Flow-Attention-based Spatio-Temporal Aggregation Network for 3D Mask Detection

Anti-spoofing detection has become a necessity for face recognition systems due to the security threat posed by spoofing attacks. Despite great success in traditional attacks, most deep-learning-based methods perform poorly in 3D masks, which can highly simulate real faces in appearance and structure, suffering generalizability insufficiency while focusing only on the spatial domain with single frame input. This has been mitigated by the recent introduction of a biomedical technology called rPPG (remote photoplethysmography). However, rPPG-based methods are sensitive to noisy interference and require at least one second (> 25 frames) of observation time, which induces high computational overhead. To address these challenges, we propose a novel 3D mask detection framework, called FASTEN (Flow-Attention-based Spatio-Temporal aggrEgation Network). We tailor the network for focusing more on fine-grained details in large movements, which can eliminate redundant spatio-temporal feature interference and quickly capture splicing traces of 3D masks in fewer frames. Our proposed network contains three key modules: 1) a facial optical flow network to obtain non-RGB inter-frame flow information; 2) flow attention to assign different significance to each frame; 3) spatio-temporal aggregation to aggregate high-level spatial features and temporal transition features. Through extensive experiments, FASTEN only requires five frames of input and outperforms eight competitors for both intra-dataset and cross-dataset evaluations in terms of multiple detection metrics. Moreover, FASTEN has been deployed in real-world mobile devices for practical 3D mask detection.Comment: 13 pages, 5 figures. Accepted to NeurIPS 202

Use and Cost of Hospitalization in Dementia: Longitudinal Results from a Community-Based Study

OBJECTIVES: The aim of this study is to examine the relative contribution of functional impairment and cognitive deficits on risk of hospitalization and costs. METHODS: A prospective cohort of Medicare beneficiaries aged 65 and older who participated in the Washington Heights-Inwood Columbia Aging Project (WHICAP) were followed approximately every 18 months for over 10 years (1805 never diagnosed with dementia during study period, 221 diagnosed with dementia at enrollment). Hospitalization and Medicare expenditures data (1999-2010) were obtained from Medicare claims. Multivariate analyses were conducted to examine (1) risk of all-cause hospitalizations, (2) hospitalizations from ambulatory care sensitive (ACSs) conditions, (3) hospital length of stay (LOS), and (4) Medicare expenditures. Propensity score matching methods were used to reduce observed differences between demented and non-demented groups at study enrollment. Analyses took into account repeated observations within each individual. RESULTS: Compared to propensity-matched individuals without dementia, individuals with dementia had significantly higher risk for all-cause hospitalization, longer LOS, and higher Medicare expenditures. Functional and cognitive deficits were significantly associated with higher risks for hospitalizations, hospital LOS, and Medicare expenditures. Functional and cognitive deficits were associated with higher risks of for some ACS but not all admissions. CONCLUSIONS: These results allow us to differentiate the impact of functional and cognitive deficits on hospitalizations. To develop strategies to reduce hospitalizations and expenditures, better understanding of which types of hospitalizations and which disease characteristics impact these outcomes will be critical

A method for detecting abnormal behavior of ships based on multi-dimensional density distance and an abnormal isolation mechanism

Abnormal ship behavior detection is essential for maritime navigation safety. Most existing abnormal ship behavior detection methods only build A ship trajectory position outlier detection model; however, the construction of a ship speed outlier detection model is also significant for maritime navigation safety. In addition, in most existing methods for detecting a ship's abnormal behavior based on abnormal thresholds, one unsuitable threshold leads to the risk of the ship not being minimized as much as possible. In this paper, we proposed an abnormal ship behavior detection method based on distance measurement and an isolation mechanism. First, to address the problem of traditional trajectory compression methods and density clustering methods only using ship position information, the minimum description length principle based on acceleration (AMDL) algorithm and Multi-Dimensional Density Clustering (MDDBSCAN) algorithm is used in this study. These algorithms not only considered the position information of the ship, but also the speed information. Second, regarding the issue of the difficulty in determining the anomaly threshold, one method for determining the anomaly threshold based on the relationship between the velocity weights and noise points of the MDDBSCAN algorithm has been introduced. Finally, due to the randomness issue of the selected segmentation value in iForest, a strategy of selectively constructing isolated trees was proposed, thus further improving the efficiency of abnormal ship behavior detection. The experimental results on the historical automatic identification system data set of Xiamen port prove the practicality and effectiveness of our proposed method. Our experiment results show that the proposed method achieves an improvement of about 10% over the trajectory outlier detection based on the local outlier fraction method, about 14% over the isolation-based online anomalous trajectory method in terms of the accuracy of ship position information anomaly detection, and about 3% over the feature fusion method in terms of the accuracy of ship speed anomaly detection. This method improves algorithm efficiency by about 5% compared to the traditional isolation forest anomaly detection algorithm

Medicaid Contributes Substantial Costs to Dementia Care in an Ethnically Diverse Community

