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Robust data inference and Cost-Effective Cell Selection for Sparse Mobile Crowdsensing

IEEE-ACM TRANSACTIONS ON NETWORKING 2024年第5期32卷 3760-3775页

作者： Li, Chengxin Li, Zhetao Long, Saiqin Qiao, Pengpeng Yuan, Ye Wang, Guoren Xiangtan Univ Sch Math & Computat Sci Xiangtan 411105 Peoples R China Jinan Univ Coll Informat Sci & Technol Guangzhou 510632 Peoples R China Beijing Inst Technol Sch Comp Sci & Technol Beijing 100081 Peoples R China

Sparse Mobile CrowdSensing (MCS) aims to reduce sensing cost while ensuring high task quality by intelligently selecting small regions for sensing and accurately inferring the remaining areas. data inference and cell selection are crucial components in Sparse MCS. However, cell division, which is a prerequisite for cell selection, has received insufficient attention. The existing uniform division method disregards the correlation of the sensing area. In addition, the impact of sparse noise on both data inference and cell selection has been ignored, potentially undermining the effectiveness of Sparse MCS. To address these issues, we propose a novel scheme termed Robust data inference and Cost-Effective cell Selection for Sparse MCS (Rices). Specifically, we first design an adaptive region division strategy that captures the correlation of sensing regions. Subsequently, we tackle the robust data inference problem in the presence of sparse noise by formulating it as a dual-objective optimization. Furthermore, we optimize the cell selection strategy to dynamically adjust the set of sampled cells under the constraints of data inference quality. Extensive experiments on large-scale real-world datesets are conducted to evaluate the proposed scheme. The results demonstrate that Rices can accurately recover missing data with 20% sparse noise and significantly reduce sensing costs compared to baseline models.

关键词： Spare mobile crowdsensing cell selection data inference matrix completion

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Applications of Deep Learning to Ocean data inference and Subgrid Parameterization

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JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS 2019年第1期11卷 376-399页

作者： Bolton, Thomas Zanna, Laure Univ Oxford Dept Phys Oxford England

Oceanographic observations are limited by sampling rates, while ocean models are limited by finite resolution and high viscosity and diffusion coefficients. Therefore, both data from observations and ocean models lack information at small and fast scales. Methods are needed to either extract information, extrapolate, or upscale existing oceanographic data sets, to account for or represent unresolved physical processes. Here we use machine learning to leverage observations and model data by predicting unresolved turbulent processes and subsurface flow fields. As a proof of concept, we train convolutional neural networks on degraded data from a high-resolution quasi-geostrophic ocean model. We demonstrate that convolutional neural networks successfully replicate the spatiotemporal variability of the subgrid eddy momentum forcing, are capable of generalizing to a range of dynamical behaviors, and can be forced to respect global momentum conservation. The training data of our convolutional neural networks can be subsampled to 10-20% of the original size without a significant decrease in accuracy. We also show that the subsurface flow field can be predicted using only information at the surface (e.g., using only satellite altimetry data). Our results indicate that data-driven approaches can be exploited to predict both subgrid and large-scale processes, while respecting physical principles, even when data are limited to a particular region or external forcing. Our in-depth study presents evidence for the successful design of ocean eddy parameterizations for implementation in coarse-resolution climate models. Plain Language Summary Models of the ocean and ocean observations are imperfect. Due to this imperfection, simulations of the ocean and our observations are not quite the same as the true ocean currents. We, therefore, need ways to make our ocean data more realistic and complete and to make it more similar to the actual ocean. Scientists have traditionally approached

关键词： machine learning turbulence oceanography data inference eddies

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PAMDI: Privacy aware missing data inference scheme for sparse mobile crowd sensing

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JOURNAL OF AMBIENT INTELLIGENCE AND SMART ENVIRONMENTS 2023年第1期15卷 19-46页

作者： Thakur, Tejendrakumar Marchang, Ningrinla North Eastern Reg Inst Sci & Technol Dept Comp Sci & Engn Nirjuli India

