Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Residual Network With Adaptive Learning Framework for Fingerprint Liveness Detection

Today, fingerprint recognition technology has aroused wide attention in the society, especially in the application of identity authentication with a smartphone as a carrier. However, the disadvantage of these devices is that the identification sensors are vulnerable to spoofing attacks from artificial replicas made from clay, gelatin, silicon, etc. To resolve it, a feasible anti-deception countermeasure, called fingerprint liveness detection (FLD), has been proposed. Different from most shallow feature methods, the deep convolutional neural network (DCNN)-based FLD methods have been widely explored with the properties of fast operation, few parameters, and end-to-end feature self-learning. Meanwhile, DCNN faces a pair of contradictory problems, on the one hand, the training accuracy will keep rising with the increasement of multilayer perceptron (MLP), finally tends to a stable value. Continue to increase the number of MLP, results will decline. Much research, on the other hand, shows that the number of MLP is the foundation for realizing a high performance detection. Hereby, we apply deep residual network (DRN) to FLD for the first time to solve the contradiction mentioned in this paper. Next, to eliminate the interference of invalid regions of given images, a region-of-interest (ROI) extraction algorithm is put forward. Afterward, to avoid the parameters learned plunging into local optimization, adaptive learning-based DRNs (ALDRNs), which automatically adjust the learning rate if those monitoring parameters (verification accuracy) are stable, are explored. Finally, we propose a novel texture enhancement based on the local gradient pattern (LGP) method to improve the generalization of a model classifier as well. Experimental results on three benchmark data sets: LivDet 2011, 2013, and 2015, show that our results outperform the state-of-the-art FLD methods.
Integration of Learning Management System Technology and Social Networking Sites in the E-Learning Mode: a Review and Discussion

Literature explores the technological development that universities have gone through and that they are currently facing, especially with the use of new technologies about information and communication applied to the teaching-learning process, so several models have been created also theories to adopt technology in the educational field, such as learning management systems (LMS) and social networking sites (SNS) both in the eLearning and bLearning modalities, which are the object of analysis from different theoretical frameworks, try to identify determining characteristics about their use and the impact on the educational process. Studies that were analyzed tend to consider that the synergy of these tools, mostly from a technological approach where importance is given to the "e" (electronic) and neglecting the "Learning" have led to think that each of these approaches that characterize these tools individually could be achieved by combining the best attributes of the LMS with the SNS in order to maximize the strengths and reduce the weaknesses of both tools, integrating technological approaches, pedagogical, social and cognitive.
Relay selection scheme for AF system with partial CSI and optimal stopping theory

Relay selection for Relay Assisted (RA) networks is an economical and effective method to improve the spectrum efficiency. Relay selection performs especially well when the source node has accurate and timely Channel State Information (CSI). However, since perfect CSI knowledge is rarely available, research of relay selection with partial (statistical) CSI is of paramount importance. In this paper, relay selection for RA networks with statistical CSI is formulated as a Multiple-Decision (MD) problem. And, the cost of obtaining the CSI is also considered in the formulated problem. Two relay selection schemes, Maximal Selection Probability (MSP) and Maximal Spectrum Efficiency Expectation (MSEE), are proposed to solve the formulated MD problem under different optimal criteria assumptions based on the optimal stopping theory. The MSP scheme maximizes the probability that the Best Assisted Relay Candidate (BARC) can be selected, whereas the MSEE scheme provides the maximal expectation of the spectrum efficiency. Experimental results show that the proposed schemes effectively improve the spectrum efficiency, and the MSEE scheme is more suitable for stable communication cases. Meanwhile, the MSP scheme is more suitable for burst communication cases.
GRAPHICAL REPRESENTATIONS OF MULTITHREADED APPLICATIONS

This article contains a brief description of existing graphical methods for presenting multithreaded applications, i.e. Control Flow Graph and Petri nets. These methods will be discussed, and then a way to represent multithreaded applications using the concurrent process system model will be presented. All these methods will be used to present the idea of a multithreaded application that includes the race condition phenomenon. In the summary, all three methods will be compared and subjected to the evaluation, which will depend on whether the given representation will allow to find the mentioned phenomenon.
Experimental Validation of an Integral Sliding Mode-Based LQG for the Pitch Control of a UAV-mimicking Platform

