Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Geographically and Temporally Weighted Regression Using S-estimator in Criminal Case in East Java Province

Geographically weighted regression (GWR) is a model that can be used for data with spatial varying. Geographically and Temporally Weighted Regression (GTWR) is a development of the GWR model for data spatial and temporal varying. Parameter estimation in GTWR model uses weighted least square method which is very sensitive to outliers data. The outlier caused bias in parameter estimation, so it must be handled by robust GTWR (RGTWR). In this research, S-estimator was used to handle outliers and estimate an RGTWR. Both GTWR and RGTWR is used to build model crime rate in East Java 2011-2015. The Crime rate is used as a response variable and the percentage of poor people, population density, and human development index are used as explanatory variables. The best model in this research is RGTWR using S-estimator. RGTWR using S-estimator has a coefficient of determination equal to 98,2 meanwhile RMSE equal to 33.941 and MAD equal to 4.994.
New Algorithms for Counting Temporal Graph Pattern

Temporal networks can describe multiple types of complex systems with temporal information in the real world. As an effective method for analyzing such network, temporal graph pattern (TGP) counting has received extensive attention and has been applied in diverse domains. In this paper, we study the problem of counting the TGP in the temporal network. Then, an exact algorithm is proposed based on the time first search (TFS) algorithm. This algorithm can reduce the intermediate results generated in the graph isomorphism and has high computational efficiency. To further improve the algorithm performance, we design an estimation algorithm by applying the edge sampling strategy to the exact algorithm. Finally, we evaluate the performances of the two algorithms by counting both the symmetric and asymmetric TGP. Extensive experiments on real datasets demonstrated that the exact algorithm is faster than the existing algorithm and the estimation algorithm can greatly reduce the running time while guaranteeing the accuracy.
Millimeter-Wave Downlink Positioning With a Single-Antenna Receiver

This paper addresses the problem of determining the unknown position of a mobile station for a mmWave multiple-input single-output (MISO) system. This setup is motivated by the fact that massive arrays will be initially implemented only on 5G base stations, likely leaving mobile stations with one antenna. The maximum likelihood solution to this problem is devised based on the time of flight and angle of departure of received downlink signals. While positioning in the uplink would rely on angle of arrival, it presents scalability limitations that are avoided in the downlink. To circumvent the multidimensional optimization of the optimal joint estimator, we propose two novel approaches amenable to practical implementation thanks to their reduced complexity. A thorough analysis, which includes the derivation of relevant Cramér–Rao lower bounds, shows that it is possible to achieve quasi-optimal performance even in presence of few transmissions, low signal-to-noise ratio (SNRs), and multipath propagation effects.
Spectral Reflectance Processing via Local Wavelength-Direction Correlations

The spectral bidirectional reflectance distribution function (BRDF) maps incident radiation of a surface to its outgoing counterpart at different wavelengths. This function plays a fundamental role in characterizing the various types of earth surfaces. Due to its high dimensionality, the measurements, analysis, and simulation of spectral BRDF are challenging. In this letter, we introduce a new method for processing spectral reflectance using the so-called data-adjacency, i.e., the correlation between adjacent wavelengths and viewing directions. The results show that the benefits of efficient representation, noise reduction, and analysis capability can all be integrated to the data.
On varieties of superalgebras with graded involution and small *-graded colength

In this thesis, we present the extension of some classical results of PI-theory to the class of ∗-superalgebras, that is, superalgebras endowed with a graded involution. Let V = var(A) be a variety generated by a finite dimensional ∗-superalgebra A over a field F of characteristic zero. Giambruno, dos Santos and Vieira proved in [9] that the ∗-graded codimension sequence of V is polynomially bounded if, and only if, V does not contain five ∗-superalgebras: D∗ the commutative algebra D = F ⊕F endowed with the exchange involution and trivial grading; D the commutative algebra with grading F (1, 1) ⊕ F (1,−1) and endowed with trivial involution; D the commutative algebra with non-trivial grading and endowed with the exchange involution; M∗ and M , where M is a suitable 4-dimensional subalgebra of the algebra of 4×4 upper triangular matrices, endowed with the reflection involution and with trivial and non-trivial grading, respectively. As a consequence the algebras D∗, D, D, M∗ and M gri generate the only varieties generated by finite dimensional ∗-superalgebras of almost polynomial growth. We expound here the classification of all subvarieties of these five varieties of almost polynomial growth, that was given in [21] and [12] in different contexts. We also exhibit the decompositions of the ∗-graded cocharacters of all minimal subvarieties of var(D∗), var(D), var(D), var(M∗) and var (M ) and compute their ∗-graded colengths. Finally, we classify the varieties generated by finite dimensional ∗-superalgebras such that their sequence of ∗-graded colengths is bounded by three.
A Distributed Algorithm For Almost-Nash Equilibria of Average Aggregative Games With Coupling Constraints

