Miracle-dz/UniFAR-FTUs huggingface dataset:
CDT-Based Gaussian Sampling: From Multi to Double Precision

The Rényi divergence is a measure of closeness of two probability distributions which has found several applications over the last years as an alternative to the statistical distance in lattice-based cryptography. A tight bound has recently been presented for the Rényi divergence of distributions that have a bounded relative error. We show that it can be used to bound the precision requirement in Gaussian sampling to the IEEE 754 floating-point standard double precision for usual lattice-based signature parameters by using a modified cumulative distribution table (CDT), which reduces the memory needed by CDT-based algorithms and, makes their constant-time implementation faster and simpler. Then, we apply this approach to a variable-center variant of the CDT algorithm which occasionally requires the online computation of the cumulative distribution function. As a result, the amount of costly floating-point operations is drastically decreased, which makes the constant-time and cache-resistant variants of this algorithm viable and efficient. Finally, we provide some experimental results indicating that comparing to rejection sampling our approach increases the GPV signature rate by a factor 4 to 8 depending on the security parameter.
Local Community Detection With the Dynamic Membership Function

Most of the community detection methods require the global information of the original network to be available, however, it is often expensive (even no way) to obtain the global information of the network in many real-world networks. So, the local community detection, only based on the local information, becomes especially important. The local community is the community in the network to which a given starting node belongs. Some local community detection methods have been proposed. However, these methods did not consider the characteristics of the local community during the local community formation. In this paper, we analyze the formation of the local community and propose two local community detection algorithms based on the dynamic membership function. Each of the algorithms is divided into three stages: 1) the initial stage, 2) the middle stage, and 3) the closing stage. At the initial stage, we design a dynamical membership function to detect local community and nodes with the greatest neighborhood intersect rate could be added to the local community. At the middle stage, we design another dynamical membership function, and the goal of this stage is to make the connection of the node in the local community closest. At the closing stage, the third dynamical membership function is provided, and the local community is further improved by collecting some nodes that should not be omitted. We test our algorithms on several synthetic datasets and real datasets; the results show that the local communities detected by our method are closer to the real local communities.
Efficient Hybrid Full-Wave/Circuital Approach for Stacks of Frequency Selective Surfaces

This letter shows an efficient hybrid method to study stacks of identical frequency selective surfaces (FSSs) separated by possibly different dielectrics. The method takes advantage of the ability of the equivalent circuit approach to create a <inline-formula><tex-math notation="LaTeX">$\Pi$</tex-math></inline-formula>-network for a pair of coupled FSSs and combines it with full-wave simulations involving a single FSS. This way, the solution of the complete problem of <inline-formula><tex-math notation="LaTeX">$N$</tex-math></inline-formula> stacked surfaces only involves full-wave simulations of a single periodic layer. Three scenarios have to be simulated where the surface is either standalone, or backed by perfect electric or perfect magnetic conductors. The proposed approach is validated through the analysis of several examples.
A Current-Accuracy-Enhanced Wide-Input-Range DC–DC LED Driver With Feedforward Synchronous Current Control

A wide-input-range (5–115 V) current-accuracy-enhanced dc–dc LED driver is presented in this paper. A feedforward synchronous current control (FSCC) scheme is developed to improve the LED current accuracy over wide ranges of input and output voltages. In the FSCC, a reconfigurable valley current sensor is proposed to provide accurate positive and negative valley inductor current information for the controller corresponding to hard- and soft-switching modes, respectively. A dynamic peak reference generator is developed to automatically adjust the peak current reference value dependent on input and output voltages for reducing the deviations of the average LED current under different input voltages and numbers of output LED, thereby improving the current accuracy. Implemented in a 0.5-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> 120-V CMOS process, the experimental results show that the proposed LED driver IC achieves a current accuracy within ±6.0% of the average LED current of 350 mA over an input voltage range from 5 to 115 V and numbers of series-connected LEDs from 1 to 25 by using only a small inductor of 10 <inline-formula> <tex-math notation="LaTeX">$\mu \text{H}$ </tex-math></inline-formula>. Compared with the state-of-the-art synchronous current control scheme, the variations of the LED current can be reduced by 2.3 and 4.2 times in the hard- and soft-switching modes, respectively, with the proposed LED driver using the FSCC.
Psychometric Properties of the Dutch Strengths and Difficulties Questionnaire (SDQ) in Adolescent Community and Clinical Populations

