Miracle-dz/UniFAR-FTUs huggingface dataset:
An approach For Face Liveness Detection and Face Recognition Using Machine Learning Algorithm

: Today’s security purpose, face recognition system approach is used in biometric systems, face recognition is widely used for security purposes as compared to the other systems that are used in security purposes. Face recognition systems are also easily spoofed and unsafe for our security system because face recognition can be done by the images, video frames. Images or videos of a person can be easily available from social media or the internet and can be easily downloaded. Also, pictures of a person can be captured from some distance. As there are more applications, where face recognition is used, this act of sealing and identifying is becoming more serious as people think. To protect from this kind of scams and spoofing, liveness detection technology has been implemented in our system.
Doing Good and Doing it with (Investment) Style

We study the asset allocation, spending behavior, fees, and investment performance of U.S. private foundations from 1991 to 2016. We find that large foundations generate positive risk-adjusted returns of about one percent per year. We document considerable time series variation in alphas and weakening performance persistence. Larger and more sophisticated foundations perform better and invest more aggressively. Foundations with concentrated stock holdings have higher returns, but they also take on more risk. Because of the constraints imposed by the five percent spending rule and accommodating monetary policy, private foundations also increase their risk-taking and reach for yield. Due to these constraints, a conservative asset allocation will decrease real principal balances over time resulting in less charitable giving.
Adaptive Ensemble Q-learning: Minimizing Estimation Bias via Error Feedback

The ensemble method is a promising way to mitigate the overestimation issue in Q-learning, where multiple function approximators are used to estimate the action values. It is known that the estimation bias hinges heavily on the ensemble size (i.e., the number of Q-function approximators used in the target), and that determining the `right' ensemble size is highly nontrivial, because of the time-varying nature of the function approximation errors during the learning process. To tackle this challenge, we first derive an upper bound and a lower bound on the estimation bias, based on which the ensemble size is adapted to drive the bias to be nearly zero, thereby coping with the impact of the time-varying approximation errors accordingly. Motivated by the theoretic findings, we advocate that the ensemble method can be combined with Model Identification Adaptive Control (MIAC) for effective ensemble size adaptation. Specifically, we devise Adaptive Ensemble Q-learning (AdaEQ), a generalized ensemble method with two key steps: (a) approximation error characterization which serves as the feedback for flexibly controlling the ensemble size, and (b) ensemble size adaptation tailored towards minimizing the estimation bias. Extensive experiments are carried out to show that AdaEQ can improve the learning performance than the existing methods for the MuJoCo benchmark.
Self-Reported Risky Driving in Relation to Changes in Amount of Driving During the COVID-19 Pandemic

The number and rate of traffic fatalities in the United States have increased significantly during the COVID-19 pandemic, despite a large reduction in the total number of miles driven on U.S. roads. Increases in crashes involving driver impairment, speeding, and seatbelt non-use have been observed; however, the reasons why such risky driving behaviors and negative traffic safety outcomes increased during the pandemic remain unclear. The current study examines data from a survey of driving behavior among the American public conducted in the fall of 2020, to investigate the relationship between respondents’ self-reported risky driving behaviors and changes in how much they drove during the pandemic. While only an estimated 4% of U.S. drivers reported that they increased their driving due to the pandemic, they tended to be younger than those who reported not changing or decreasing how much they drove, and a greater proportion were male. They were also more likely to report engaging in a wide array of risky driving behaviors including distracted driving, speeding, aggressive driving, substance-impaired driving, and seatbelt non-use. Importantly, their higher rates of risky behaviors were not fully explained by their demographic characteristics nor their frequency of driving. Results thus suggest that at least some of the increase in crash rates observed during the COVID 19 pandemic may be attributable to an increase in driving among a small subset of drivers with both higher baseline risk and greater propensity to engage in risky driving behaviors. Although these data do not indicate the reasons why a small subset of drivers increased their driving nor why they are more likely to engage in risky behaviors, they provide some insights into the increased risks on U.S. roads during a period when aggregate metrics of travel showed decreases.
Development of Coronal Holes Index For Study of Coronal Hole Geo-Effectiveness