We consider the framework of average aggregative games, where the cost function of each agent depends on his own strategy and on the average population strategy. We focus on the case in which the agents are coupled not only via their cost functions, but also via a shared constraint coupling their strategies. We propose a distributed algorithm that achieves an $\varepsilon$-Nash equilibrium by requiring only local communications of the agents, as specified by a sparse communication network. The proof of convergence of the algorithm relies on the auxiliary class of network aggregative games. We apply our theoretical findings to a multimarket Cournot game with transportation costs and maximum market capacity.
Semiparametric Estimation and Variable Selection for Single‐Index Copula Models

A copula model with flexibly specified dependence structure can be useful to capture the complexity and heterogeneity in economic and financial time series. However, there exists little methodological guidance for the specification process using copulas. This paper contributes to fill this gap by considering the recently proposed single-index copulas, for which we propose a simultaneous estimation and variable selection procedure. The proposed method allows to choose the most relevant state variables from a comprehensive set using a penalized estimation, and we derive its large sample properties. Simulation results demonstrate the good performance of the proposed method in selecting the appropriate state variables and estimating the unknown index coefficients and dependence parameters. An application of the new procedure identifies six macroeconomic driving factors for the dependence among U.S. housing markets.
Rough Set Discretize Classification of Intrusion Detection System

Many pattern classification tasks confront with the problem that may have a very high dimensional feature space like in Intrusion Detection System (IDS) data. Rough set is widely used in IDS to overcome the arising issue. In rough set, there are several stage processing including discretization part which is a vital task in data mining, particularly in the classification problem. Two results distinguish showing that the discretization stage is hugely important in both training and testing of IDS data. In proposed framework, analysis should been done to discretization, reduct and rules stage to determine the significant algorithm and core element in IDS data. The classification using standard voting, since it is a rule-based classification.
A Data Reduction Pipeline for Gemini-North’s Near-infrared Integral Field Spectrometer

We present a python package, called Nifty4Gemini, and its associated Pyraf/Python based pipeline for processing Gemini-North Near-Infrared Integral Field Spectrometer (NIFS) observations. Built on the Gemini IRAF package’s capabilities, Nifty4Gemini's associated NIFS pipeline is a data reduction package which reduces NIFS raw data and produces a flux and wavelength calibrate science cube with the full signal-to-noise ratio, ready for science analysis. It utilizes tasks from the Gemini IRAF package, PyRAF, and packages from the Gemini AstroConda environment. Nifty4Gemini is a configuration-based pipeline framework written in python which is easily extensible to integrate additional pipelines and user-defined scripts. Nifty4Gemini is open source and available for download at https://github.com/mrlb05/Nifty4Gemini with its documentation available at https://nifty4gemini.readthedocs. io/en/latest/. A permanent version of the software described in this paper is archived at https://zenodo.org/record/1000413.
SMART FOG COMPUTING FOR EFFICIENT SITUATIONS MANAGEMENT IN SMART HEALTH ENVIRONMENTS

Ontologies are considered a backbone for supporting advanced situation management in various smart domains, particularly smart health. It plays a vital role in understanding user context in order to determine patients’ safety, situation identification accuracy, and provide personalized comfort. The smart health domain contains a huge number of different types of context profiles related to interactive devices, linked health objects, and smart-home. The key role of context profiles is to deduce urgent situations that are needed to run adaptation components on a specific smarthealth Fog. Existing platforms and middlewares lack support to efficiently analyze a large number of heterogeneous specific profiles and continuous context changing in near real time. In this paper, we focus on data and dissemination of information from services related to the field of e-health. This paper aims to provide a new generic user situation-aware profile ontology (GUSP-Onto) for a semantic description of heterogeneous users’ profiles with efficient patients’ situation management and health multimedia information dissemination related to smart health services. Based on the users’ situation management ontology, a two-layered architecture was proposed. The first layer is used to achieve a quality diagnosis of urgent situations including a smart fog computing enhanced with semantic profile modeling that offers efficient situation management. The second layer allows a more in-depth situation analysis for patients and enhanced rich services using cloud computing that provides good scalability. The most innovative of this architecture is the potential benefits from the semantic representation to conduct emergency situation knowledge reasoning and ultimately realize early service selection and adaptation process. The experimental results show a decreased time response and an enhanced accuracy of the proposed approach.
Walk Proximal Gradient: An Energy-Efficient Algorithm for Consensus Optimization