This study assessed the factor structures of the Strengths and Difficulties Questionnaire (SDQ) adolescent and parent versions and their measurement invariance across settings in clinical (n = 4,053) and community (n = 962) samples of Dutch adolescents aged 12 to 17 years. Per SDQ version, confirmatory factor analyses were performed to assess its factor structure in clinical and community settings and to test for measurement invariance across these settings. The results suggest measurement invariance of the presumed five-factor structure for the parent version and a six-factor structure for the adolescent version. Furthermore, evaluation of the SDQ scale sum scores as used in practice, indicated that working with sum scores yields a fairly reasonable approximation of working with the favorable but less easily computed factor scores. These findings suggest that adolescent- and parent-reported SDQ scores can be interpreted using community-based norm scores, regardless of whether the adolescent has been referred for mental health problems.
A meta-analysis of techniques to promote motivation for health behaviour change from a self-determination theory perspective

ABSTRACT A systematic review and meta-analysis was conducted of the techniques used to promote psychological need satisfaction and motivation within health interventions based on self-determination theory (SDT; Ryan & Deci, 2017. Self-determination theory: Basic psychological needs in motivation, development, and wellness. New York, NY: Guilford Press). Eight databases were searched from 1970 to 2017. Studies including a control group and reporting pre- and post-intervention ratings of SDT-related psychosocial mediators (namely perceived autonomy support, need satisfaction and motivation) with children or adults were included. Risk of bias was assessed using items from the Cochrane risk of bias tool. 2496 articles were identified of which 74 met inclusion criteria; 80% were RCTs or cluster RCTs. Techniques to promote need supportive environments were coded according to two established taxonomies (BCTv1 and MIT), and 21 SDT-specific techniques, and grouped into 18 SDT based strategies. Weighted mean effect sizes were computed using a random effects model; perceived autonomy support g = 0.84, autonomy g = 0.81, competence g = 0.63, relatedness g = 0.28, and motivation g = 0.41. One-to-one interventions resulted in greater competence satisfaction than group-based (g = 0.96 vs. 0.28), and competence satisfaction was greater for adults (g = 0.95) than children (g = 0.11). Meta-regression analysis showed that individual strategies had limited independent impact on outcomes, endorsing the suggestion that a need supportive environment requires the combination of multiple co-acting techniques.
High-Dimensional Poisson DAG Model Learning Using ε1-Regularized Regression

In this paper, we develop a new approach to learning high-dimensional Poisson directed acyclic graphical (DAG) models from only observational data without strong assumptions such as faithfulness and strong sparsity. A key component of our method is to decouple the ordering estimation or parent search where the problems can be efficiently addressed using $\ell_1$-regularized regression and the mean-variance relationship. We show that sample size $n = \Omega( d^{2} \log^{9} p)$ is sufficient for our polynomial time Mean-variance Ratio Scoring (MRS) algorithm to recover the true directed graph, where $p$ is the number of nodes and $d$ is the maximum indegree. We verify through simulations that our algorithm is statistically consistent in the high-dimensional $p>n$ setting, and performs well compared to state-of-the-art ODS, GES, and MMHC algorithms. We also demonstrate through multivariate real count data that our MRS algorithm is well-suited to estimating DAG models for multivariate count data in comparison to other methods used for discrete data.
Evaluation of Unsupervised Classification on Police Patrol Zone Design Problem

