The insider on the outside: a novel system for the detection of information leakers in social networks

Confidential information is all too easily leaked by naive users posting comments. In this paper we introduce DUIL, a system for Detecting Unintentional Information Leakers. The value of DUIL is in its ability to detect those responsible for information leakage that occurs through comments posted on news articles in a public environment, when those articles have withheld material non-public information. DUIL is comprised of several artefacts, each designed to analyse a different aspect of this challenge: the information, the user(s) who posted the information, and the user(s) who may be involved in the dissemination of information. We present a design science analysis of DUIL as an information system artefact comprised of social, information, and technology artefacts. We demonstrate the performance of DUIL on real data crawled from several Facebook news pages spanning two years of news articles

Deep Contract Design via Discontinuous Networks

Contract design involves a principal who establishes contractual agreements about payments for outcomes that arise from the actions of an agent. In this paper, we initiate the study of deep learning for the automated design of optimal contracts. We introduce a novel representation: the Discontinuous ReLU (DeLU) network, which models the principal's utility as a discontinuous piecewise affine function of the design of a contract where each piece corresponds to the agent taking a particular action. DeLU networks implicitly learn closed-form expressions for the incentive compatibility constraints of the agent and the utility maximization objective of the principal, and support parallel inference on each piece through linear programming or interior-point methods that solve for optimal contracts. We provide empirical results that demonstrate success in approximating the principal's utility function with a small number of training samples and scaling to find approximately optimal contracts on problems with a large number of actions and outcomes

Pro-social behavior in rats is modulated by social experience

In mammals, helping is preferentially provided to members of one's own group. Yet, it remains unclear how social experience shapes pro-social motivation. We found that rats helped trapped strangers by releasing them from a restrainer, just as they did cagemates. However, rats did not help strangers of a different strain, unless previously housed with the trapped rat. Moreover, pair-housing with one rat of a different strain prompted rats to help strangers of that strain, evidence that rats expand pro-social motivation from one individual to phenotypically similar others. To test if genetic relatedness alone can motivate helping, rats were fostered from birth with another strain and were not exposed to their own strain. As adults, fostered rats helped strangers of the fostering strain but not rats of their own strain. Thus, strain familiarity, even to one's own strain, is required for the expression of pro-social behavior

A Continuous-Time Dynamic Factor Model for Intensive Longitudinal Data Arising from Mobile Health Studies

Intensive longitudinal data (ILD) collected in mobile health (mHealth) studies contain rich information on multiple outcomes measured frequently over time that have the potential to capture short-term and long-term dynamics. Motivated by an mHealth study of smoking cessation in which participants self-report the intensity of many emotions multiple times per day, we propose a dynamic factor model that summarizes the ILD as a low-dimensional, interpretable latent process. This model consists of two submodels: (i) a measurement submodel -- a factor model -- that summarizes the multivariate longitudinal outcome as lower-dimensional latent variables and (ii) a structural submodel -- an Ornstein-Uhlenbeck (OU) stochastic process -- that captures the temporal dynamics of the multivariate latent process in continuous time. We derive a closed-form likelihood for the marginal distribution of the outcome and the computationally-simpler sparse precision matrix for the OU process. We propose a block coordinate descent algorithm for estimation. Finally, we apply our method to the mHealth data to summarize the dynamics of 18 different emotions as two latent processes. These latent processes are interpreted by behavioral scientists as the psychological constructs of positive and negative affect and are key in understanding vulnerability to lapsing back to tobacco use among smokers attempting to quit.Comment: Main text is 19 pages with 4 figures and 1 table. Supporting material is 25 page