Coronal holes are expansion regions in the solar corona with lower density and temperature than their surroundings. Coronal holes have open magnetic field structure that radiates out into interplanetary space as the solar wind. During minimum solar activity, coronal holes play important role in space weather because they are sources of high speed-streams. Therefore, studying the geo-effectiveness of coronal holes is very important. This paper presented a development of the coronal holes geo-effectiveness index related to the occurrence of geomagnetic storms. The index which was represented as “G(CH)”, is based on the combination of center position and cross-sectional length of coronal holes surface on latitudinal direction. Using 5 years of data of coronal holes (2015-2020), we analyzed the threshold combinations of the two parameters that can be used to scale the geo-effectiveness of a coronal hole. The results of all CH events with G(CH) > 0 should be considered as geomagnetic storm generator; CH events having the same characteristics as the CME event, i.e. although they have the potential to generate geomagnetic storms, the determining factor is influenced by the behavior and conditions of the solar wind and IMF Bz (-) during reconnection. So, the intensity of geomagnetic storm is strongly influenced by behavior and conditions (value and duration) both solar wind and Bz(-); the duration of CH event with G(CH) > 0 that have the potential to generate geomagnetic storms is a minimum of 6 days after the CH event; CH events accompanied by CIR affect the intensity and duration of the geomagnetic storms they generate, and the minimum value of G(CH) = 2.21.
Commons People: Additive Manufacturing Enabled Collaborative Commons Production

“Never doubt that a small group of thoughtful, committed citizens can change the world; indeed, it’s the only thing that ever has. ” – Margaret Mead In a post industrial age of production the incumbent model of mass manufacture characterized by integrated organizations and economies of scale could be showing signs of giving some way to a new production paradigm. Due to technology advances especially in the field of additive manufacturing (AM) small scale de-centralized production is again a possibility. Where once the tools to produce goods were seen as large capital investment beyond the means of most small enterprises, it is now possible for individuals to access tools that are capable of producing consumer goods. This shift had led to the emergence of a social phenomenon that until now was bound to the digital world. Networks of individuals are coming together to collectively design, develop, manufacture and distribute goods through a network under the banner of an open hardware movement. This paper presents a case study of one such network designing and distributing open source prosthetics using a collaborative commons enabled by AM technology.
Do Firms Have A Preference Order While Repaying Lenders? Relationship vs Transaction Banking

Do firms prefer repaying a relationship lender over a transaction lender or vice versa? It is unclear whether a shock to aggregate default in the economy would show a higher default rate towards the informed relationship lenders or the uninformed arm’s length lenders? A difference in differences analysis shows that firms are more likely to default on relationship lenders compared to arm’s length lenders. Firms default even more on relationship lenders that have helped the firm in the past, indicating that relationship banking may create a soft budget constraint. This effect is observed for under-capitalized and well-capitalized banks, and also for both public and private lenders. The findings are robust to alternate definitions of relationship banking and controlling for the outstanding loan amount.
Second Response to Reviewer #2 of gmd-2018-156

This paper describes the splitting supercell idealized test case used in the 2016 Dynamical Core Model Intercomparison Project (DCMIP2016). These storms are useful testbeds for global atmospheric models because the horizontal scale of convective plumes is O(1km), emphasizing non-hydrostatic dynamics. The test case simulates a supercell on a reduced radius sphere with nominal resolutions ranging from 4km to 0.5km and is based on the work of Klemp et al. (2015). Models are initialized with an atmospheric environment conducive to supercell formation and forced with a small thermal perturbation. 5 A simplified Kessler microphysics scheme is coupled to the dynamical core to represent moist processes. Reference solutions for DCMIP2016 models are presented. Storm evolution is broadly similar between models, although differences in final solution exist. These differences are hypothesized to result from different numerical discretizations, physics-dynamics coupling, and numerical diffusion. Intramodel solutions generally converge as models approach 0.5km resolution, :::::::: although :::::::::: exploratory ::::::::: simulations :: at ::::::: 0.25km ::::: imply ::::: some ::::::::: dynamical :::: cores ::::::: require :::: more ::::::::: refinement :: to ::::: fully :::::::: converge. These results can be used as a 10
Neural State Machine for 2D and 3D Visual Question Answering

This thesis focuses on the Visual Question Answering (VQA) task in 2D images and 3D environments using the Neural State Machine (NSM). The NSM is a state-of-the-art approach for VQA that simulates reasoning over scene-graphs. We re-implement and extend the NSM by adding a narrowing mechanism for localised attention, and by applying bilinear attention on scene graph representations of the input scene. We show that these extensions lead to improved performance on the VQA task in both the 2D and 3D domains. Prior work on VQA has focused on reasoning in the 2D image domain, and has not addressed how the VQA task can be formulated with 3D data. To address the latter domain, we create a 3D VQA dataset based on 3D reconstructions of real environments. Then, we compare the performance of the NSM with common approaches for 3D VQA in on a range of question types. We show that the NSM is competitive with other VQA methods in the 3D domain and our extensions also lead to improved VQA accuracy in the 3D domain.
Detecting Mild Cognitive Impairment Through Digitized Trail-Making Test Interface

