration (8x8) S-box using metaheuristic approaches based on the Glowworm Swarm Optimization (GSO) algorithm and three-dimensional (3D) logistic equations to construct pseudo-random number generators.Chaotic maps boost the initial population of the GSO, making it a suitable place to start.The nonlinearity score of the S-box served as the GSO's objective function.Operations for exploration and exploitation are balanced using the GSO.In comparison to various references, the proposed S-box satisfied all of the S-security box's requirements, including the strict avalanche criterion (SAC), bijection, nonlinearity, output bits independence criteria (BIC), and histogram.The proposed method's nonlinearity property was 107.00.It is regarded as a successful and effective result.BIC and SAC averages were 103.500 and 0.499, respectively.Compared to a simple histogram, the encryption histogram was more uniform.As a result, it has been demonstrated that the suggested S-box is robust and resistant to cryptographic assaults.
A Mel-weighted Spectrogram Feature Extraction for Improved Speaker Recognition System

Speaker recognition system is intended to recognize a person's identity.This task can be done by knowing the feature of the contained voice signals.The feature can be extracted using a feature extraction technique.One of the most popular feature extraction techniques is Mel-Frequency Cepstral Coefficient (MFCC).Until this time, the MFCC is still a challenging technique to be developed.In this work, a proposed technique is developed based on the MFCC.The original MFCC is modified in the process of filter bank and by adding spectrogram.The target of this work is to obtain a better recognition accuracy than the original MFCC.This target can be done by applying a technique called Mel-weighted spectrogram.The output of the proposed technique are spectrogram images which contain the feature of the voices.The spectrogram result is then classified using dissimilarity space based on Euclidean distance to identify the person's identity.For the dataset, this work uses 315 recorded voice signals, consisting of 3 speakers, each pronouncing five words repeatedly 21 times on seven different days.The performance of this system is evaluated by comparing the percentage of accuracy among the proposed technique, the original MFCC, and two other MFCC-based techniques.In this work, the proposed technique is better than the three other techniques with an accuracy up to 88.57%.From these results, the Mel-weighted spectrogram can be considered as a recommendation for obtaining a higher recognition rate in speaker recognition system.
Countering cybercrimes as the strategy of enhancing sustainable digital economy in Nigeria

Purpose This study aims to examine the threats posed by cybercrimes toward the quest for achieving a reliable digital economy and to identify practical strategies for countering the crime. Design/methodology/approach A qualitative phenomenological paradigm was used as the methodology. Both primary and secondary sources were consulted for data collection. A thematic analytical interpretation was used for data analysis. Findings This study discovered that Nigeria is a leverage environment for using digital economy as a means of diversifying the economy owing to population bulge, and an increase patronage of internet and digital space but the threats of cyberfraudsters is ostracising a substantial number from using it which is decreasing the performance of the digital economy. Research limitations/implications The research is limited by the scarcity of resources to widen the horizon of the study particularly the fieldwork and the rampant incidences of cybercrime in Nigeria which poses a difficult task for studying the phenomenon. In addition, this study is constrained by the confidentiality in releasing data owing to the sensitivity of the subject of study and its relationship to national security. Practical implications This study presents some suggestions that are plausible in countering the crimes and enhancing the digital economy in Nigeria through effective surveillance, massive information and communication technology (ICT) awareness creation for users and severe penitent sanctions for cybercriminals will help in securing digital spaces. Social implications This study has social implications including the suggestion for simplifying digital transactions that will save time, increases economic activities and passive multiple stream income for Nigerians and ease of doing business with less risk of robbery and other cumbersome tasks. Originality/value This study has originality value because after the survey of the existing literature, a field work is conducted to have the views of experts on the subject of study which adds value to the subject and originality of the findings.
Researchers and their data: A study based on the use of the word<i>data</i>in scholarly articles

Abstract Data is one of the most used terms in scientific vocabulary. This article focuses on the relationship between data and research by analyzing the contexts of occurrence of the word data in a corpus of 72,471 research articles (1980–2012) from two distinct fields (Social sciences, Physical sciences). The aim is to shed light on the issues raised by research on data, namely the difficulty of defining what is considered as data, the transformations that data undergo during the research process, and how they gain value for researchers who hold them. Relying on the distribution of occurrences throughout the texts and over time, it demonstrates that the word data mostly occurs at the beginning and end of research articles. Adjectives and verbs accompanying the noun data turn out to be even more important than data itself in specifying data. The increase in the use of possessive pronouns at the end of the articles reveals that authors tend to claim ownership of their data at the very end of the research process. Our research demonstrates that even if data-handling operations are increasingly frequent, they are still described with imprecise verbs that do not reflect the complexity of these transformations.
A video anomaly detection method based on percentile loss training and attention mechanism