Police patrols are one of the effective ways to respond a recent incident or to prevent a likely crime. However, due to limited number of available officers, not all streets can be patrolled continuously. To minimize the potential crimes, available forces should be systematically assigned to their zones, where an incident is most likely to happen. In this study, we propose a new technique that automatically determines the patrol zones and shifts, then assigns available officers to the zones. Our technique utilizes the previous crime data of the region and employs unsupervised classification to automatically identify zones. Then, it estimates a crime weight for each zone based on several factors, such as the probability and type of an incident. Finally, it assigns officers to the zones by prioritizing crime weight estimations. As a case study, we tested our system on the city of Montgomery, AL. We used the crime dataset including all reported 19687 incidents in 2017. We have built our scheduling system using 11 months of data and then tested the system using the incidents happened in the last month. Test results show that, with the proposed technique, scheduled officers are most likely to present nearby the incident before it happens. We measured 7 minutes of response time on the average, which is lower than the national average.
Adaptive Tracking Control for Lagrangian Networks with Unknown Dynamics over a Directed Graph

This paper investigates the tracking synchronization problem of delayed networked Lagrangian systems over directed networks. A tracking synchronization protocol based on the periodically intermittent control scheme is proposed for such systems in the presence of parametric uncertainties. Some algebraic criteria for tracking synchronization with time-delays are established. It turns out that these criteria can provide a novel periodically intermittent control strategy to synchronize globally networked Lagrangian systems to a given time-varying target trajectory. Finally, numerical simulation results are given to show the effectiveness of the proposed control algorithms.
Consensus Control of UUVs Formation Under Position and Velocity Topologies with Different Time-Varying Delays

A new method which applied consensus algorithm to solve the coordinate control problems of leader-following UUVs formation. In order to overcome the limited communication width, a new topology type which divided the traditional communication topology into two independent parts: position communication topology and velocity communication topology is proposed. And in the novel communication topologies there are different time-varying delays. In additions, the math model of UUV is nonlinear and coupled. With the help of state feedback linearization method, the nonlinear math model of UUV can be transferred into a double-integrator dynamic model. Based on the result of linearized model, the leader-following UUVs formation coordinate control can be regarded as a consensus problem with different time-varying communication delays. As a result, sufficient conditions for the stability of system are proposed and they are proven by using the Laypunov-Razumikhin theorem. In the end, the simulation results are presented to confirm and illustrate the theoretical results.
Real-time Dynamic Track Planning of Multi-UAV Formation Based on Improved Artificial Bee Colony Algorithm

Aiming at the Quad-rotor UAV formation in the environment of dynamic threats, improved artificial bee colony (IABC) algorithm was suggested in the real-time track planning because of its fewer control parameters and faster convergence. The simulation results show that the track planning based on IABC algorithm is faster than that based on ant colony optimization (ACO) algorithm in avoiding threats, and the real-time dynamic track planning based on IABC is effective in scenarios of the fixed and unfixed multi-UAV formation faced with dynamic threats.
Ionic Electroactive Polymer Actuated Prosthetic Finger Design

The proposed work covers an ionic electroactive polymer actuator production for use in a prosthetic finger design. The best mechanical design will be possible if it considers anatomical features of the real finger/hand structure. A number of different finger movements for different purposes are formed by the antagonistic pairs of muscles. Ionic electroactive polymer actuators are intended to be used instead of intrinsic muscles, which enable precise movement of the hand beyond the force required for movement due to their low power consumption, low noise, high power/mass ratio.
Applying Data Mining Techniques to Predict Student Dropout: A Case Study

