Miracle-dz/UniFAR-FTUs huggingface dataset:
Random Effects Models with Deep Neural Network Basis Functions: Methodology and Computation

Deep neural networks (DNNs) are a powerful tool for functional approximation. We describe flexible versions of generalized linear and generalized linear mixed models incorporating basis functions formed by a deep neural network. The consideration of neural networks with random effects seems little used in the literature, perhaps because of the computational challenges of incorporating subject specific parameters into already complex models. Efficient computational methods for Bayesian inference are developed based on Gaussian variational approximation methods. A parsimonious but flexible factor parametrization of the covariance matrix is used in the Gaussian variational approximation. We implement natural gradient methods for the optimization, exploiting the factor structure of the variational covariance matrix to perform fast matrix vector multiplications in iterative conjugate gradient linear solvers in natural gradient computations. The method can be implemented in high dimensions, and the use of the natural gradient allows faster and more stable convergence of the variational algorithm. In the case of random effects, we compute unbiased estimates of the gradient of the lower bound in the model with the random effects integrated out by making use of Fisher's identity. The proposed methods are illustrated in several examples for DNN random effects models and high-dimensional logistic regression with sparse signal shrinkage priors.
Tendências de pesquisa na Educação Matemática no Paraná: uma análise a partir das linhas de pesquisa dos cursos de mestrado da Área 46

O presente trabalho tem como objetivo identificar a presença da Educação Matemática nos cursos de mestrado da Área 46 do Paraná, a partir da análise das linhas de pesquisa. A pesquisa é de natureza qualitativa, do tipo exploratória e documental. Da análise dos coletados da Plataforma Sucupira e das páginas dos programas de Pós-graduação, emergiram 7 categorias de temáticas que compõem as linhas de pesquisa dos cursos de mestrado da Área 46, no que se refere ao foco desta investigação. Em ordem decrescente de frequência as categorias são: Ensino e/ou Aprendizagem; Tecnologia, Recursos e Mídias; Formação de professores; História, Filosofia e Sociologia; Cultura, Artes, Diversidade e Educação não Formal; Conhecimento Científico e Sociedade. As temáticas que compõem as três primeiras categorias estão presentes, tanto nos mestrados na modalidade acadêmica quanto profissional, enquanto as demais estão circunscritas aos cursos de mestrado acadêmico.
Is computer field mature enough to be used as a tool in psychology?

The last two decades have shown an increasing number of studies involving “virtual psychotherapists” and robot caregivers of humans with cognitive, social, behavioral deficits or some other type of disorder. A characteristic of these studies is the need to combine actors of the computer, psychology and psychiatry areas. To establish the needs and contributions of these actors we conducted searches in two different databases: PsycoInfo of the American Psychological Association (APA) and Web of Science of the Clarivate Analytics. If, on the one hand, this analysis reveals the fundamental contribution of psychology in the development of applications to meet the demand for virtual agents and social robots, on the other hand, the analysis also identified the contributions and factors that still need to be addressed to enable the widespread use of these agents as a support tool in psychology and psychiatry clinics.
An average case analysis of the minimum spanning tree heuristic for the power assignment problem

We present an average case analysis of the minimum spanning tree heuristic for the power assignment problem. The worst‐case approximation ratio of this heuristic is 2. We show that in Euclidean d‐dimensional space, when the vertex set consists of a set of i.i. d. uniform random independent, identically distributed random variables in [0,1]d, and the distance power gradient equals the dimension d, the minimum spanning tree‐based power assignment converges completely to a constant depending only on d.
Mapping movie genre evolution (1994 – 2019) using the role of cultural and temporal shifts: a thematic analysis

Background: The gratification each person seeks through a movie is different. Sometimes a person would seek information through movies or use them for educational purposes, and some might watch movies to escape into a world of fantasy or humour. Keeping in mind the diverse and ever-changing needs of the audience, the primary objective of this study is to observe the evolution of movie genre and content preference in India, which is one of the largest and culturally intriguing movies producing nation across the globe. Methods: To attain the objective, the researchers have thematically analysed the top five revenue grossing movie genres over a span of 26 years (1994 to 2019) in Bollywood. More than 100 storylines have been analysed to develop genre trend graphs and the results indicate a sharp decline in the popularity of romantic and family dramas, whereas comedy and action movies have witnessed an overall growth with romantic comedies (romcoms), being the most stable amongst the top five. Results: Several societal factors like the changing family structure, education level, access to dating applications and even terrorism have been considered to elucidate the evolving psyche of the audience. The theoretical understanding of the result is derived from the uses and gratification theory, cross culture communication theory, and Bandura’s social cognitive theory. Conclusions: This study would provide film-makers with a guide to understanding the changing movie genre preferences in India, which in turn would help them to produce economically profitable movies in future.
Federated-Based Deep Reinforcement Learning (Fed-DRL) for Energy Management in a Distributive Wireless Network