Currently, the mainstream methods for video anomaly detection include frame reconstruction and video stream prediction. These methods are always confronted with the problem of over-generalization, which makes the network reconstruct or predict both normal and anomalous modes well and makes it difficult to distinguish the normal and anomalous modes. In this paper, we propose a video anomaly detection model based on the U-Net architecture, in which percentile loss (PL) training is introduced to weaken the generalization of the network by diminishing the influence of complex regular modes that tend to be anomalous on parameter update, and in turn weaken the model’s ability to predict anomalous modes. Moreover, based on the fact that most of the objects in video surveillance are stationary and abnormal behaviors tend to occur in moving objects, a variance attention module is used after the encoder module to make the network focus on moving objects that tend to contain abnormal modes. The proposed framework has been evaluated on publicly available real-world anomaly detection datasets including CUHK Avenue, UCSD and ShanghaiTech. The experiments show that the proposed algorithm improves the detection performance compared with the state-of-the-art algorithms.
Digital Transformation of Education in the Context of Informatization of Education and Society Against the Background of Russian Armed Aggression: Current Problems and Vectors of Development

Digital transformation is the basis of Ukraine’s war tactics. The aim is to analyze the practice and context of the evolution of informatization of education, the contribution and focus on educational technologies of distance learning in the languages of war. The method of sociological research of open primary collection of information in Kamyanets-Podilsky Ivan Ohienko National University, Volodymyr Vynnychenko Central Ukrainian State Pedagogical University, and the National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, described the problems of organization, forms, and tendencies of the educational process. The novelty - is the allocation of operational algorithms in the field of distance learning technologies, which allow teaching and supporting the participants of the educational process under conditions of armed Russian aggression. Practical significance - the analysis of the dynamics of development of informatization of society in modern conditions; in the search for digital transformations and updating of education during the war; in the description of modern practices and vectors of development. The results revealed: that educational trends in zones or without hostilities, indicators of technical development in conditions of war, and levels and principles of development of public informatization. In war conditions the educational institutions technically coped with the emergency situation.
NNNPE: non-neighbourhood and neighbourhood preserving embedding

Manifold learning is an important class of methods for nonlinear dimensionality reduction. Among them, the LLE optimisation goal is to maintain the relationship between local neighbourhoods in the original embedding manifold to reduce dimensionality, and NPE is a linear approximation to LLE. However, these two algorithms only consider maintaining the neighbour relationship of samples in low-dimensional space and ignore the global features between non-neighbour samples, such as the face shooting angle. Therefore, in order to simultaneously consider the nearest neighbour structure and global features of samples in nonlinear dimensionality reduction, it can be linearly calculated. This work provides a novel linear dimensionality reduction approach named non-neighbour and neighbour preserving embedding (NNNPE). First, we rewrite the objective function of the algorithm LLE based on the principle of our novel algorithm. Second, we introduce the linear mapping to the objective function. Finally, the mapping matrix is calculated by the method of the fast learning Mahalanobis metric. The experimental results show that the method proposed in this paper is effective.
Security research on practical measurement-device-independent quantum key distribution

The ideal measurement-device-independent quantum key distribution (MDI-QKD), which is immune to all detector side-channel attacks, is built on the Bell state measurement of two single-photon states. However, in practical MDI-QKD where phase-randomized weak coherent pulses (PR-WCPs) are used, the mismatch between states preparation and measurement in X basis leads to the bit error rate of more than 25%, which poses a challenge to the security of QKD. In this paper, we provide a security analysis of practical MDI-QKD based on polarization coding. We analyze the Hong-Ou-Mandel interference of PR-WCPs and Poisson-distributed photon number states (PNs), and find that these two sources are equivalent in MDI-QKD. The security analysis based on entanglement distillation is carried out on PNs, and the measurement results in Z basis and X basis are averaged to overcome the noncorrelation of the error rates in these two bases. Compared with GLLP scheme, a tighter key rate is obtained in this work, and the key rate deviates from the linear key rate bound in a short distance due to the finite proportion of multiphoton terms.
How technologies can be used to reduce poverty and promote sustainable development in Haiti: the case of the PIGraN Project

One of the biggest challenges facing many developing countries consists of improving the quality of life and well-being of their citizens while respecting the principles of sustainable development. Several development aid programs have been implemented in these countries over the past decades, requiring tremendous human and financial investments. However, the results are often disappointing. In the case of Haiti, although several billion dollars have been allocated to this country in the form of development aid over the past thirty years, poverty lingers and citizens' daily life has not improved. Testing a new approach to tackle this problem, the PIGraN project was designed to focus on an innovative use of information technologies, learning technologies and energy technologies, to prototype sustainable development and well-being in the north of Haiti. The chapter reviews this case study aiming to show the first results obtained combining sustainable human development and poverty reduction.
Development and Evaluation of a Software Product Line for M-Learning Applications