The prevention of students dropping out is considered very important in many educational institutions. In this paper we describe the results of an educational data analytics case study focused on detection of dropout of System Engineering (SE) undergraduate students after 7 years of enrollment in a Colombian university. Original data is extended and enriched using a feature engineering process. Our experimental results showed that simple algorithms achieve reliable levels of accuracy to identify predictors of dropout. Decision Trees, Logistic Regression and Na¨ıve Bayes results were compared in order to propose the best option. Also, Watson Analytics is evaluated to establish the usability of the service for a non expert user. Main results are presented in order to decrease the dropout rate by identifying potential causes. In addition, we present some findings related to data quality to improve the students data collection process.
Feature prioritization and regularization improve standard accuracy and adversarial robustness

Adversarial training has been successfully applied to build robust models at a certain cost. While the robustness of a model increases, the standard classification accuracy declines. This phenomenon is suggested to be an inherent trade-off. We propose a model that employs feature prioritization by a nonlinear attention module and L2 feature regularization to improve the adversarial robustness and the standard accuracy relative to adversarial training. The attention module encourages the model to rely heavily on robust features by assigning larger weights to them while suppressing non-robust features. The regularizer encourages the model to extract similar features for the natural and adversarial images, effectively ignoring the added perturbation. In addition to evaluating the robustness of our model, we provide justification for the attention module and propose a novel experimental strategy that quantitatively demonstrates that our model is almost ideally aligned with salient data characteristics. Additional experimental results illustrate the power of our model relative to the state of the art methods.
Synthesizing versus Augmentation for Arabic Word Recognition with Convolutional Neural Networks

In this paper, we present a sub-word recognition method for historical Arabic manuscripts, using convolutional neural networks. We investigate the benefit of extending training set with synthetically created samples in comparison to augmentation. We show that annotating around ten pages of a manuscript and extending it, is sufficient for successful sub-word recognition in the whole manuscript. In addition, we show the contribution of using different combinations of training sets and compare their sub-word recognition performance in the whole manuscript.
Adaptive Control for the Four-Bar Linkage Mechanism Based on Differential Evolution

Position tracking in the four-bar linkage mechanism is an important task in several high precision automation processes in the industry. Accuracy may decrease due to uncertainties, but an adaptive control approach can handle them. In this work, an adaptive control strategy for the trajectory tracking of the four-bar linkage mechanism is presented. This strategy includes an adaptive mechanism based on a dynamic optimization process. In this process, a dynamic optimization problem is stated and solved on-line using a meta-heuristic optimizer to find the best set of control parameters which are used in a PID-computed-torque controller. Differential Evolution is chosen as the optimizer and a variant with an optimum tracking mechanism which allows to maintain the diversity of solutions is proposed in order to handle the changing best solution of the dynamic optimization problem. A full statistical analysis is used to prove the effectiveness of the proposed strategy.
Polynomial Fuzzy Observed-State Feedback Stabilization via Homogeneous Lyapunov Methods

A homogeneously state-dependent polynomial Lyapunov function for an observed-state feedback polynomial fuzzy control system is proposed. From which an advanced sufficient sum of squares (SOS) condition formulated in terms of state-dependent matrix inequalities to achieve fuzzy stabilization is derived. Three main features, compared with existing nonhomogeneously polynomial methods, are as follows: 1) homogeneous Lyapunov function and its time derivative are nicely linked via the Hessian matrix; 2) removing the nonconvex obstacles on <inline-formula> <tex-math notation="LaTeX">$\dot{P}(x)$</tex-math></inline-formula> when deriving a nonhomogeneous Lyapunov stabilization condition <inline-formula><tex-math notation="LaTeX">$\dot{V}(x)$</tex-math></inline-formula>; 3) separation principle holds; since common <inline-formula><tex-math notation="LaTeX">$P$</tex-math></inline-formula> falls out as a special case, the proposed homogeneous SOS approach is placed ahead of the existing SOS-based methods found in polynomial fuzzy stabilization controls. To verify the problem-solving theories of the homogeneous polynomial method announced, four examples are demonstrated to show the promising features of the approach adduced. Finally, an Appendix illustrating the solving procedure is added for clarification on how the examples are solved via SOS.
Modelo de canal específico del entorno para el análisis y simulación de sistemas vía radio en interiores