Studies on developing future-generation wireless systems are expected to support increased infrastructure development and device subscriptions with densely deployed base stations (BSs). Economically, decreasing BS energy consumption levels and achieving "greenness" remain key factors for the giant industry. Some research works have proposed deep reinforcement techniques to solve energy management (EM) issues in cellular networks. However, these techniques are inefficient in a distributive network environment and expose the devices to privacy issues. Federated learning (FL) is proven to enforce device privacy and train models distributively. Thus, this work proposes an autonomous switching mode framework for BSs based on federated-deep reinforcement learning to address the aforementioned challenges encountered by prior studies. Specifically, we deploy multiple deep reinforcement learning (mDRL) agents to influence the decision of the BS for energy management (EM). On the other hand, to make deep reinforcement learning (DRL)-based decisions feasible and satisfy D-QoS, we train the DRL-agents distributively by employing the federated learning (FL) concept. The results show the effectiveness of our proposed framework under distributed network scenarios compared with other benchmark algorithms.
UNDERSTANDING THE QURAN HOLISTICALLY: INTERDISCIPLINARY STUDY OF THE LANGUAGE AND LINGUISTICS OF THE QURAN

This study aims to analyze the impact of the language used in the Quran on Islamic discourse, using qualitative research methods and content analysis of Quranic verses and scholarly literature. The study finds that the Quranic language's unique literary style and linguistic features contribute to its authority and persuasiveness, shaping Islamic discourse over time. Rhetorical devices, such as repetition and parallelism, add to the text's effectiveness. The study highlights the importance of understanding the Quranic language and its impact on Islamic theology, jurisprudence, and ethics, emphasizing the need for interdisciplinary research. By analyzing the linguistic and literary features of the Quran, scholars can gain a deeper understanding of the text's meaning and message and appreciate its continued relevance in contemporary times. This study contributes to the broader understanding of the role of language in shaping religious discourse and interpretation.
The Influence of Product Attributes and Print Advertisements on Consumers' Purchase Intention under Different Involvement Degrees: an Empirical Evidence of Department Stores in Wuhan City

: This paper first studies the impact of the combination of two product attributes (functional attributes, hedonic attributes) and two advertising statements (imperative statements/non-imperative statements) on consumers’ purchase intention from the perspective of enterprises, and obtains the optimal matching combination by comparing the differences in the impact of different combinations on purchase intention. Further, from the perspective of consumers, this paper introduces the degree of consumer involvement as a moderating variable to explore its influence on the relationship among product attributes, advertising statements and consumer purchase intention, and deeply analyzes the changes in the influence of product attributes and advertising statements on consumer purchase intention under the regulation of the degree of involvement. At the same time, this paper explores the factors affecting consumers’ purchase intention from the perspective of enterprises and consumers, which is more consistent with the current market situation and can provide suggestions and guidance for enterprises to make marketing decisions. In terms of research design, this paper used the method of describing the experiment, and used inter-group analysis.
Observer‐based robust control for N‐coupled wave equations

In this article, we consider robust output regulation for an MIMO wave system with disturbance and reference produced from an exosystem. The approach we adopt is the observer‐ based approach from partial differential equation (PDE) perspective. The main differences from our previous works are two points: (a) the eigenvalues of the exosystem are not necessarily required to be algebraically simple; (b) tracking error feedback dynamic controls are designed directly from the uncertain PDE itself, not a nominal system where disturbance and reference are specially chosen so that the nominal waves and exosystem are detectable. To this purpose, we introduce transformations to transfer the original system into a disturbance free system, and at the same time, each reference is transformed into a linear combination of the exosystem. An observer is designed to estimate the states of the transformed disturbance free system and the unknown linear combinations of the exosystem. The feedforward controls are designed where the input disturbances are then written based on the linear combinations of the exosystem. A set of observer‐based feedback controls is then designed by replacing the state and disturbance with their estimates from the observer. The robustness of the closed‐loop system is discussed by analyzing the stability of the transformed tracking error system and the observer error system. The numerical simulations are presented to validate visually the effectiveness of the proposed tracking error feedback control.
The Effect of Symmetry on the De-Anonymizability of Social Networks

