Fingerprint identification systems have been widely deployed in many occasions of our daily ***,together with many advantages,they are still vulnerable to the presentation attack(PA)by some counterfeit *** address cha...
详细信息
Fingerprint identification systems have been widely deployed in many occasions of our daily ***,together with many advantages,they are still vulnerable to the presentation attack(PA)by some counterfeit *** address challenges from PA,fingerprint liveness detection(FLD)technology has been proposed and gradually attracted people’s *** vast majority of the FLD methods directly employ convolutional neural network(CNN),and rarely pay attention to the problem of overparameterization and over-fitting of models,resulting in large calculation force of model deployment and poor model *** at filling this gap,this paper designs a lightweight multi-scale convolutional neural network method,and further proposes a novel hybrid spatial pyramid pooling block to extract abundant features,so that the number of model parameters is greatly reduced,and support multi-scale true/fake fingerprint ***,the representation self-challenge(RSC)method is used to train the model,and the attention mechanism is also adopted for optimization during execution,which alleviates the problem of model over-fitting and enhances generalization of detection ***,experimental results on two publicly benchmarks:LivDet2011 and LivDet2013 sets,show that our method achieves outstanding detection results for blind materials and *** size of the model parameters is only 548 KB,and the average detection error of cross-sensors and cross-materials are 15.22 and 1 respectively,reaching the highest level currently available.
As the fifth-generation(5G)mobile communication network may not meet the requirements of emerging technologies and applications,including ubiquitous coverage,industrial internet of things(IIoT),ubiquitous artificial i...
详细信息
As the fifth-generation(5G)mobile communication network may not meet the requirements of emerging technologies and applications,including ubiquitous coverage,industrial internet of things(IIoT),ubiquitous artificial intelligence(ai),digital twins(DT),etc.,this paper aims to explore a novel space-air-ground integrated network(SAGIN)architecture to support these new requirements for the sixth-generation(6G)mobile communication network in a flexible,low-latency and efficient ***,we first review the evolution of the mobile communication network,followed by the application and technology requirements of *** the current 5G non-terrestrial network(NTN)architecture in supporting the new requirements is deeply *** that,we proposes a new flexible,low-latency and flat SAGIN architecture,and presents corresponding use ***,the future research directions are discussed.
Nowadays, Alzheimer's disease is one of the most severe threats to people of all ages and socioeconomic backgrounds. Their everyday activities may measure this disorder, and how they speak about it shows how conce...
详细信息
This work considers an important problem of identifying the dynamics of chemical reaction networks from time-series data. We propose an approach to identify complex chemical reaction networks (CRN) from concentration ...
This work considers an important problem of identifying the dynamics of chemical reaction networks from time-series data. We propose an approach to identify complex chemical reaction networks (CRN) from concentration data using the concept of sparse model identification. Particularly, we demonstrate challenges associated with the application of the sparse identification of nonlinear dynamics (SINDy) and its variants to data obtained from CRNs. We develop a SINDy-CRN algorithm based on the properties of CRNs for identifying governing equations of a CRN. The proposed algorithm is illustrated using a numerical simulation example.
Counterfactual estimation from observations represents a critical endeavor in numerous application fields, such as healthcare and finance, with the primary challenge being the mitigation of treatment bias. The balanci...
详细信息
Counterfactual estimation from observations represents a critical endeavor in numerous application fields, such as healthcare and finance, with the primary challenge being the mitigation of treatment bias. The balancing strategy aimed at reducing covariate disparities between different treatment groups serves as a universal solution. However, when it comes to the time series data, the effectiveness of balancing strategies remains an open question, with a thorough analysis of the robustness and applicability of balancing strategies still lacking. This paper revisits counterfactual estimation in the temporal setting and provides a brief overview of recent advancements in balancing strategies. More importantly, we conduct a critical empirical examination for the effectiveness of the balancing strategies within the realm of temporal counterfactual estimation in various settings on multiple datasets. Our findings could be of significant interest to researchers and practitioners and call for a reexamination of the balancing strategy in time series settings. Copyright 2024 by the author(s)
The use of MRI scans to identify and categorise brain tumors is the main focus of this study. The goal is to develop a precise and reliable method for early identification and accurate categorisation of brain tumours,...
The use of MRI scans to identify and categorise brain tumors is the main focus of this study. The goal is to develop a precise and reliable method for early identification and accurate categorisation of brain tumours, including pituitary neoplasms, meningiomas, and gliomas. Deep learning methods were utilized to analyze MRI image databases, resulting in a 96% identification accuracy for brain tumors and a 98% categorization accuracy for the three types. This research demonstrates the potential of deep learning techniques for accurate brain tumor identification and categorization. The early identification and precise categorization of brain tumors can assist medical professionals in making informed decisions about the best treatment options for patients, leading to better outcomes and survival rates. The article provides an extensive review of current methods for identifying and classifying brain tumors using MRI data. It emphasizes the critical importance of this field of study in improving patient outcomes and reducing unnecessary treatments.
Feature extraction plays a critical role in text classification, as it converts textual data into numerical representations suitable for machine learning models. A key challenge lies in effectively capturing both sema...
详细信息
Effective blood glucose forecasting is crucial for detecting events such as hypo- or hyperglycemia in people with diabetes, yet remains challenging in domains with only small, heterogeneous datasets, such as in the pe...
详细信息
Urban rail transit (URT) is vulnerable to natural disasters and social emergencies including fire, storm and epidemic (such as COVID-19), and real-time origin-destination (OD) flow prediction provides URT operators wi...
详细信息
Identifying cell types and understanding their functional properties is crucial for unraveling the mechanisms underlying perception and cognition. In the retina, functional types can be identified by carefully selecte...
详细信息
暂无评论