scholarly journals Cross-Urban Point-of-Interest Recommendation for Non-Natives

2018 ◽  
Vol 15 (3) ◽  
pp. 82-102 ◽  
Author(s):  
Tao Xu ◽  
Yutao Ma ◽  
Qian Wang
Keyword(s):  
Human Mobility ◽  
The United States ◽  
Behavioral Patterns ◽  
Point Of Interest ◽  
Machine Learning Model ◽  
Proposed Model ◽  
Mobility Behavior ◽  
Personal Assistant ◽  
Location Based Social Networks ◽  
And Control

This article describes how understanding human mobility behavior is of great significance for predicting a broad range of socioeconomic phenomena in contemporary society. Although many studies have been conducted to uncover behavioral patterns of intra-urban and inter-urban human mobility, a fundamental question remains unanswered: To what degree is human mobility behavior predictable in new cities—a person has never visited before? Location-based social networks with a large volume of check-in records provide an unprecedented opportunity to investigate cross-urban human mobility. The authors' empirical study on millions of records from Foursquare reveals the motives and behavioral patterns of non-natives in 59 cities across the United States. Inspired by the ideology of transfer learning, the authors also propose a machine learning model, which is designed based on the regularities that they found in this study, to predict cross-urban human whereabouts after non-natives move to new cities. The experimental results validate the effectiveness and efficiency of the proposed model, thus allowing us to predict and control activities driven by cross-urban human mobility, such as mobile recommendation, visual (personal) assistant, and epidemic prevention.

scholarly journals Incorporating Memory-Based Preferences and Point-of-Interest Stickiness into Recommendations in Location-Based Social Networks

2021 ◽  
Vol 10 (1) ◽  
pp. 36
Author(s):  
Hang Zhang ◽  
Mingxin Gan ◽  
Xi Sun
Keyword(s):  
Social Networks ◽  
Human Mobility ◽  
Mobility Patterns ◽  
Point Of Interest ◽  
Poi Recommendation ◽  
Repeat Visit ◽  
Preference Evolution ◽  
Attenuation Mechanism ◽  
Mobility Behavior ◽  
Location Based Social Networks

In location-based social networks (LBSNs), point-of-interest (POI) recommendations facilitate access to information for people by recommending attractive locations they have not previously visited. Check-in data and various contextual factors are widely taken into consideration to obtain people’s preferences regarding POIs in existing POI recommendation methods. In psychological effect-based POI recommendations, the memory-based attenuation of people’s preferences with respect to POIs, e.g., the fact that more attention is paid to POIs that were checked in to recently than those visited earlier, is emphasized. However, the memory effect only reflects the changes in an individual’s check-in trajectory and cannot discover the important POIs that dominate their mobility patterns, which are related to the repeat-visit frequency of an individual at a POI. To solve this problem, in this paper, we developed a novel POI recommendation framework using people’s memory-based preferences and POI stickiness, named U-CF-Memory-Stickiness. First, we used the memory-based preference-attenuation mechanism to emphasize personal psychological effects and memory-based preference evolution in human mobility patterns. Second, we took the visiting frequency of POIs into consideration and introduced the concept of POI stickiness to identify the important POIs that reflect the stable interests of an individual with respect to their mobility behavior decisions. Lastly, we incorporated the influence of both memory-based preferences and POI stickiness into a user-based collaborative filtering framework to improve the performance of POI recommendations. The results of the experiments we conducted on a real LBSN dataset demonstrated that our method outperformed other methods.

scholarly journals Medical Capacity Shortages Facilitated the Rapid Dissemination of COVID-19 in Wuhan, New York State, and Italy

2021 ◽  
Author(s):  
Yuehao Xu ◽  
Cheng Zhang ◽  
Lixian Qian
Keyword(s):  
New York ◽  
New York State ◽  
The United States ◽  
Policy Makers ◽  
York State ◽  
Proposed Model ◽  
Rapid Dissemination ◽  
City State ◽  
And Control ◽  
The Impact