Social network de-anonymization, which refers to re-identifying users by mapping an anonymized network to a correlated real-name network, is an important problem in network science that has received intensive study. Although different de-anonymization algorithms have been proposed, the performance limit of any algorithm on a de-anonymization problem remains less explored. In this paper, we investigate the Optimal De-Anonymization Yield (ODAY), i.e., the maximum extent to which the network can be de-anonymized, of a given de-anonymization problem. Specifically, due to the uncertainty of the true mapping, we define ODAY as the maximum expected number of correctly mapped nodes, and propose general approaches for its quantification in general networks. Therefore, ODAY can be viewed as a quantitative and non-asymptotic concretization of the concept of network de-anonymizability, which is mostly investigated in an asymptotic manner in prior art. Inspired by the effect of network symmetry on the de-anonymizability, we show that for a graph pair with arbitrary topologies, ODAY equals the maximum diagonal sum of a matching probability matrix generated from graph homomorphisms. Due to the exponential complexity of enumerating all the possible homomorphisms, we further obtain an upper bound of ODAY by counting the orbits of each of the two graphs, which significantly reduces the computational cost. Two case studies apply our general findings on symmetry and ODAY to specific network models, and a sampling-based algorithm framework is proposed for ODAY calculation in practice. Extensive experiments are performed to validate our findings. To the best of our knowledge, this work is the first effort that quantifies the de-anonymizability of general networks in a non-asymptotic manner from the perspective of network symmetry, and thus sheds light on privacy enhancement for social network design.
Innovative methods for extraction of essential oils from medicinal plants

Essential oils are concentrated liquids of intricate combinations of volatile substances, extracted from various plant parts. Several bioactive substances with antibacterial and antioxidant activities are abundant in essential oils. Some essential oils have also been employed in medicine. Due to the risk associated with employing synthetic preservatives, the use of essential oils as natural additives for extending the shelf life of food products has also drawn considerable attention. They are used in the pharmaceutical, cosmetic, and food industries for their functional properties. There are various methods for extraction, but both the quality and the percentage yield of essential oil never remain the same. So, innovative and non-conventional techniques of essential oils extraction from medicinal plants were evoled to get quantitative and qualitative yield. In the present article, we searched and reviewed innovative techniques used for the extraction of essential oils from medicinal and aromatic plants through electronic searches of PubMed, Medline, Wiley, Scopus, and Google Scholar. For the extraction of essential oils, several innovative/non-conventional techniques have been reported in literature. Extraction of essential oil by using innovative techniques retards the risk of losing the essential components of plants, maintains the quality, reduces chemical risk, extraction time, acts eco-friendly, and increases the percentage yield of the essential oils. This paper presents the success story of innovative extraction methods of essential oils in accordance with sustainable development and environmental protection.
Measurements of high energy cosmic rays above 10 PeV with KASCADE-Grande

In the present work, different aspects of the measurement and reconstruction of extensive air showers generated by cosmic rays in the energy range between 10 16 eV and 10 18 eV are investigated. KASCADE-Grande detects charged particles of extensive air showers at ground level. From the energy deposits and the arrival times of the particles in the detector stations, the main parameters of the extensive air showers are reconstructed: the impact point, the direction of the shower axis, the total number of electrons (N e ), and the total number of muons (Nμ) of the shower at observation level. The nurnbers of electrons and muons are related to the mass and energy of the primary particle and are the basis for further analysis. The shape of the electron number spectrum reflects the shape of the cosmic ray energy spectrum. Knowing the reconstruction accuracies, the N e spectrum of the KASCADE- Grande data is studied in order to identify possible spectral features. It is found, that observed structures are consistent with the detector resolution and the reconstruction uncertainties. On the basis of the correlation between the total number of muons in the showers and the total number of charged particles, a composition estimation is carried out. A k Nearest Neighbours (kNN) classification procedure is developed: a measured air shower is classified as proton-like or iron-like by finding its nine nearest neighbours in the parameter space of the selected mass sensitive observables. The total number of charged particles (N ch ) and the total number of muons (N μ ) are found to be suitable observables for this purpose. The parameter space is populated by a set of simulated proton and iron induced air showers and an air shower is assigned to the class most common among its neighbours. The misclassification errors, as obtained in the training phase, are taken into account. An increase of the relative number of iron-like induced air showers is observed between 10 16 eV and 3.2·10 16 eV, which could be associated with the decrease of the cosmic ray light component (He, CNO group) due to the break in its energy spectrum. Above 3.2·10 16 eV, about 70% of the events result to be iron-like and ≈ 30% proton-like. At the highest observed energies an increase of light primaries is indicated. The results presented in this work constitute a first attempt to carry out composition analysis with KASCADE-Grande data.
Designing futures through student engagement: A policy futures perspective