With the number of Alzheimer’s patients reaching 5 million in 2014 according to the U.S. Center for Disease Control and Prevention, increasing emphasis has been placed on identifying and understanding its precursor condition, Mild Cognitive Impairment (MCI). MCI is characterized by subtle but abnormal cognitive decline and is challenging to detect without formal testing. Neuropsychologists use paper-and-pencil tests such as the Trail-Making Test (TMT) for diagnosis, and ongoing research places importance on high-granularity sketch data from digital TMTs. We present SmartStrokes, a digital TMT app designed to simulate the paper-and-pencil testing experience on a tablet and stylus. Our contribution frames the principles of digital sketch recognition and Human-Computer Interaction (HCI) into the existing neuropsychological test, outlining the creation of a pair of classiﬁcation models that identify MCI on an individual segmented line basis. Such a per-line classiﬁcation method which could provide localized sketching behavior indicative of MCI. We also present an interface for the digital TMT and a reﬁnement of line segmentation algorithms from previous research to better distinguish between the actions that a participant takes when completing the exam.
Design of the Novel Optoelectronic Logic Gates Based on the Multimode Interference

Novel optoelectronic logic gates based on multimode interference structure have been proposed for the first time. Different from conventional methods, the multimode interference structure is employed to realize the logic gates function, and the proposed logic gates take advantages of the linear operation and the easy-to-implement logic function. In this chapter, the detailed investigations such as configuration of the logic gate and design rules of MMI structure have been considered. Furthermore, the lower loss can be obtained by the simulation results.
A Few-Shot Semantic Parser for Wizard-of-Oz Dialogues with the Precise ThingTalk Representation

Previous attempts to build effective semantic parsers for Wizard-of-Oz (WOZ) conversations suffer from the difficulty in acquiring a high-quality, manually annotated training set. Approaches based only on dialogue synthesis are insufficient, as dialogues generated from state-machine based models are poor approximations of real-life conversations. Furthermore, previously proposed dialogue state representations are ambiguous and lack the precision necessary for building an effective agent.This paper proposes a new dialogue representation and a sample-efficient methodology that can predict precise dialogue states in WOZ conversations. We extended the ThingTalk representation to capture all information an agent needs to respond properly. Our training strategy is sample-efficient: we combine (1) few-shot data sparsely sampling the full dialogue space and (2) synthesized data covering a subset space of dialogues generated by a succinct state-based dialogue model. The completeness of the extended ThingTalk language is demonstrated with a fully operational agent, which is also used in training data synthesis. We demonstrate the effectiveness of our methodology on MultiWOZ 3.0, a reannotation of the MultiWOZ 2.1 dataset in ThingTalk. ThingTalk can represent 98% of the test turns, while the simulator can emulate 85% of the validation set. We train a contextual semantic parser using our strategy, and obtain 79% turn-by-turn exact match accuracy on the reannotated test set.
Method of Swarm Aggregation and Control for Intelligent Robot Based on Three-dimensional Gene Regulatory Network

In recent years, the research on robot swarm aggregation and control has received much attention in the field of intelligent robot, and robot swarm aggregation and control base on Gene Regulatory Network (GRN) model has been a hot topic. Within these research, most studies consider robots into a two-dimensional scene to simplify the aggregation and control model. However, with the demand of practical scenarios and UAV clusters, swarm robots are increasingly applied to complex tasks in three-dimensional environment the traditional Gene Regulatory Network (GRN) model is not feasible to generalize in three-dimensional space. Because the traditional model could not generate suitable robot population aggregation pattern. To this end, the study proposes an method on aggregation and control of swarm intelligent robot based on 3D Gene Regulatory Network. Then, This paper shows a complex scenes to test the performance of the method in three-dimensional space. Furthermore, this paper also uses the V-rep platform to simulate real scenarios and verify the efficacy of the model when the environment added the physics engine. Experimental results show that our control model has good performance and is robust under complex scenarios.
Modern Variational Methods for Semi-Supervised Text Classiﬁcation

In the advent of powerful pretrained feature extractors trained on massive amounts of data, modern architectures such as ELM O and BERT have been shown to signiﬁcantly improve performance in nearly every language task that involves them. Despite what can be achieved through these modern pretraining methods, they fail to accommodate environments in which such methods are not feasible; when researchers are constrained either by the amount of relevant in-domain data, or by their computational resources. Modern architectures also rely heavily on the language modeling objective, which has thus made it the most widely studied objective for feature extraction from unstructured text as a consequence. In this thesis, I instead explore alternative learning methods for feature extraction using variational methods in the context of both the low-resource and high-resource settings. By introducing V AMPIRE , 1 a descendant of topic models, I adapt neural variational document modeling to modern pretraining methods in order to produce powerful feature extractors that are especially motivated when resources are limited. I also explore the notion of joint semi-supervision, the act of unifying both labeled and unlabeled data under a single objec-tive. By studying the phenomena surrounding model pretraining and joint semi-supervision using the variational bound, while providing direct comparisons to strong baselines in both the low-resource and high-resource settings, I provide a comprehensive exposition of the modernization of semi-supervised variational methods using semi-supervised text classiﬁcation as a case study.
Fluid–structure interaction computations for geometrically resolved rotor simulations using CFD