Decentralized computing is widely used for multiagent systems since it works without a central computing node. In this paper, we develop a first-order algorithm for decentralized consensus optimization that is more energy efficient than the current state-of-the-art. Our algorithm is suitable for application scenarios such as networks of wireless sensors and Internet of Things, where some agents have limited (battery) energy. We call our algorithm walk proximal gradient (WPG), which passes a token through a walk (a succession of nodes) in the graph. The agents that are visited during the walk compute the gradients of their private functions and update the token. We analyze WPG where the walk is the repetition of a Hamiltonian cycle and show that the token converges to the consensual solution faster (in terms of energy consumption) than existing gradient-based decentralized methods. We also generalize the analysis to the non-Hamiltonian graphs. Numerical experiments are presented to validate the energy efficiency of our algorithm.
Integration of Analytical Hierarchy Process and Landslide Susceptibility Index Based Landslide Susceptibility Assessment of the Pearl River Delta Area, China

Landslide is one of the most disastrous geological hazards in the Pearl River Delta area. In the article, landslide susceptibility index (LSI) and analytical hierarchy process (AHP) have been adopted to assess landslide susceptibility (LS) of the Pearl River Delta area. A total of 294 historical landslide sites were extracted from China Geological Survey Result CGS2015-008 and remote sensing images as landslide inventory. In each experiment, 198 landslide points were randomly selected as training samples and the remaining 96 as verification samples. Nine influencing factors, i.e., standard deviation (STD) of elevation, terrain roughness, curvature, lithology, fault, land use, water density, road density, and aspect, were obtained through ASTER GDEM, China Geological Survey Result CGS2015-008, Geographical Information Monitoring Cloud Platform, Open Street Map, and Google Earth. LSI was used to represent the contribution of each category within the influencing factors to the occurrence of landslides (LSIi), and AHP was used to calculate the weights between different influencing factors (Wi). Finally, the summation of the product of LSIi by Wi was used to represent the LS value for each pixel. Then, the study area was grouped into five susceptibility classes based on the landslide susceptibility. It is indicated that terrain factors have the greatest impact on landslides, followed by engineering geological lithology and land use types. The high and very high susceptibility classes were mainly distributed in the area of: 1) STD of elevation of 0.8–2.5, roughness of 1.004–1.030, and curvature 2.7–7.2; 2) sand-shale stone and coal, bedded clastic rock, and bedded epimetamorphic rock; and 3) woodland and the urban and rural land. The receiver operating characteristic curves of many experimental results prove that the model has fine prediction performance and good stability. This article could provide a basis for landslide prevention and land development in the Pearl River Delta area.
Bayesian Neural Network improvements to nuclear mass formulae and predictions in the SuperHeavy Elements region

A systematic study based on the Bayesian Neural Network (BNN) statistical approach is introduced to improve the predictive power of current nuclear mass formulae when applied to nuclides not yet experimentally detected. In a previous work by the present authors, the methodology was introduced considering only the Duflo-Zuker mass model (Duflo J. and Zuker A., Phys. Rev. C, 52 (1995) R23) to explore the S uperH eavy E lements (SHE) region, with focus on the α-decay process. Due to the discrepancy among different mass formulae we decided to extend in the present calculation the application of the Bayesian Neural Network methodology to other mass formula models and to discussing their implications on predictions of SHE α-decay half-lives. The -value prediction using a set of ten different mass models has been greatly improved for all models when compared to the available experimental data. In addition, we have used the improved -value to determine the SHE α-decay half-lives with a well-succeeded model in the literature, currently employed for different hadronic nuclear decay modes of heavy nuclei, the E ffective L iquid D rop M odel (ELDM). Possible SHE candidates recently investigated are explicitly calculated (specially the 298, 299,300120 isotopes, and results present a promising via of research for these nuclei through α-decay process.
Random Attractor for Hindmarsh-Rose Equations with additive noise

For stochastic Hindmarsh-Rose equations with addictive noises in the study of neurodynamics, the longtime and global pullback dynamics on a two-dimensional bounded domain is explored in this work. Using the additive transformation and by the sharp uniform estimates, we proved the pullback absorbing and the pullback asymptotically compact characteristics of the Hindmarsh-Rose random dynamical system in the $L^2$ Hilbert space. It shows the existence of a random attractor for this random dynamical system.
Remote N2O gas sensing by enhanced 910-m propagation of THz pulses.