In this paper, an enhanced Integral Sliding Mode-based Linear Quadratic Gaussian (ISM-LQG) controller has been proposed and verified in real-time on a Twin Rotor multi-input-multi-output MIMO System (TRMS). A TRMS serves as a suitable laboratory-based platform to evaluate the performance of control algorithms for complex Unmanned Aerial Vehicle (UAV) systems such as rotocraft. In the proposed scheme, an ISM enhancement to an LQG has been introduced, which attempts to overcome modelling inaccuracies and uncertainties. The novelty of the proposed control law lies in hybridizing a robust control approach with an optimal control law to achieve improved performance. Experimental results on the TRMS demonstrate that the ISM-LQG strategy significantly improves the tracking performance of the TRMS pitch and hence confirm the applicability and efficiency of the proposed scheme.
Analysis of Temperature Field and Water Cooling of Outer Rotor In-Wheel Motor for Electric Vehicle

In view of the narrow installation space of high power density and high torque in-wheel motor, the heat existed the in-wheel motor is balanced by cooling water channel to ensure the heat dissipation of the high power density in-wheel motor. In this paper, Ansoft Maxwell software is used to establish the two-dimensional loss simulation model of in-wheel motor, and the winding loss, stator core loss, rotor core loss and permanent magnet eddy current loss of in-wheel motor are calculated respectively. The temperature field calculation model of in-wheel motor is established. The heat exchange coefficient of motor is calculated, and the equivalent treatment of windings is carried out. The spiral channel is selected to cool the motor. The CFD software is used to simulate and analyze the cooling fluid in the channel. The heat dissipation coefficient curve is calculated. Finally, the calculated results of in-wheel motor loss and heat dissipation coefficient curve are imported into the transient temperature field for simulation, and the temperature field distributions before cooling and after cooling are obtained. The results show that the temperature drop of winding is 32%, the temperature drop of stator core is 30%, the temperature drop of permanent magnet is 26%, and the temperature drop of rotor is 25%. The spiral channel structure adopted in this paper is reasonable and feasible, which provides a certain theoretical reference value for the research and development of in-wheel motor heat dissipation system.
Wavelet-based transforms as a means of signal separation

The goal of this paper is to facilitate the use of waveletbased transforms in BSS (and, more generally, ICA) algorithms. We investigate the performance of transforms such as the Continuous Wavelet Transform, Discrete Wavelet Transform and Discrete Wavelet Packet Transform relative to the commonly-used Short-Time Fourier Transform in terms of the efficiency of signal-power representation and the minimisation of cross-channel effects in two-channel mixtures.
Solar Power Prediction using Recurrent Neural Network

: The non-renewable sources of energy are limited and will get exhausted eventually. Looking at the current need of electric power and its fulfilment, the non-conventional way of generating this energy has become essential. Climate change and energy crisis have motivated us to make use of renewable non-conventional source of energy. This paper discusses the theoretical assumptions and design aspects of developing a Model which will predict the solar power generation beforehand. The paper aims at promoting the use of renewable source of energy by developing a model which will accurately predict the solar power generation. The suggested model uses Long Short Term Memory Recurrent Neural Network (LSTM RNN) Algorithms to predict the power generation which will be beneficial to both Industries and Residents.
Kinematic and Dynamic Analysis of an Upper limb Rehabilitation Robot

: The system outputs are predictable using the model, knowledge of an accurate model of a system is always beneficial to develop a robust and safe control while allowing a reduction of sensors-related costs. This paper presents the kinematic and dynamic analysis of multipurpose upper limb rehabilitation robot, Universal Haptic Pantograph. This robot, due to its lockable and un-lockable joints, can change its mechanical structure so that it empowers stroke patients to perform diverse training activities, exercises of the shoulder, elbow, and wrist. This work centers around the ARM mode, which is a preparation mode used to restore elbow and shoulder. The kinematical model of UHP is recognized based on the loop vector conditions, while the dynamical model is inferred dependent on the Lagrangian Formulation . The test outcomes show that the mean position error between the estimated values with the robot and actual measured values stays in 2mm. Moreover, the error between the estimated and measured interaction force is smaller than 10% of the maximum force range. So the robot can be adapted to estimate motion and force as well as control it without the need for additional sensors such as force sensor, resulting in the reduction of total robot cost. contact point ( P (cid:2887)(cid:2924) ) and the torques ( (cid:2764) (cid:1813) ) applied by the motors through the SEA based drive system. The two subsystems are connected in the transmission point ( P (cid:2904) ) with the end goal that the torque ( (cid:2764) (cid:1813) ) exerted by the actuators of the drive system and the force ( F (cid:2887)(cid:2924) ) and motion ( P (cid:2887)(cid:2924) ) applied to the Pantograph by the patient are transmitted bilaterally in the form of force ( F (cid:2904) ) and motion ( P (cid:2904) ) . As referenced previously, an appropriate mathematical model of the robot is required to actualize the robot-patient force interaction controller. Since the model requests position measurements of the drive system for the estimation of the torque ( (cid:2764) (cid:1813) ), the force ( F (cid:2887)(cid:2924) ) and motion ( P (cid:2887)(cid:2924) ), the UHP prototype includes two optical encoders and two linear potentiometers to measure the actuators rotation angle ( q (cid:2923) ) and the lengths of SEAs upper springs ( (cid:1866) (cid:3020)(cid:3002) and (cid:1866) (cid:3020)(cid:3003) ) , respectively. The model of the SEA based drive framework is utilized to register the actuators torque ( (cid:2764) (cid:1813) ) in the force controller as well as to assess the force ( F (cid:2904) ) and motion ( P (cid:2904) ) of the transmission point. The Pantograph model, on the other hand, estimates the force ( F (cid:2887)(cid:2924) ) and motion (P (cid:2887)(cid:2924)
Abnormal Usage Sequence Detection for Identification of User Needs via Recurrent Neural Network Semantic Variational Autoencoder