The ubiquity of mobile devices has birthed one of the most promising IoT applications called Mobile Crowd Sensing (MCS) wherein mobile devices carried around by a crowd are used to sense phenomena of interest. Subsequently, sensed data are collected, aggregated and analysed to extract useful information. Sparse Mobile Crowd Sensing (SMCS) aims at reducing the sensing overhead (e.g., battery consumption, incentive cost, etc.) by lowering the number of sensing tasks performed. Sensed data thus collected are used to infer missing values. However, it must be ensured that user's private information (e.g., user's home location) cannot be derived from the sensed data shared by a user. We propose a novel approach entitled `Privacy Aware Missing data inference Scheme for Sparse Mobile Crowd Sensing (PAMDI)' which employs the concept of perceptual hash for ensuring privacy while trying to maintain performance guarantees. Simulation results with the help of two real-world data-sets point towards the feasibility of the proposed approach for provisioning user privacy. We use regression algorithms for missing data inference in PAMDI and find that linear regression algorithms work best with the proposed privacy approach as compared to non-linear regression algorithms. Moreover, we observe that inference accuracy is more or less maintained even after introducing privacy with the proposed approach. In particular, for the first data-set (Temperature data-set), the mean absolute error (MAE) and root mean squared error (RMSE) values obtained by the linear algorithms using the proposed approach are about 2.65. C and 2.9. C respectively. On the other hand, the corresponding MAE and RMSE values generated by the linear algorithms when no privacy is introduced are about 2.25. C and 2.85. C respectively. For non-linear algorithms, the corresponding error values are higher. We also observe the same trend in the results of the second data-set.

关键词： Privacy security mobile crowdsensing mobile and wearable computing data inference

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Mitigating the Risk of Cascading Blackouts: A data inference Based Maintenance Method

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IEEE ACCESS 2018年 6卷 39197-39207页

作者： Liu, Feng Guo, Jinpeng Zhang, Xuemin Hou, Yunhe Mei, Shengwei Tsinghua Univ Dept Elect Engn State Key Lab Power Syst Beijing 100084 Peoples R China Univ Hong Kong Dept Elect & Elect Engn Hong Kong Hong Kong Peoples R China

The risk of cascading blackouts (RCB) is of great significance in practice because cascading outages can have catastrophic consequences. As there is a positive relationship between the probability of cascading blackouts and that of component failures, an effective way to mitigate the RCB is to perform maintenance. However, this approach is of limited value when considering extremely complicated cascading outages, such as those in particularly large systems. In this paper, we propose a methodology to efficiently identify the most influential component(s) for mitigating the RCB in a large-power system based on inference from the simulation data. First, we establish a data-based analytic relationship between the adopted maintenance strategies and the estimated RCB. Then, we formulate the component maintenance decision-making problem as a nonlinear 0-1 programming problem. We then quantify the credibility of the estimated RCB and develop an adaptive method to determine the minimum required number of simulations, which is a crucial parameter in the optimization model. Finally, we devise two heuristic algorithms to efficiently identify approximately optimal solutions. The proposed method is then validated by way of numerical experiments based on IEEE standard systems and an actual provincial system.

关键词： data inference maintenance strategy risk of cascading blackouts

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Random field-based regional liquefaction hazard mapping - data inference and model verification using a synthetic digital soil field

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BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT 2018年第3期77卷 1273-1286页

作者： Juang, C. Hsein Shen, Mengfen Wang, Chaofeng Chen, Qiushi Tongji Univ Coll Transportat Engn Key Lab Rd & Traff Engn Minist Educ Shanghai 201804 Peoples R China Clemson Univ Glenn Dept Civil Engn Clemson SC 29634 USA

Geostatistical tools and random field models have been increasingly used in recent liquefaction mapping studies. However, a systematic verification and assessment of random field models has yet to be taken, and implications of various random field-based mapping approaches are unknown. In this paper, an extremely detailed three-dimensional synthetic digital soil field is artificially generated and used as a basis for assessing and verifying various random field-based models for liquefaction mapping. Liquefaction hazard is quantified in terms of the liquefaction potential index (LPI), which is mapped over the studied field. A classical CPT-based liquefaction model is adopted to assess liquefaction potential of a soil layer. Different virtual field investigation plans are designed to assess the dependency of data inference and model performance upon the level of availability of sampling data. Model performances are assessed using three information theory-based measures. Results show that when sampling data is sufficient, all random field-based models examined capture fairly well the benchmark liquefaction potentials in the studied field. As the size of the sampling data decreases, the accuracy of predictions decreases for all models but to different degrees;the three-dimensional random field model gives the best result in this scenario. All random field-based models examined in this paper yield a slightly more conservative prediction of liquefaction potential over the studied field.

关键词： Random field model Liquefaction mapping Liquefaction potential index Cone penetration test data inference Model verification

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3DA-NTC: 3D Channel Attention Aided Neural Tensor Completion for Crowdsensing data inference

3DA-NTC: 3D Channel Attention Aided Neural Tensor Completion...