Durante los ultimos anos, la tecnicas de trazado de rayos han sido ampliamente utilizadas como herramientas de simulacion para el diseno y planificaicon de sistemas de comunicacion inalambricos tanto en microceldas urbanas como enpicoceldas de interiores, debido a la compleja naturaleza de estos escenarios. El valor de tales herramientas depende de la exactitud con que sean capaces de predecir la propagacion en entornos reales. Por ello, la validacion de cualquier modelo de propagacion debe incluir comparaciones de medidas con simulaciones. En esta tesis se analiza la precision de las tecnicas de trazado de rayos basadas en una implementacion tridimensional de la Optica Geometrica y la Teoria Uniforme de la Difraccion mediante la comparacion entre medidas y simulaciones llevadas a cabo en varios escenarios de interiores, bajo diferentes condiciones de propagacion y en diferentes bandas de frecuencia. El analisis de banda estrecha muestra que tanto el nivel medio de la senal recibida como el comportamiento estadistico de sus variaciones respecto a la media pueden estimarse de forma precisa. Ademas, se demuestra que a partir de la informacion de la senal reicibida en un unico punto, obtenida mediante trazado de rayos, es posible estimar la potencia media y las estadisticas de la senal en un area local de ese punto. Esta posibilidad tiene gran intres practico, ya que reduce sustancialmetne el timpo de computo y hace factible la obtencion de modelos de canal especificos del entorno que den cuenta de las variaciones del canal para distintas zonas dentro del area de interes. Se investiga tambien la utilizacion de un esquema de diversidad de polarizacion en interiores como mecanismo para combatir la propagacion multicamino y se estudia el comportamiento de los desvanecimientos temporales, presentes generalmente junto con los espaciales en el tipo de escenarios que nos ocupan, y que son la principal caus
Motives for Instagram Use and Topics of Interest among Young Adults

Instagram is currently the most popular social media app among young people around the world. More than 70% of people between the ages of 12 and 24 are Instagram users. The research framework of this study was constructed based on smartphone addiction and the uses and gratifications theory. We used 27 question items divided into five factors, namely social interaction, documentation, diversion, self-promotion, and creativity, to investigate the motives for Instagram use and topics of interest among university students in Taiwan. A total of 307 valid questionnaires were obtained. The results revealed that on the whole, the motives for Instagram use were mostly to look at posts, particularly involving social interaction and diversion motives. The level of agreement expressed toward motives for creating posts was lower. Gender, professional training background, and level of addiction to Instagram all exert influence on motives for Instagram use. Over half of the students majoring in design followed artisans and celebrities (including designers), and female students noticed ads on Instagram more than male students did.
Space Anomalies in OCRs for Arabic Like Scripts

This paper investigates and analyses the nature of errors occurring in Optical Character Recognition (OCR) for Arabic-like scripts. Existing research on the area of OCR for Arabic-like scripts often focuses on achieving the best performance in terms of character error rates. Only little effort targets at the analysis of the nature of these errors (anomalies) that may occur. One such important anomaly is Space Anomaly. This anomaly is due to the presence of breaker characters that are an essential part of Arabic-like scripts. The spaces introduced by breaker characters are not depicted in the ground truth making it hard for OCR to generalize. The OCR model either learns to inhibit the original spaces or to generate extra spaces at places where they are not correct. Due to this confusion, the rendering looks sub-optimal. This analyses and removes space anomalies. We present a joint approach that does not only perform OCR but also handles the space anomalies in a robust manner, hence significantly outperforming the state-of-the-art. Although the implication of the work is shown by improved character recognition rate, the impact of this research is much higher in terms of the correctness of the OCR for useful purposes, especially for rendering. The claim is supported by empirical evaluation and it is shown that the proposed approach achieved the best results.