ABSTRACT In this paper, we propose an advanced method to detect abnormal usage patterns for identifying the fine-grained levels of user needs. Most previous studies investigated user need identification based on users textual reviews. Thus, they focused only on the explicit needs of the product levels, and not on the implied needs of the fine-grained levels. Although in a few recent studies the authors attempted to identify user needs based on abnormality detection, they considered only limited elements of the usage sequence, such as touching buttons, and did not consider the important elements, such as the dragging interaction and the pop-up and notification components. Thus, in this study, we considered all the elements of the usage sequence to identify abnormal usage sequences for recognizing user needs at the fine-grained level. Moreover, we utilized the recurrent neural network semantic variational autoencoder (RNN-SVAE) architecture, which is a state-of-the-art architecture for sentence embedding, to represent the usage sequences effectively. In detail, we calculate the vector representation of the entire usage sequence utilizing the RNN-SVAE architecture based on heterogeneous embedding to apply the abnormality detection method for determining abnormal sequences corresponding to user needs. The experimental results verify that our proposed method extracts meaningful abnormal usage patterns that previous approaches do not identify. Additionally, our proposed method shows a higher correlation of the coefficient score between the abnormality score and the importance score of the extracted sequences than do previous approaches.
Improving Real-Time Performance Under Reliability Requirement Assurance in Automotive Electronic Systems

The architecture of modern automotive electronic systems is composed of multiple distributed mixed-criticality functions. The effective utilization of such systems with heterogeneity is hindered by the scheduling of multifunctions. Minimizing the response time of functions (e.g., makespan) is an important optimization objective, and several scheduling algorithms for heterogeneous distributed architectures that optimize this objective have been proposed. However, the reliability requirements of functions of automotive systems are also an vital objective that needs to be optimized. Ignoring the reliability requirements of the functions during the scheduling process can cause catastrophic consequences, especially in automotive electronic systems. We propose a heuristic scheduling algorithm called real-time aware scheduling under reliability requirement assurance (RTRRA). The proposed algorithm meets the reliability requirement of each function by reliability transfer method technique and reduces the high-criticality functions’ deadline miss ratio (DMR) by applying resource reallocation policy. Our results indicate that the RTRRA can reduce the response time of critical functions and meet the reliability requirements of all functions compared with the state-of-the-art algorithms.
Low-power area-efficient SAR ADCs with on-chip voltage reference

• A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers.
Kurt Semm and the Fight against Skepticism: Endoscopic Hemostasis, Laparoscopic Appendectomy, and Semm's Impact on the “Laparoscopic Revolution”

In the 1970s, Semm developed thermocoagulation, adapted the Roeder Loop, and further invented extra- and intracorporeal endoscopic knotting to achieve endoscopic hemostasis. His numerous technical inventions, especially the electronic insufflator, allowed more complex operations to be performed laparoscopically. His technique, however, was not quickly adopted by the surgical community. When the first fully laparoscopic appendectomy was carried out by Semm in 1980, a veritable storm broke loose. In the opinion of many prominent surgeons, Semm exaggerated the problem of adhesions, and laparoscopic technique itself was regarded as very dangerous. Misunderstood by medical scientists, Semm displayed an ability to force his ideas through despite skepticism and suspicion. He realized that endoscopic surgery had tremendous potential, and promoted laparoscopic technique not only in his field of gynecology but among general surgeons as well. In 1985, Muhe, of Boblingen, Germany, used Semm's technique to remove the first gallbladder in the world laparoscopically. Three years later when Semm presented a videotape of his laparoscopic appendectomy in Baltimore, he gave impetus to McKernan and Saye of Marietta, Georgia, to carry out the first laparoscopic cholecystectomy in the United States.
Structural analysis of power grid security

