The Effect of Recency to Human Mobility

In recent years, we have seen scientists attempt to model and explain human dynamics and, in particular, human movement. Many aspects of our complex life are affected by human movements such as disease spread and epidemics modeling, city planning, wireless network development, and disaster relief, to name a few. Given the myriad of applications it is clear that a complete understanding of how people move in space can lead to huge benefits to our society. In most of the recent works, scientists have focused on the idea that people movements are biased towards frequently-visited locations. According to them, human movement is based on an exploration/exploitation dichotomy in which individuals choose new locations (exploration) or return to frequently-visited locations (exploitation). In this work, we focus on the concept of recency. We propose a model in which exploitation in human movement also considers recently-visited locations and not solely frequently-visited locations. We test our hypothesis against different empirical data of human mobility and show that our proposed model is able to better explain the human trajectories in these datasets

Citizen Gain: The Economic Benefits of Naturalization for Immigrants and the Economy

Citizenship brings many benefits to immigrants, the opportunity to participate more fully in our democracy through the right to vote being primary among them. But beyond the clear civic gain is an often overlooked economic benefit: for a variety of reasons, naturalized immigrants are likely to see a boost in their family incomes that can benefit their children, their communities and the nation as a whole.Why is the economic importance of naturalization -- the process by which immigrants become citizens -- so often overlooked? Part of the reason is that much of the heated debate around the economic effects of immigration in the U.S. tends to focus on the unauthorized (or "illegal") population. The economic evidence in this arena points in multiple directions -- positive gains at an aggregate level, negative effects on specific sectors of the labor market, mixed impacts on government coffers -- but lost in that discussion is the fact that nearly three-fourths of all immigrants are either naturalized citizens or Lawful Permanent Residents (LPRs), those who have legal status and may be eligible to naturalize but have not yet done so. What would happen if those individuals who were eligible to naturalize actually chose to do so? How much would their economic situation improve -- and what would be the effects on the overall economy? If such gains are possible, how could policymakers help to encourage even higher rates of naturalization? In this policy brief, SIIS tackles these questions by combining individual-level data from the Census Bureau's 2010 Public Use Microdata Sample (PUMS) with the most recent data on the number of LPRs eligible to naturalize from the U.S. Office of Immigration Statistics (OIS). This brief begins with a review of the literature, drawing out both theory and evidence on why naturalization might be associated with a higher earnings trajectory. The authors then discuss the data used and the regression models developed; and make a number of choices along the way to insure that the estimates presented here are as conservative as possible. The brief then discuss how the wage trajectory might change over time -- benefits would actually accrue over a number of years -- and then examines the possible impacts on aggregate earnings and the overall economy.The brief concludes with a discussion of the policy implications, particularly how these benefits might be made clear to those who have not yet naturalized and how new financial and other vehicles could be used to induce higher levels of naturalization

Neural Models of Temporally Organized Behaviors: Handwriting Production and Working Memory

Advanced Research Projects Agency (ONR N00014-92-J-4015); Office of Naval Research (N00014-91-J-4100, N00014-92-J-1309

History navigation in location-based mobile systems

The aim of this paper is to provide an overview and comparison of concepts that have been proposed to guide users through interaction histories (e.g. for web browsers). The goal is to gain insights into history design that may be used for designing an interaction history for the location-based Tourist Information Provider (TIP) system [8]. The TIP system consists of several services that interact on a mobile device

Older adults' comprehension of speech as interactive domestic alarm system output: A field study

Please contact the publisher for further reprinting or re-use.A rapidly ageing population has led to the development of Interactive Domestic Alarm Systems (IDASs) to assist older adults with independent living. This research considers the use of speech as IDAS output and the impact the domestic environment may have on older adults’ comprehension of speech outputs. This paper introduces IDASs, the benefits of employing speech as a mode of system output and the critical design issue of user comprehension. Extending previous laboratory studies (see Lines & Hone, 2002a, Lines & Hone, 2002b, Lines & Hone, 2002c) a field investigation is reported that considers older adults’ comprehension of speech gender and speech type [natural/synthetic] within the domestic environment. The main findings are discussed and future research directions explored