Abstract Background: During the coronavirus disease 2019 (COVID-19) outbreak, every public health system faced the potential challenge of medical capacity shortages. Infections without timely diagnosis or treatment may facilitate the stealth transmission and spread of the virus. Important as the influence of capacity shortages on the epidemic, it is still unclear how they could intensify the spread of the epidemic qualitatively under different circumstances. Our study aims to throw light on this influence.Methods: Using infection and medical capacity information reported in Wuhan in China, New York State in the United States, and Italy, we developed a dynamic susceptible–exposed–infected–recovered (SEIR) model to estimate the impact of medical capacity shortages during the COVID-19 outbreak at the city, state, and country levels.Results: The proposed model can fit data well (R-square > 0.9). Through sensitivity analysis, we found that doubled capacity would lead to a 39% lower peak infected number in Wuhan. Italy and New York State have similar results.Conclusions: The less shortages in medical capacity, the faster decline in the daily infection numbers and the fewer deaths, and more shortage would lead to steepen infection curve. This study provides a method for estimating potential shortages and explains how they may dynamically facilitate disease spreading during future pandemics such as COVID-19. Based on this, policy makers may figure out some way to explore more medical capacity and control the epidemic better.

scholarly journals Forecasting COVID-19 Outbreak Progression Using Hybrid Polynomial-Bayesian Ridge Regression Model

2020 ◽  
Author(s):  
Mohd Saqib
Keyword(s):  
Ridge Regression ◽  
Good Accuracy ◽  
The United States ◽  
Degree Polynomial ◽  
The Public ◽  
Probabilistic Distribution ◽  
Machine Learning Model ◽  
Proposed Model ◽  
Hybrid Machine ◽  
Public Datasets

Abstract In 2020, Coronavirus Disease 2019 (COVID-19), caused by the SARS-CoV-2 (Severe Acute Respiratory Syndrome Corona Virus 2) Coronavirus, unforeseen pandemic put humanity at big risk and health professionals are facing several kinds of problem due to rapid growth of confirmed cases. That is why some prediction methods are required to estimate the magnitude of infected cases and masses of studies on distinct methods of forecasting are represented so far. In this study, we proposed a hybrid machine learning model that is not only predicted with good accuracy but also takes care of uncertainty of predictions. The model is formulated using Bayesian Ridge Regression hybridized with an n-degree Polynomial and uses probabilistic distribution to estimate the value of the dependent variable instead of using traditional methods. This is a completely mathematical model in which we have successfully incorporated with prior knowledge and posterior distribution enables us to incorporate more upcoming data without storing previous data. Also, L2 (Ridge) Regularization is used to overcome the problem of overfitting. To justify our results, we have presented case studies of three countries, –the United States, Italy, and Spain. In each of the cases, we fitted the model and estimate the number of possible causes for the upcoming weeks. Our forecast in this study is based on the public datasets provided by John Hopkins University available until 11th May 2020. We are concluding with further evolution and scope of the proposed model.

When Temporal Orbits Collide: Embodied Trans Temporalities in U.S. Prisons

Somatechnics ◽  
2017 ◽  
Vol 7 (1) ◽  
pp. 74-94 ◽  
Author(s):  
Rae Rosenberg
Keyword(s):  
United States ◽  
Case Studies ◽  
The United States ◽  
Industrial Complex ◽  
Prison Industrial Complex ◽  
Trans Women ◽  
And Control

This paper explores trans temporalities through the experiences of incarcerated trans feminine persons in the United States. The Prison Industrial Complex (PIC) has received increased attention for its disproportionate containment of trans feminine persons, notably trans women of colour. As a system of domination and control, the PIC uses disciplinary and heteronormative time to dominate the bodies and identities of transgender prisoners by limiting the ways in which they can express and experience their identified and embodied genders. By analyzing three case studies from my research with incarcerated trans feminine persons, this paper illustrates how temporality is complexly woven through trans feminine prisoners' experiences of transitioning in the PIC. For incarcerated trans feminine persons, the interruption, refusal, or permission of transitioning in the PIC invites several gendered pasts into a body's present and places these temporalities in conversation with varying futures as the body's potential. Analyzing trans temporalities reveals time as layered through gender, inviting multiple pasts and futures to circulate around and through the body's present in ways that can be both harmful to, and necessary for, the assertion and survival of trans feminine identities in the PIC.

