Geographic differential privacy for mobile crowd coverage maximization

For real-world mobile applications such as location-based advertising and spatial crowdsourcing, a key to success is targeting mobile users that can maximally cover certain locations in a future period. To find an optimal group of users, existing methods often require information about users' mobility history, which may cause privacy breaches. In this paper, we propose a method to maximize mobile crowd's future location coverage under a guaranteed location privacy protection scheme. In our approach, users only need to upload one of their frequently visited locations, and more importantly, the uploaded location is obfuscated using a geographic differential privacy policy. We propose both analytic and practical solutions to this problem. Experiments on real user mobility datasets show that our method significantly outperforms the state-of-the-art geographic differential privacy methods by achieving a higher coverage under the same level of privacy protection

Localization of Continuous Gas Leaks from a Flat-Surface Structure Using an Acoustic Emission Sensor Array

Leak localization is of great importance for pressurized vessels for reasons of safety and maintenance. This paper presents an experimental study using an Acoustic Emission sensor array coupled with a hyperbolic positioning algorithm for continuous leak localization. The study aims to detect continuous CO2 leak from an isotropic flat-surface structure on a pressurized vessel in the Carbon Capture and Storage system. The proposed approach consists of four main stages. In the first stage, empirical mode decomposition is deployed to extract the useful signal from the noise. The second step is concerned with the estimation of the time differences of the sensor array in conjunction with correlation signal processing. The third stage estimates the distance difference between the sensing elements from the measured time differences and wave speed. Finally, a hyperbolic positioning algorithm is used to locate the leak source on the flat-surface structure. Results obtained from experiments on a 100 cm ? 100 cm stainless plate demonstrate that the mean full-scale error in the leak localization is 4.9%

Location privacy-preserving task allocation for mobile crowdsensing with differential geo-obfuscation

In traditional mobile crowdsensing applications, organizers need participants' precise locations for optimal task allocation, e.g., minimizing selected workers' travel distance to task locations. However, the exposure of their locations raises privacy concerns. Especially for those who are not eventually selected for any task, their location privacy is sacrificed in vain. Hence, in this paper, we propose a location privacy-preserving task allocation framework with geo-obfuscation to protect users' locations during task assignments. Specifically, we make participants obfuscate their reported locations under the guarantee of differential privacy, which can provide privacy protection regardless of adversaries' prior knowledge and without the involvement of any third- part entity. In order to achieve optimal task allocation with such differential geo- obfuscation, we formulate a mixed-integer non-linear programming problem to minimize the expected travel distance of the selected workers under the constraint of differential privacy. Evaluation results on both simulation and real-world user mobility traces show the effectiveness of our proposed framework. Particularly, our framework outperforms Laplace obfuscation, a state-of-the-art differential geo-obfuscation mechanism, by achieving 45% less average travel distance on the real-world data

Storyfier: Exploring Vocabulary Learning Support with Text Generation Models

Vocabulary learning support tools have widely exploited existing materials, e.g., stories or video clips, as contexts to help users memorize each target word. However, these tools could not provide a coherent context for any target words of learners' interests, and they seldom help practice word usage. In this paper, we work with teachers and students to iteratively develop Storyfier, which leverages text generation models to enable learners to read a generated story that covers any target words, conduct a story cloze test, and use these words to write a new story with adaptive AI assistance. Our within-subjects study (N=28) shows that learners generally favor the generated stories for connecting target words and writing assistance for easing their learning workload. However, in the read-cloze-write learning sessions, participants using Storyfier perform worse in recalling and using target words than learning with a baseline tool without our AI features. We discuss insights into supporting learning tasks with generative models.Comment: To appear at the 2023 ACM Symposium on User Interface Software and Technology (UIST); 16 pages (7 figures, 23 tables

Izraženost surfaktantnog proteina B u bronhoalveolarnom ispirku terminske novorođenčadi sa sindromom respiracijskog distresa

The aim was to investigate the surfactant protein B (SP-B) expression in the bronchoalveolar lavage fluid (BALF ) of full-term neonates with respiratory distress syndrome (RD S). The enzyme-linked immunosorbent assay was performed to assess SP-B expression in BALF of 60 full-term neonates with RD S and 23 healthy neonates and correlation of SP-B level with RD S classification according to chest x-ray findings and PaO2/FiO2 before mechanical ventilation in neonates with RD S. The SP-B level was significantly lower in the RD S group (17.63±6.80 ng/mL) than in healthy neonates (103.95±6.38 ng/mL) (P<0.001). The SP-B level correlated positively with PaO2/ FiO2 before mechanical ventilation (r=0.838, P<0.001). Moreover, the lower the SP-B level, the more severe was the RD S as determined by chest x-ray (P<0.001). In conclusion, full-term neonates with RD S had reduced SP-B in BALF , which was related to the severity of RD S, suggesting that SP-B supplement may be an effective strategy in the treatment of RD S in full-term neonates.Cilj studije bio je ispitati izraženost surfaktantnog proteina B (SP-B) u bronhoalveolarnom ispirku (BALF ) terminske novorođenčadi sa sindromom respiracijskog distresa (SRD ). Izraženost SP-B određena je testom ELI SA u BALF 60 terminske novorođenčadi sa SRD i 23 zdrave novorođenčadi. Utvrđena je korelacija razine SP-B s klasifikacijom SRD prema rendgenskoj snimci prsišta i vrijednosti PaO2/FiO2 prije mehaničke ventilacije u novorođenčadi sa SRD . U skupini novorođenčadi sa SRD razina SP-B bila je značajno niža (17,63±6,80 ng/mL) od one u zdrave novorođenčadi (103,95±6,38 ng/ mL) (P<0,001). Utvrđena je pozitivna korelacija razine SP-B i PaO2/FiO2 prije mehaničke ventilacije (r=0,838, P<0,001). Štoviše, što je bila niža razina SP-B, to je teži bio SRD procijenjen prema rendgenskoj snimci prsišta (P<0,001). Zaključuje se da terminska novorođenčad sa SRD ima sniženu razinu SP-B u BALF i to je povezano s težinom SRD . Ovi nalazi ukazuju na to da bi dodatak SP-B mogla biti učinkovita strategija u liječenju SRD kod terminske novorođenčadi

Localization of CO2_2 gas leakages through acoustic emission multi-sensor fusion based on wavelet-RBFN modeling

CO$_2$ leakage from transmission pipelines in carbon capture and storage systems may seriously endanger the ecological environment and human health. Therefore, there is a pressing need of an accurate and reliable leak localization method for CO$_2$ pipelines. In this study, a novel method based on the combination of a wavelet packet algorithm and a radial basis function network (RBFN) is proposed to realize the leak location. Multiple acoustic emission (AE) sensors are first deployed to collect leakage signals of CO$_2$ pipelines. The characteristics of the leakage signals from the AE sensors under different pressures are then analyzed in both time and frequency domains. Further, leakage signals are decomposed into three layers using wavelet decomposition theory. Wavelet packet energy and maximum value, and time difference calculated by cross-correlation are selected as the input feature vectors of the RBFN. Experiments were carried out on a laboratory-scale test rig to verify the validity and correctness of the proposed method. Leakage signals at different positions under different pressures were obtained on the CO$_2$ pipeline leakage test bench. Compared with the time difference of arrival method, the relative error obtained using the proposed method is less than 2%, which has certain engineering application prospects