Objectives: The main objective of this study was to estimate effects of dementia on Medicaid expenditures in an ethnically diverse community. Methods: The sample included 1,211 Medicare beneficiaries who did not have any Medicaid coverage and 568 who additionally had full Medicaid coverage enrolled in the Washington Heights-Inwood Columbia Aging Project (WHICAP), a multiethnic, population-based, prospective study of cognitive aging in northern Manhattan (1999–2010). Individuals’ dementia status was determined using a rigorous clinical protocol. Relationship between dementia and Medicaid coverage and expenditures were estimated using a two-part model. Results: In participants who had full Medicaid coverage, average annual Medicaid expenditures were substantially higher for those with dementia than those without dementia ($50,270 vs.$21,966, p < .001), but Medicare expenditures did not differ by dementia status ($8,458 vs.$9,324, p = .19). In participants who did not have any Medicaid coverage, average annual Medicare expenditures were substantially higher for those with dementia than those without dementia ($12,408 vs.$8,113, p = .02). In adjusted models, dementia was associated with a $6,278 increase in annual Medicaid spending per person after controlling for other characteristics. Discussion: Results highlight Medicaid’s contribution to covering the cost of dementia care in addition to Medicare. Studies that do not include Medicaid are unlikely to accurately reflect the true cost of dementia

Dependence Clusters in Alzheimer Disease and Medicare Expenditures: A Longitudinal Analysis From the Predictors Study

Introduction: Dependence in Alzheimer disease has been proposed as a holistic, transparent, and meaningful representation of disease severity. Modeling clusters in dependence trajectories can help understand changes in disease course and care cost over time. Methods: Sample consisted of 199 initially community-living patients with probable Alzheimer disease recruited from 3 academic medical centers in the United States followed for up to 10 years and had ≥2 Dependence Scale recorded. Nonparametric K-means cluster analysis for longitudinal data (KmL) was used to identify dependence clusters. Medicare expenditures data (1999-2010) were compared between clusters. Results: KmL identified 2 distinct Dependence Scale clusters: (A) high initial dependence, faster decline, and (B) low initial dependence, slower decline. Adjusting for patient characteristics, 6-month Medicare expenditures increased over time with widening between-cluster differences. Discussion: Dependence captures dementia care costs over time. Better characterization of dependence clusters has significant implications for understanding disease progression, trial design and care planning

The Predictors Study: Development and Baseline Characteristics of the Predictors 3 Cohort

INTRODUCTION: The Predictors study was designed to predict the length of time to major disease outcomes in Alzheimer's disease (AD) patients. Here, we describe the development of a new, Predictors 3, cohort. METHODS: Patients with prevalent or incident AD and individuals at-risk for developing AD were selected from the North Manhattan community and followed annually with instruments comparable to those used in the original two Predictors cohorts. RESULTS: The original Predictors cohorts were clinic based and racially/ethnically homogenous (94% white, 6% black; 3% Hispanic). In contrast, the 274 elders in this cohort are community-based and ethnically diverse (39% white, 40% black, 21% other; 78% Hispanic). Confirming previous observations, psychotic features were associated with poorer function and mental status and extrapyramidal signs with poorer function. DISCUSSION: This new cohort will allow us to test observations made in our original clinic-based cohorts in patients that may be more representative of the general community

Opinion mining in social media

Nowadays, social networking sites like Twitter, Facebook, YouTube have gained so much popularity and they had become a significant part of our daily life. People tend to share their ideas on social media platforms and massive amounts of data are generated daily. The mining of sentiments expressed in huge opinionated text data has become an increasingly popular research field. The unstructured and huge volume of social media posts makes the data extremely challenging to analyse with high accuracy and efficiency. In previous researches done, most of the existing computational models for classifying sentiments from informal documents relied heavily on machine learning techniques. Feature selection is the key step in training the sentiment classifier with machine learning techniques, however, there is no definite conclusion on how to choose the features that can enhance the performance of classifiers in terms of accuracy and computation time. In this work, experiments were conducted using both machine-learning-based approaches and hybrid approaches to explore the importance of feature selection in sentiment analysis. A near-linear relationship had been observed between log(no_of_features) and classifier accuracy. When the accuracy exceeded the maximum point, a slowly decreasing trend was observed. Results obtained were validated and similar trends existed in both binary and multiclass classification. Hybrid approaches had also been applied and the accuracies were compared with machine- learning-based methods. The results of the comparison further proved that the performance of a classifier could be enhanced with proper feature selection.Bachelor of Engineering (Electrical and Electronic Engineering

An Adaptive Routing-Forwarding Control Scheme Based on an Intelligent Fuzzy Decision-Making System for Opportunistic Social Networks

Routing selection in opportunistic social networks is a complex and challenging issue due to intermittent communication connections among mobile devices and dynamic network topologies. The structural characteristics of opportunistic social networks indicate that the social attributes of mobile nodes play a significant role on data dissemination. To this end, in this paper, we propose an adaptive routing-forwarding control scheme (FPRDM) based on an intelligent fuzzy decision-making system. On the foundation of the conception of fuzzy inference logic, two techniques are used in the proposed routing algorithm. Information fusion of social characteristics of message users and node identification are implemented based on the fuzzy recognition strategy, and the fuzzy decision-making mechanism is applied to control message replication and optimize data transmission. Simulation results demonstrate that, in the best case, the proposed scheme presents an average delivery ratio of 0.8, reduces the average end-to-end delay by nearly 45% as compared with the Epidemic routing protocol, and lowers the network overhead by about 75% as compared to the Spray and Wait routing algorithm

Economic growth, distribution policy and other factors: key elements in poverty alleviation

Poverty, as a universal problem, has been widely concerned by scholars all over the world. This essay argues how economic growth, distribution policy and other factors affect poverty reduction issues. Firstly, this article will briefly introduce the context of the poverty issue and how economic growth influences poverty reduction. Secondly, two exceptional cases and three non-economic factors will be analyzed to test whether distribution policy and other factors can also influence or dismiss poverty. Finally, this article will conclude that rapid economic growth is good for poverty alleviation but not all the needs. The distribution policies and other non-economic factors may also influence the link between economic growth and poverty reduction