Explaing portuguese's public administration absenteeism through data mining

Portuguese Public Administration (PPA) is the largest contractor in the country, with 12.8% of the Portugal’s active people working for it. Absenteeism and productivity are mutually connected. Thus, companies from public and private sector should always have it in mind, to prevent flaws in the processes and profit loss. Effectively, the main goal of this study is to understand PPA’s absenteeism, particularly the duration of the worker’s next absence, what leads to it, as well as explaining it, by creating a data mining model that fits the problem. To study PPA’s absenteeism it was collected data from a Human Capital Management (HCM) system, by extracting the annual absenteeism report, for 2016, and queries to the worker’s profile, absenteeism history and job characteristics, resulting in around 59,000 different absence records. Data mining techniques were used to clean the dataset and Recency, Frequency and Monetary (RFM) value methodology to add new variables to the problematic, originating richer information about the worker and the absence itself. Thereafter, the Support Vector Machines (SVM) algorithm was applied for modeling the absence duration in day and a 10-fold cross-validation scheme was adopted to assess and confirm the model’s robustness. Finally, major findings were revealed by this study as features related to the worker’s profile are less relevant than absence related features; the influence of the RFM methodology in this study, which managed to get all its computed variables in the 25th most important features; and the discovery of the most concerning employee profile.A Administração Pública Portuguesa (APP) é o maior contratante do país, englobando 12.8% da população ativa. O absentismo e a produtividade estão mutuamente ligados, logo tanto as empresas dos vários setores devem tê-las em atenção para prevenir falhas nos processos e perda de lucro. Efetivamente, o principal propósito deste estudo é perceber o absentismo na APP, em especial a duração da próxima ausência de um trabalhador, as suas causas e explicá-la, através da criação de um modelo adequado ao problema. Para modelar o absentismo na APP recolheram-se dados de um sistema de gestão de recursos humanos, extraindo o relatório anual de absentismo, para 2016, e dados do perfil do trabalhador, histórico de absentismo e especificações do contrato, resultando em cerca de 59,000 ausências. Por sua vez, foram usadas técnicas de data mining para limpar o conjunto de dados e a metodologia Recency, Frequency and Monetary value (RFM) para adicionar novas variáveis à problemática e obter mais perspetivas sobre o trabalhador e a ausência. De seguida, foi aplicado o algoritmo Support Vector Machines (SVM) para modelar a duração da ausência em dias e um esquema de validação cruzada com 10 folds, que testou e aprovou a robustez do modelo. Por fim, este estudo revelou várias descobertas como: variáveis relacionadas com o perfil do trabalhador são menos relevantes que as relacionadas com a ausência em si; a influência da metodologia RFM neste estudo, que conseguiu ter todas as suas variáveis nas mais importantes; e a descoberta do perfil do trabalhador mais preocupante

All look the same? Diversity of labour market outcomes of Chinese ethnic group populations in the UK

With high average levels of qualifications and pay, ethnic Chinese minorities in the UK are often regarded as a migrant ‘success story’. At the same time, the limited evidence we have suggests that Chinese minorities may face ethnic penalties in the labour market, and that there is considerable heterogeneity within the aggregate Chinese ethnic category. In this paper, we address these issues of labour market outcomes and heterogeneity among UK Chinese using 38 pooled quarters of the UK Labour Force Survey. We show that for both wages and employment there are differences in labour market experience across five distinct Chinese origin groups compared to those similarly qualified in the white majority. Consistent ‘winners’ are Taiwan and Malaysian-born Chinese, while Mainland Chinese and Hong Kong-born experience substantial wage penalties. UK-born Chinese face wage penalties when working in traditional industries, in which they continue to cluster, and unemployment penalties. An important contributory factor to labour market outcomes of the different groups appears to be the extent of their relationship with the ethnic economy. We relate our findings to theories of ethnic embeddedness and enclave economies, as well as to the varying contexts of reception faced by immigrants from different cohorts

Exploring Student Check-In Behavior for Improved Point-of-Interest Prediction

With the availability of vast amounts of user visitation history on location-based social networks (LBSN), the problem of Point-of-Interest (POI) prediction has been extensively studied. However, much of the research has been conducted solely on voluntary checkin datasets collected from social apps such as Foursquare or Yelp. While these data contain rich information about recreational activities (e.g., restaurants, nightlife, and entertainment), information about more prosaic aspects of people's lives is sparse. This not only limits our understanding of users' daily routines, but more importantly the modeling assumptions developed based on characteristics of recreation-based data may not be suitable for richer check-in data. In this work, we present an analysis of education "check-in" data using WiFi access logs collected at Purdue University. We propose a heterogeneous graph-based method to encode the correlations between users, POIs, and activities, and then jointly learn embeddings for the vertices. We evaluate our method compared to previous state-of-the-art POI prediction methods, and show that the assumptions made by previous methods significantly degrade performance on our data with dense(r) activity signals. We also show how our learned embeddings could be used to identify similar students (e.g., for friend suggestions).Comment: published in KDD'1