Futures research is gaining increased prominence in educational research and development ( Tesar, 2021 ), and particularly now as we emerge from the global COVID-19 pandemic, which has provided a lever for change and an opportunity for innovation in learning, teaching and assessment ( Hall et al., 2020 ; Jandrić et al., 2022 ; Tesar, 2020 ). Designing Futures (DF) is an initiative that aims to transform the student learning experience at university, including through promoting student entrepreneurship and enhanced interaction with enterprise, industry and the innovation sector, supported by a national employability policy agenda, and concomitant, significant government funding. Ireland’s Higher Education Authority has invested €7.57 m in the DF programme at University of Galway for a period of 5 years, 2020–2025. However, introducing such a programme as DF within higher education raises problematic tensions around the purpose of higher education today, as set amidst the current policy futures perspective. Specifically, how do we balance policy imperatives to work more closely with enterprise and industry, while at the same time protecting the essential role of higher education, which must be to provide a formative context for all students to reach their fullest potential as active citizens? This paper helps to position the concept of student engagement, taking DF as an exemplar initiative, and examining the concept as it is construed and deployed in an innovative, futures-oriented educational programme. This review is critical for DF, to ensure we remain fundamentally focused on education, and not just for the world of work, which is of course important, but beyond enterprise and industry: to ensure students’ readiness for the complex and challenging world of today and tomorrow. Furthermore, this constitutes an important contribution to the literature, at a time when the identity of the university and purpose of higher education are the focus of an educationally problematic neoliberal agenda ( Mintz, 2021 ).
Una revisión de tres clíticos adverbiales en purépecha y su relación con las categorías de evidencialidad y modalidad epistémica

This paper describes the evidential clitic =nha in Purepecha, alongside two other clitics of epistemic value: =mintu and =xeru . It is shown that =mintu expresses a high degree of certainty, while =xeru corresponds to a low degree of certainty. Moreover, the use of =mintu does not present restrictions as to whether the evidence is direct or indirect, unlike languages like Spanish or English, where the use of a modal of epistemic necessity is only felicitous in contexts of indirect evidence (von Fintel and Gillies 2006).
Origins of Vorticity in a Simulated Tornadic Mesovortex Observed during PECAN on 6 July 2015

To better understand and forecast nocturnal thunderstorms and their hazards, an expansive network of fixed and mobile observing systems was deployed in the summer of 2015 for the Plains Elevated Convection at Night (PECAN) field experiment to observe low-level jets, convection initiation, bores, and mesoscale convective systems. On 5–6 July 2015, mobile radars and ground-based surface and upper-air profiling systems sampled a nocturnal, quasi-linear convective system (QLCS) over South Dakota. The QLCS produced several severe wind reports and an EF-0 tornado. The QLCS and its environment leading up to the mesovortex that produced this tornado were well observed by the PECAN observing network. In this study, observations from radiosondes, Doppler radars, and aircraft are assimilated into an ensemble analysis and forecasting system to analyze this event with a focus on the development of the observed tornadic mesovortex. All ensemble members simulated low-level mesovortices with one member in particular generating two mesovortices in a manner very similar to that observed. Forecasts from this member were analyzed to examine the processes increasing vertical vorticity during the development of the tornadic mesovortex. Cyclonic vertical vorticity was traced to three separate airstreams: the first from southerly inflow that was characterized by tilting of predominantly crosswise horizontal vorticity along the gust front, the second from the north that imported streamwise horizontal vorticity directly into the low-level updraft, and the third from a localized downdraft/rear-inflow jet in which the horizontal vorticity became streamwise during descent. The cyclonic vertical vorticity then intensified rapidly through intense stretching as the parcels entered the low-level updraft of the developing mesovortex.
Forecasting Electricity Consumption based on Nested LSTM and Attention Mechanism Approach with Cuckoo Search Optimizer

This paper presents electricity forecasting by integration of Nested Long Short Term Memory (NLSTM) as the another type of LSTM, Attention Mechanism (AM) as the technique for Neural Network and optimized by Cuckoo Search Optimizer (CSO) as the metaheuristic. It is important because of the electricity forecasting issue. Electricity is the crucial energy, due to the many sectors which is require it. Estimation of electricity consumption can enhance the effectiveness of generation and supply. The shortage and outage of electricity is the thing that should be avoided through the electricity forecasting. In order to create better forecasting result, the forecasting by NLSTM-AM will be combined. NLSTM-AM has several parameters which influence the network performance. Those parameters will be optimized by CSO. To discover the performance of this proposed method, the RMSE of this method will be compared by other algorithms. In this paper there are two datasets which will be used, those are Taiwan electricity consumption and Turkey electricity consumption. It is proved that NLSTM-AM-CSO has the smallest RMSE rather than other algorithms.