The progresses in research of complex network theory inspire scientists to make the analysis from a new structural perspective in terms of nodes and their connection patterns. However, a pure topological approach, as proposed in the current literature, fails in capturing the specific features of power systems. Two different aspects, namely operation state and network structure, are proposed for solving power grid security problems and their relationships are discussed. Therefore it is pointed out that power security metrics should keep the dominance of complex network approach in structure analysis and capture special features of power system engineering. Furthermore, on the basis of net-ability and entropic degree, a concept of transmission betweenness is proposed. With the drop of net-ability, the critical transmission lines is identified, and entropic degree and transmission betweenness are used to quantitatively measure the connectivity and responsibility of buses respectively. Finally, the new metrics are applied to the Italian power transmission network for analysis and comparison to verify the effectiveness of the proposed method
View and Path Planning for Scaling 3D Acquisition to Many Objects

Demand for high-volume 3D scanning of real objects is rapidly growing in a wide range of applications, including quality-control for manufacturing, online retailing, entertainment with virtual reality, as well as archaeological documentation and reconstruction. Fully realizing the potential of 3D acquisition requires scanning large numbers of objects with high quality and at reasonable cost. Although mature technologies exist for high-fidelity 3D model acquisition, deploying them at scale continues to require non-trivial manual labor. This dissertation focuses on studying practical 3D acquisition for large numbers of objects. The problem is challenging, because it is hard to automatically find a proper set of scanner views that can not only completely cover the surface of multiple objects with different shapes, but also capture high fidelity surface model of the objects. Furthermore, it is non-trivial to position a 3D scanner at each of the desired views accurately and efficiently. We propose a prototype system for multi-object 3D acquisition, which allows nonexpert users to scan large numbers of physical objects within a reasonable amount of time, and with greater ease. Our system uses novel planning algorithms to control a structured-light scanner mounted on a calibrated motorized positioning system. We demonstrate the ability of our prototype to safely, robustly, and automatically acquire 3D models for large collections of small objects. We propose an objective function for automated view and path planning, taking into account both accuracy and efficiency of the scanning system. We analyze different approaches to optimize for the objective, and discuss their performance and practicality. In addition, we address the problem of surface inaccessibility to further refine our multi-object 3D acquisition system. We explore solutions for improvement from both the hardware and software ends. iii
Machine learning portfolios with equal risk contributions: evidence from the Brazilian market

We use machine learning methods to forecast individual stock returns in the Brazilian stock market, using a unique data set including technical and fundamental predictors. We find that portfolios formed on the highest quintile of predicted returns significantly outperform market benchmarks. However, portfolios formed on the lowest quintile of predicted returns earn positive returns and have high volatilities, making traditional long-short strategies unnatractive. To resolve this problem, we propose an equal risk contribution (ERC) ensemble approach to build a portfolio combining long-short portfolios obtained with various machine learning methods such that (i) the risk contributions of all individual long-short portfolios are equal, and (ii) the aggregate risk contribution of all long positions equals that of all short positions. The ERC ensemble portfolio outperforms, on an after cost, risk-adjusted basis, all individual machine learning long-short portfolios, as well as equally-weighted ensembles of these portfolios.
The D&D Sorting Hat: Predicting Dungeons and Dragons Characters from Textual Backstories

Dungeons and Dragons is a tabletop roleplaying game which focuses heavily on character interaction and creating narratives. The current state of the game’s character creation process often bogs down new players in decisions related to game mechanics, not a character’s identity and personality. This independent study investigates the use of machine learning and natural language processing to make these decisions for a player based on their character’s backstory the textual biography or description of a character. The study presents a collection of existing characters and uses these examples to create a family of models capable of predicting a character’s class, race, and attribute scores. The accuracy and limitations of these models are discussed, but they represent a first step in abstracting away some of the more tedious parts of character creation for new players.
An Improved Propagator Estimator for DOA Estimation in Monostatic MIMO Radar in the Presence of Imperfect Waveforms

This paper discusses the direction-of-arrival (DOA) estimation problem in multiple-input multiple-output (MIMO) radar with non-orthogonal waveforms. Unlike the traditional orthogonal waveforms-based counterparts, spatially colored noise as well as corrupted virtual direction matrix would appear due to the correlated waveforms in DOA estimation. To tackle these issues, an improved propagator estimator is developed. The spatially colored noise caused by waveform imperfectly is eliminated via cross-correlation technique. Thereafter, DOA is obtained by exploiting the propagator and invariant techniques. The proposed estimator does not involve eigendecomposition of the high dimensional matrix, and it provides closed-form solution for DOA estimation. Compared with the existing frameworks, it is computationally much more economic. Moreover, it offers very close DOA estimation accuracy to the matrix-based methods. Numerical simulations are carried out to show the improvement of the proposed estimator.