Robust Minutiae Extractor: Integrating Deep Networks and Fingerprint Domain Knowledge

We propose a fully automatic minutiae extractor, called MinutiaeNet, based on deep neural networks with compact feature representation for fast comparison of minutiae sets. Specifically, first a network, called CoarseNet, estimates the minutiae score map and minutiae orientation based on convolutional neural network and fingerprint domain knowledge (enhanced image, orientation field, and segmentation map). Subsequently, another network, called FineNet, refines the candidate minutiae locations based on score map. We demonstrate the effectiveness of using the fingerprint domain knowledge together with the deep networks. Experimental results on both latent (NIST SD27) and plain (FVC 2004) public domain fingerprint datasets provide comprehensive empirical support for the merits of our method. Further, our method finds minutiae sets that are better in terms of precision and recall in comparison with state-of-the-art on these two datasets. Given the lack of annotated fingerprint datasets with minutiae ground truth, the proposed approach to robust minutiae detection will be useful to train network-based fingerprint matching algorithms as well as for evaluating fingerprint individuality at scale. MinutiaeNet is implemented in Tensorflow: https://github.com/luannd/MinutiaeNetComment: Accepted to International Conference on Biometrics (ICB 2018

Bridging the Gaps: A Policy Analysis of Child Human Trafficking in Underserved Populations, its Impact on Mental Illnesses, and Recommendations for Enhancing the Existing System

Extensive research indicates that children who are victims of human and sex trafficking are at an increased risk of developing a variety of mental health illnesses and concerns. This policy analysis aims to pinpoint the deficiencies within the United States\u27 current system and propose evidence-based interventions that can effectively mitigate the negative mental health impact of these individuals. Through a comprehensive literature review and analysis, this study establishes a strong correlation between child trafficking and mental illnesses, predominately in underserved communities. Data interpreted in this paper are collected from published studies, articles, governmental websites, and organizational pages. The proposed recommendations can guide policymakers and legislators to address this public health issue at the grass-roots level based on the identified gaps. Addressing these barriers ensures that individuals who are victims of human trafficking can receive fundamental behavioral health support that will be sustained for years to come

Distinct counting with a self-learning bitmap

Counting the number of distinct elements (cardinality) in a dataset is a fundamental problem in database management. In recent years, due to many of its modern applications, there has been significant interest to address the distinct counting problem in a data stream setting, where each incoming data can be seen only once and cannot be stored for long periods of time. Many probabilistic approaches based on either sampling or sketching have been proposed in the computer science literature, that only require limited computing and memory resources. However, the performances of these methods are not scale-invariant, in the sense that their relative root mean square estimation errors (RRMSE) depend on the unknown cardinalities. This is not desirable in many applications where cardinalities can be very dynamic or inhomogeneous and many cardinalities need to be estimated. In this paper, we develop a novel approach, called self-learning bitmap (S-bitmap) that is scale-invariant for cardinalities in a specified range. S-bitmap uses a binary vector whose entries are updated from 0 to 1 by an adaptive sampling process for inferring the unknown cardinality, where the sampling rates are reduced sequentially as more and more entries change from 0 to 1. We prove rigorously that the S-bitmap estimate is not only unbiased but scale-invariant. We demonstrate that to achieve a small RRMSE value of $\epsilon$ or less, our approach requires significantly less memory and consumes similar or less operations than state-of-the-art methods for many common practice cardinality scales. Both simulation and experimental studies are reported.Comment: Journal of the American Statistical Association (accepted

FACTORS INFLUENCING THE USE OF PHYPHOX SOFTWARE IN PHYSICS TEACHING AT HIGH SCHOOLS IN VIETNAM

In the context of contemporary digital education, the use of Phyphox software to turn smartphones into physics experiment equipment opens new opportunities for physics education in Vietnam, yet there are many challenges and opportunities to be explored. This study aims to evaluate the factors influencing the integration of this technology into teaching through a survey of 48 physics teachers nationwide using an online questionnaire with a 5-point Likert scale. Results show that teachers are confident and willing to adopt new technology, recognizing the positive impact of the software on educational quality and student engagement. However, they face difficulties integrating the software into lessons and lack support from resources and school leadership. The study emphasizes the importance of integrating technology into education and provides a basis for developing more effective teacher support strategies. This research contributes to the theoretical foundation on software use in education and aids policy makers, educational managers, and software developers in shaping strategies to optimize technology use in education, enhancing educational quality and preparing students with the necessary skills for the digital era

On the technical diagnostics of ball-bearing based on modelling of rotor-bearing systems by vibration method

In this paper, after analysing deformations of loading ball-bearing it is able to investigate vibrations of rotor-bearing system as the system with non-linear and periodically varying stiffness. The modelling of the systems is depent on properties of the shaft (rigid or flexible) and on measurement techniques for system vibrations (by eddy current proximity probe for rotating shaft or on non-rotating parts of the rotor-bearing systems) it can be described by a single degree-of-freedom or two-degree-of freedom nonlinear systems subjected to parametric and external excitations. Therefore it is possible to obtain different characteristics of system vibrations with respect to different defects of ball-bearing. These symptoms help us to identify and estimate the bearing quality by measurement and analysis of system vibrations