This paper presents a newly developed high-fidelity fluid–structure interaction simulation tool for geometrically resolved rotor simulations of wind turbines. The tool consists of a partitioned coupling between the structural part of the aero-elastic solver HAWC2 and the finite volume computational fluid dynamics (CFD) solver EllipSys3D. The paper shows that the implemented loose coupling scheme, despite a non-conservative force transfer, maintains a sufficient numerical stability and a second-order time accuracy. The use of a strong coupling is found to be redundant. In a first test case, the newly developed coupling between HAWC2 and EllipSys3D (HAWC2CFD) is utilized to compute the aero-elastic response of the NREL 5-MW reference wind turbine (RWT) under normal operational conditions. A comparison with the low-fidelity but state-of-the-art aero-elastic solver HAWC2 reveals a very good agreement between the two approaches. In a second test case, the response of the NREL 5-MW RWT is computed during a yawed and thus asymmetric inflow. The continuous good agreement confirms the qualities of HAWC2CFD but also illustrates the strengths of a computationally cheaper blade element momentum theory (BEM) based solver, as long as the solver is applied within the boundaries of the employed engineering models. Two further test cases encompass flow situations, which are expected to exceed the limits of the BEM model. However, the simulation of the NREL 5-MW RWT during an emergency shut down situation still shows good agreements in the predicted structural responses of HAWC2 and HAWC2CFD since the differences in the computed force signals only persist for an insignificantly short time span. The considerable new capabilities of HAWC2CFD are finally demonstrated by simulating vortex-induced vibrations on the DTU 10-MW wind turbine blade in standstill. Copyright © 2016 John Wiley & Sons, Ltd.
Influence of phosphorus-solubilizing microorganisms and phosphate amendments on pearl millet growth and nutrient use efficiency in different soils types

Influence of phosphorus-solubilizing microorganisms and phosphate amendments on pearl millet growth and nutrient use efficiency in different soils types Flávia Cristina dos Santos, Denise Pacheco dos Reis, Eliane Aparecida Gomes, Daniela de Azevedo Ladeira, Antônio Carlos de Oliveira, Izabelle Gonçalves Melo, Fabiane Ferreira de Souza, Bianca Braz Mattos, Cleide Nascimento Campos and Christiane Abreu de Oliveira-Paiva*
A NOVEL DEEP LEARNING ARCHITECTURE FOR VEHICLE MAKE AND MODEL RECOGNITION: A REVIEW

: : Vehicles make and model recognition (VMMR) is an important component of the Intelligent Transport System (ITS). It plays an essential role in traffic monitoring and vehicle surveillance. This paper is to review different methods for vehicle recognition, different deep learning architecture to check which can give better accuracy. License plate recognition is earliest method for vehicle recognition. After development of technology vehicle make and model recognition is done using different architecture. As there is a similarity occurs in different types of cars there is a chance for false prediction.so the large scale data set and different data augmentation technique is needed to increase the number of images. CNN model is applied to train and test the data set.
Usage of Social Networking Sites and Technological Impact on the Interaction- Enabling Features

Technology mediated social networking site conveniently allows its users to communicate, develop and maintain friendships. The usage pattern of social networking consumes time and exposes users profile information to online threat. Increasing security concern necessitate for a study especially as the population of the mostly used online social networking site has rapidly increased to about 4.545 billion users spending an average time of 2 hours 43 seconds daily. The technological impact of social networking site reviewed focuses onthe information used for communication, interaction and to socialize with other users. The study highlighted alternative pathway to circumvent online security risks.
Dead-time Compensation of Space Vector PWM Inverter for Induction Motor

A dead-time compensation strategy is presented to compensate dead-time error voltage and eliminate zero- current clamping effect for vector controlled induction motor drive system. The factors influencing dead-time effect are analyzed, and expression of equivalent dead time is deduced. Average dead-time compensation technique is adopted to compensate error voltage at two-axes stationary frame. To improve accuracy of current polarity detection, components of magnetizing current and torque current are transformed into two-axes stationary frame. Therefore, compensating voltage vector can be determined according to the sector where the current vector locates. In addition, a zero-current clamping phenomenon eliminating scheme is adopted combining with the above compensation method to improve the compensation performance. The proposed compensation method is realized with TMS320F2812 DSP chip. Experimental results demonstrate the feasibility of the dead-time compensation method in 11 kW vector controlled induction motor drive system.