Within-Field Rice Yield Estimation Based on Sentinel-2 Satellite Data

[EN] Rice is considered one of the most important crops in the world. According to the Food and Agriculture Organization of the United Nations (FAO), rice production has increased significantly (156%) during the last 50 years, with a limited increase in cultivated area (24%). With the recent advances in remote sensing technologies, it is now possible to monitor rice crop production for a better understanding of its management at field scale to ultimately improve rice yields. In this work, we monitor within-field rice production of the two main rice varieties grown in Valencia (Spain) JSendra and Bomba during the 2020 season. The sowing date of both varieties was May 22-25, while the harvesting date was September 15-17 for Bomba and October 5-8 for JSendra. Rice yield data was collected over 66.03 ha (52 fields) by harvesting machines equipped with onboard sensors that determine the dry grain yield within irregular polygons of 3-7 m width. This dataset was split in two, selecting 70% of fields for training and 30% for validation purposes. Sentinel-2 surface reflectance spectral data acquired from May until September 2020 was considered over the test area at the two different spatial resolutions of 10 and 20 m. These two datasets were combined assessing the best combination of spectral reflectance bands (SR) or vegetation indices (VIs) as well as the best timing to infer final within-field yields. The results show that SR improves the performance of models with VIs. Furthermore, the correlation of each spectral band and VIs with the final yield changes with the dates and varieties. Considering the training data, the best correlation with the yields is obtained on July 4, with R-2 for JSendra of 0.72 at 10 m and 0.76 at 20 m resolution, while the R-2 for Bomba is 0.87 at 10 m and 0.92 at 20 m resolution. Based on the validation dataset, the proposed models provide within-field yield modelling Mean Absolute Error (MAE) of 0.254 t.ha(-1) (Mean Absolute Percentage Error, MAPE, of 3.73%) for JSendra at 10 m (0.240 t.ha(-1); 3.48% at 20 m) and 0.218 t.ha(-1) (MAPE 5.82%) for Bomba (0.223 t.ha(-1); 5.78% at 20 m) on July 4, that is three months before harvest. At parcel level the model's MAE is 0.176 t.ha(-1) (MAPE 2.61%) for JSendra and 0.142 t.ha(-1) (MAPE 4.51%) for Bomba. These results confirm the close correlation between the rice yield and the spectral information from satellite imagery. Additionally, these models provide a timeliness overview of underperforming areas within the field three months before the harvest where farmers can improve their management practices. Furthermore, it highlights the importance of optimum agronomic management of rice plants during the first weeks of rice cultivation (40-50 days after sowing) to achieve high yields.This research was partially funded by the program Generacio Talent of Generalitat Valenciana (CIDEGENT/2018/009).Franch-Gras, B.; San Bautista Primo, A.; Fita-Silvestre, D.; Rubio Michavila, C.; Tarrazó-Serrano, D.; Sánchez, A.; Skakun, S.... (2021). Within-Field Rice Yield Estimation Based on Sentinel-2 Satellite Data. Remote Sensing. 13(20). https://doi.org/10.3390/rs13204095132

Time-varying model of engagement with digital self reporting: Evidence from smoking cessation longitudinal studies

ObjectiveInsufficient engagement is a critical barrier impacting the utility of digital interventions and mobile health assessments. As a result, engagement itself is increasingly becoming a target of studies and interventions. The purpose of this study is to investigate the dynamics of engagement in mobile health data collection by exploring whether, how, and why response to digital self-report prompts change over time in smoking cessation studies.MethodData from two ecological momentary assessment (EMA) studies of smoking cessation among diverse smokers attempting to quit (N = 573) with a total of 65,974 digital self-report prompts. We operationalize engagement with self-reporting in term of prompts delivered and prompt response to capture both broad and more granular engagement in self-reporting, respectively. The data were analyzed to describe trends in prompt delivered and prompt response over time. Time-varying effect modeling (TVEM) was employed to investigate the time-varying effects of response to previous prompt and the average response rate on the likelihood of current prompt response.ResultsAlthough prompt response rates were relatively stable over days in both studies, the proportion of participants with prompts delivered declined steadily over time in one of the studies, indicating that over time, fewer participants charged the device and kept it turned on (necessary to receive at least one prompt per day). Among those who did receive prompts, response rates were relatively stable. In both studies, there is a significant, positive and stable relationship between response to previous prompt and the likelihood of response to current prompt throughout all days of the study. The relationship between the average response rate prior to current prompt and the likelihood of responding to the current prompt was also positive, and increasing with time.ConclusionOur study highlights the importance of integrating various indicators to measure engagement in digital self-reporting. Both average response rate and response to previous prompt were highly predictive of response to the next prompt across days in the study. Dynamic patterns of engagement in digital self-reporting can inform the design of new strategies to promote and optimize engagement in digital interventions and mobile health studies

Multiple imputation of missing data in multilevel ecological momentary assessments: an example using smoking cessation study data

Advances in digital technology have greatly increased the ease of collecting intensive longitudinal data (ILD) such as ecological momentary assessments (EMAs) in studies of behavior changes. Such data are typically multilevel (e.g., with repeated measures nested within individuals), and are inevitably characterized by some degrees of missingness. Previous studies have validated the utility of multiple imputation as a way to handle missing observations in ILD when the imputation model is properly specified to reflect time dependencies. In this study, we illustrate the importance of proper accommodation of multilevel ILD structures in performing multiple imputations, and compare the performance of a multilevel multiple imputation (multilevel MI) approach relative to other approaches that do not account for such structures in a Monte Carlo simulation study. Empirical EMA data from a tobacco cessation study are used to demonstrate the utility of the multilevel MI approach, and the implications of separating participant- and study-initiated EMAs in evaluating individuals’ affective dynamics and urge

Myocardial infarction and stroke associated with diuretic based two drug antihypertensive regimens: population based case-control study

Objective To examine the association of myocardial infarction and stroke incidence with several commonly used two drug antihypertensive treatment regimens