We modified our 910-m long path THz system to increase the signal-to-noise ratio (S/N) with a nanostructure plasmonic THz transmitter (Tx) chip and a seven-mirror array reflector with 1 m diameter. When the THz pulse propagates the 910-m distance in the atmosphere, the S/N is up to 1170:1, which made the THz pulse measurable at a high water vapor density (WVD) of up to 25.2 g/m3. The time shift of the THz pulse according to the WVD measured for each meteorological season was matched well with the theoretical result. Due to the modified long-distance THz system, we were able to measure for the first time the resonances of N2O gas, which is located 455 m away from the Tx and receiver (Rx) chips and contained in a 1.5-m diameter rubber balloon under atmospheric pressure. Seven resonances can be detected except for one overlay of resonant frequency by water vapor.
Combining Linear Algebra and Numerical Optimization for Gray-Box Affine State-Space Model Identification

Accurately estimating the parameters of a gray-box linear time-invariant state-space representation is still a challenging problem especially if the number of unknowns exceeds ten. The standard nonlinear optimization-based procedures often fail because the initial guesses are not in the domain of attraction of the user-defined cost function global minimum. Herein, a new solution is developed to give access to estimated parameters which are accurate estimates of the unknown parameters directly or, at worst, good initial guesses for any standard nonlinear optimization-based method. By assuming that 1) the gray-box model to identify is minimal and structurally identifiable, 2) the parameter dependency is affine, and 3) the available datasets have been transformed into a reliable and accurate black-box state-space model, our procedure consists in determining the unique similarity transformation between the black-box and gray-box representations of the system to identify. More specifically, we introduce a technique which first extracts a system of equations linear in terms of the similarity transformation; second, it solves this system by using standard linear algebra tools; finally, completes this two-step procedure by a numerical optimization solution (dedicated now to a very small number of unknowns) if the second step does not give access to all the similarity transformation parameters effectively. In this contribution, we first describe our new procedure, prove its capabilities of giving accurate estimates under specific practical constraints, and finally demonstrates its effectiveness with simulation examples.
EDUCATIONAL REWARD INFORMATION COMMUNICATION API (ERIC API): A PRELIMINARY STUDY RESULT

Educational Resource Information Communication (ERIC) API is used to connect two separate systems while keeping both systems working independently without leaking users’ privacy data. This research uses ERIC API to integrate an educational reward system called Trading Card Game with Moodle, a famous open-source learning management system. When students authorize Moodle to dispatch the rewards (i.e., in-game cards) for completing learning activities (e.g., assignments and quizzes) to their account in the Trading Card Game, Moodle will no information about the credentials that they have in the Trading Card Game. This research conducts a pilot study to understand whether or not students are satisfying with having the API to integrate Moodle and Trading Card Game. The results not only show that ERIC API is acceptable for students but also provide researchers and teachers support of evidence to having a reward system into their learning management system.
Numerical modelling of oscillating flow around a circular cylinder

The current trend in pipeline research is dominated by two approaches: experimental and numerical modelling. Recent advancement in computer technology has enabled more complicated engineering problems be modelled on a personal computer. Hence, the principal aim of this research is to investigate the issues regarding the numerical modelling of an oscillating flow around a fixed circular cylinder. These include a domain size study and a grid dependence study for two-dimensional modelling and a detailed comparison between the two-dimensional modelling and threedimensional modelling of the flow. The current study has compared and verified the two-dimensional and threedimensional models with experimental results. A few typical quantities were used for the comparison and verification, such as force coefficients, vorticity and pressure around the cylinder. Due to the length of candidature, for the two-dimensional model, only five Keulegan-Carpenter number (KC) cases were studied, K C = 5 , 9, 14, 17 and 25. For three-dimensional, only K C = 9 and 17 were studied. Using the finite element method (FEM) with a large eddy simulation model (LES), the two-dimensional and three-dimensional numerical models for an oscillating flow around a circular cylinder were developed. The two-dimensional model was first used to investigate the oscillating flow around a circular cylinder. The issues investigated in this model include the domain size study, grid dependence study, flow structures, hydrodynamic forces, separation angle and force coefficients of different K C cases. It was found that the two-dimensional model can predict the low K C cases fairly well compared with experimental results but not for high K C cases. Then the threedimensional model was applied to model an oscillating flow around a circular cylinder and the results generated agreed well with the experimental results for both K C cases. Also, it was found that the three-dimensional model gave a better prediction on oscillating flows around a circular cylinder than the two-dimensional model through comparing force coefficients, flow structures, pressure contours and force history around the cylinder.
Mathematical model of lead transfer in human body

A mathematical model of lead transfer in human body is developed by using compartmental model based on previous studies and formulated in mathematical terms to calculate and predict the level of lead in human body. The Batschelet's threecompartmental model being used in this study is adopted from the Rabinowitz et al. (1976). The experimental data adopted in this study is drawn from the published researches to test the mathematical model for solving the real biological system illustrated by the Batschelet's three-compartmental model and to ascertain the mathematical model fit the experimental data. Some examples of a real biological system of lead transfer in human body are studied such as the amount of lead and the equilibrium state of lead level stored in several major parts of human body. A mathematical model derived from the compartmental model which involves first-order ordinary differential equations is solved analytically by using a mathematical technique which utilize some basic concepts from the algebra of matrices. The mathematical general solution obtained is employed by using Microsoft Excel program to produce tables and plot graphs.