The popularity of mobile devices in all social classes has motivated the development of mobile learning (m-learning) applications. The existing applications, even having many benefits and facilities in relation to the teaching-learning process, still presents problems and challenges, es- pecially regarding the development, reuse and architectural standardization. Particularly, there is a growing adoption of the Software Product Line (SPL) concept, in view of research that investigates these gaps. This paradigm enables organizations to explore the similarities and variabilities of their products, increasing the reuse of artifacts and, consequently, reducing costs and development time. In this context, we discuss how systematic reuse can improve the development of solutions in the m-learning domain. Therefore, this work presents the design, development and experimental evaluation of M-SPLearning, an SPL created to enable the systematic production of m-learning applications. Specifically, the conception of M-SPLearning covers from the initial study for an effective domain analysis to the implementation and evaluation of its functional version. In this regard, the products have been experimentally evaluated by industry software developers, pro- viding statistical evidence that the use of our SPL can speed up the time-to-market of m-learning applications, in addition to reducing their respective number of faults.
Directional Statistics-Based Deep Metric Learning for Pedestrian Tracking and Re-Identification

Multiple Object Tracking (MOT) is the problem that involves following the trajectory of multiple objects in a sequence, generally a video. Pedestrians are among the most interesting subjects to track and recognize for many purposes such as surveillance, and safety. In recent years, Unmanned Aerial Vehicles (UAV’s) have been viewed as a viable option for monitoring public areas, as they provide a low-cost method of data collection while covering large and difficult-to-reach areas. In this paper, we present an online pedestrian tracking and re-identification framework based on learning a compact directional statistic distribution (von-Mises-Fisher distribution) for each person ID using a deep convolutional neural network. The distribution characteristics are trained to be invariant to clothes appearances and to transformations including rotation, translation, and background changes. Learning a vMF for each ID helps simultaneously in measuring the similarity between object instances and re-identifying the pedestrian’s ID. We experimentally validated our framework on standard publicly available dataset, which we used as a case study.
Extractive text-image summarization with relation-enhanced graph attention network

Multi-modal summarization with multi-modal output (MSMO) aims to generate multi-modal summaries for a multi-modal document to improve readability of summaries by making use of information of different modalities. Most existing Seq2Seq-based MSMO models cannot well capture multi-modal relations which are significant for generating high-quality multi-modal summaries. To address this issue, this paper proposes a relation-enhanced graph attention network for extractive text-image summarization (ReGAT-Summ) to capture inter-modal and intra-modal relations in the multi-modal document. Firstly, a multi-modal graph is constructed from the document. Then, node representations are calculated by proposed graph neural network. Finally, a sentence-image selector is trained to select salient sentences and images, which are further aligned by training. To our knowledge, we are the first to explore the graph-based model for MSMO. Experiments on two news datasets E-DailyMail and NYTime800k demonstrate that ReGAT-Summ achieves the state-of-the-art performance in terms of automatic metrics and human evaluations.
Checking equivalence in a non-strict language

Program equivalence checking is the task of confirming that two programs have the same behavior on corresponding inputs. We develop a calculus based on symbolic execution and coinduction to check the equivalence of programs in a non-strict functional language. Additionally, we show that our calculus can be used to derive counterexamples for pairs of inequivalent programs, including counterexamples that arise from non-termination. We describe a fully automated approach for finding both equivalence proofs and counterexamples. Our implementation, Nebula, proves equivalences of programs written in Haskell. We demonstrate Nebula's practical effectiveness at both proving equivalence and producing counterexamples automatically by applying Nebula to existing benchmark properties.
Research on Papua, a digital tool with artificial intelligence in favor of learning and linguistic attitudes towards the learning of the English language in students of Spanish language as L1

This study examines learners' perceptions of the linguistic and learning potential of an AI-based English language learning app called Papua. The study took a quasi-experimental approach in which 128 students of the degree in Primary Education at the University of Huelva, Spain, gained experience of using the app over a six-week period, and then answered a questionnaire. This was of a quantitative design, and included the following components: attitudes towards learning English; attitudes towards learning oral and written skills, and towards opportunities for interacting in English; and attitudes towards the motivation provided by the app. These attitudes were contrasted according to the variables memory and self-evaluation. The results of the study showed that oral skills were perceived as the most improved skill. Participants also foregrounded the enhancement of memorization of vocabulary, and positively evaluated the self-evaluation feature included in the app.