A STRATEGY FOR MONITORING THE IMPACTS OF COMBINED SEWER OVERFLOWS ON THE OHIO RIVER

1994 ◽  
Vol 30 (1) ◽  
pp. 167-175
Author(s):  
Alan H. Vicory ◽  
Peter A. Tennant
Keyword(s):  
National Policy ◽  
The United States ◽  
Ohio River ◽  
Combined Sewer Overflows ◽  
Treatment Technology ◽  
Design Storm ◽  
Combined Sewer ◽  
Sewer Overflows ◽  
Definition Of ◽  
And Control

With the attainment of secondary treatment by virtually all municipal discharges in the United States, control of water pollution from combined sewer overflows (CSOs) has assumed a high priority. Accordingly, a national strategy was issued in 1989 which, in 1993, was expanded into a national policy on CSO control. The national policy establishes as an objective the attainment of receiving water quality standards, rather than a design storm/treatment technology based approach. A significant percentage of the CSOs in the U.S. are located along the Ohio River. The states along the Ohio have decided to coordinate their CSO control efforts through the Ohio River Valley Water Sanitation Commission (ORSANCO). With the Commission assigned the responsibility of developing a monitoring approach which would allow the definition of CSO impacts on the Ohio, research by the Commission found that very little information existed on the monitoring and assessment of large rivers for the determination of CSO impacts. It was therefore necessary to develop a strategy for coordinated efforts by the states, the CSO dischargers, and ORSANCO to identify and apply appropriate monitoring approaches. A workshop was held in June 1993 to receive input from a variety of experts. Taking into account this input, a strategy has been developed which sets forth certain approaches and concepts to be considered in assessing CSO impacts. In addition, the strategy calls for frequent sharing of findings in order that the data collection efforts by the several agencies can be mutually supportive and lead to technically sound answers regarding CSO impacts and control needs.

AN EFFICIENT MACHINE LEARNING MODEL FOR PREDICTION OF ACUTE MYOCARDIAL INFARCTION

2020 ◽  
Vol 13 ◽  
Author(s):  
Dhilsath Fathima.M ◽  
S. Justin Samuel ◽  
R. Hari Haran
Keyword(s):  
Machine Learning ◽  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Logistic Regression ◽  
Decision Tree ◽  
Learning Model ◽  
Training Dataset ◽  
Data Set ◽  
Machine Learning Model ◽  
Proposed Model

Aim: This proposed work is used to develop an improved and robust machine learning model for predicting Myocardial Infarction (MI) could have substantial clinical impact. Objectives: This paper explains how to build machine learning based computer-aided analysis system for an early and accurate prediction of Myocardial Infarction (MI) which utilizes framingham heart study dataset for validation and evaluation. This proposed computer-aided analysis model will support medical professionals to predict myocardial infarction proficiently. Methods: The proposed model utilize the mean imputation to remove the missing values from the data set, then applied principal component analysis to extract the optimal features from the data set to enhance the performance of the classifiers. After PCA, the reduced features are partitioned into training dataset and testing dataset where 70% of the training dataset are given as an input to the four well-liked classifiers as support vector machine, k-nearest neighbor, logistic regression and decision tree to train the classifiers and 30% of test dataset is used to evaluate an output of machine learning model using performance metrics as confusion matrix, classifier accuracy, precision, sensitivity, F1-score, AUC-ROC curve. Results: Output of the classifiers are evaluated using performance measures and we observed that logistic regression provides high accuracy than K-NN, SVM, decision tree classifiers and PCA performs sound as a good feature extraction method to enhance the performance of proposed model. From these analyses, we conclude that logistic regression having good mean accuracy level and standard deviation accuracy compared with the other three algorithms. AUC-ROC curve of the proposed classifiers is analyzed from the output figure.4, figure.5 that logistic regression exhibits good AUC-ROC score, i.e. around 70% compared to k-NN and decision tree algorithm. Conclusion: From the result analysis, we infer that this proposed machine learning model will act as an optimal decision making system to predict the acute myocardial infarction at an early stage than an existing machine learning based prediction models and it is capable to predict the presence of an acute myocardial Infarction with human using the heart disease risk factors, in order to decide when to start lifestyle modification and medical treatment to prevent the heart disease.

THE DEVELOPMENT OF ACCOUNTING AND INTERNAL CONTROL FOR THE NATIONAL LAND SYSTEM OF THE USA

1994 ◽  
Vol 21 (1) ◽  
pp. 189-213
Author(s):  
Michael P. Schoderbek
Keyword(s):  
United States ◽  
Internal Control ◽  
The United States ◽  
Record Keeping ◽  
Governmental Accounting ◽  
Land System ◽  
The Usa ◽  
And Control

This paper examines the early accounting practices that were used to administer the United States' national land system. These practices are of significance because they provide insights on early governmental accounting and they facilitated an orderly settlement of the western territories. The analysis focuses on the record-keeping and control practices that were developed to meet the provisions of the Land Act of 1800 and to account for land office transactions. These accounting procedures were extracted from the correspondence between the Department of the Treasury and the various land officers.

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