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International Joint Conference on Neural Networks (IJCNN)

作者： Kang, Xu Jia, Jia Jia, Zhiyang Ren, Jiadong China Univ Petr Beijing Karamay Karamay Peoples R China Beijing Univ Posts & Telecommun Sch Cyberspace Secur Beijing Peoples R China Yanshan Univ Sch Informat Sci & Engn Qinhuangdao Hebei Peoples R China

ISBN: (纸本)9798350359329;9798350359312

Mobile crowdsensing is a promising scheme for performing large-scale urban monitoring, but it always faces the issue of unstable spatiotemporal coverage, which results in the incompletion of data collection. The common solutions for tackling this issue, are to use the existing subset of measurements for inferring the remaining unsensed data by leveraging the latent data correlations. However, existing data inference techniques, both for matrix/tensor factorization based methods and deep learning based methods, cannot well capture the high-order and dynamic data correlations simultaneously under the mobile crowdsensing scheme. In this paper, we propose a novel 3Dimensional (3D) channel attention aided neural tensor completion method, called "3DA-NTC", for more accurate crowdsensing data inference, through leveraging both the multi-dimensional data structure mining ability of tensor factorization as well as the high-order, dynamic correlation learning ability of deep neural network. Specifically, to capture the spatiotemporal and multitype data correlations, we first use a 3D tensor to model the 3Order interaction among crowdsensing data. Then, we combine the traditional inner product based tensor factorization with outer product computing to enhance the modeling of nonlinear data correlations and form an interaction tensor, based on which, we apply a 3D channel attention aided convolutional neural network to further extract the features of high-order and dynamic data interactions for missing value inference. Extensive experiments on two real-world urban sensing datasets, including U-Air and SensorScope, are conducted to evaluate the performance of 3DANTC, and the results demonstrate the superiority of our method compared with the state-of-the-art (SOTA) baselines in missing data recovery.

关键词： data inference attention mechanism neural network tensor completion mobile crowdsensing

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Spatiotemporal Fracture data inference in Sparse Urban CrowdSensing 41

Spatiotemporal Fracture Data Inference in Sparse Urban Crowd...

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41st IEEE Conference on Computer Communications (IEEE INFOCOM)

作者： Wang, En Zhang, Mijia Xu, Yuanbo Xiong, Haoyi Yang, Yongjian Jilin Univ Coll Comp Sci & Technol Changchun Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun Peoples R China Baidu Res Big Data Lab Seattle CA USA

ISBN: (纸本)9781665458221

While Mobile CrowdSensing (MCS) has become a popular paradigm that recruits mobile users to carry out various sensing tasks collaboratively, the performance of MCS is frequently degraded due to the limited spatiotemporal coverage in data collection. A possible way here is to incorporate sparse MCS with data inference, where unsensed data could be completed through prediction. However, the spatiotemporal data inference is usually "fractured" with poor performance, because of following challenges: 1) the sparsity of the sensed data, 2) the unpredictability of a spatiotemporal fracture and 3) the complex spatiotemporal relations. To resolve such fracture data issues, we elaborate a data generative model for achieving spatiotemporal fracture data inference in sparse MCS. Specifically, an algorithm named Generative High-Fidelity Matrix Completion (GHFMC) is proposed through combining traditional Deep Matrix Factorization (DMF) and Generative Adversarial Networks (GAN) for generating spatiotemporal fracture data. Along this line, GHFMC learns to extract the features of spatiotemporal data and further efficiently complete and predict the unsensed data by using Binary Cross Entropy (BCE) loss. Finally, we conduct experiments on three popular datasets. The experimental results show that our approach performs higher than the state-of-the-art (SOTA) baselines in both data inference accuracy and fidelity.

关键词： Mobile CrowdSensing data inference spatiotemporal fracture data Generative Adversarial Network

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Innovative data inference

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Preview 2011年第151期2011卷 24-29页

The goal of exploration geophysics is to infer the nature of buried structure and, in particular, generate drill targets that lead to a mineral deposit discovery or reserve delineation. As a profession, we aim to turn geophysical data into geological information. Most geophysical techniques enable inferences to be made from airborne, ground-based or bore-hole data through a deterministic process whereby a single model ‘answer’ is generated. Well-founded algorithms include uncertainty estimates for different parameters in the model and/or some form of model validation. This approach has been successful to date, and we advocate the continued use of deterministic *** also advocate that alternate strategies for extracting information from data are used alongside deterministic strategies. If we consider two general properties of data inference approaches, that of (1) assurance and (2) opportunity, deterministic approaches score poorly regarding opportunity: that is, useful answers may be missed. Alternate strategies can be computationally intensive, but several important classes of approach, summarised in this article are now tractable on workstation or high-specification notebook PCs. By using a range of strategies we can maximise both assurance and opportunity for a particular data inference goal and obtain extra, useful geological information from our data.

关键词： data inference information inversion modelling

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"It's Like How Mom Cooks for You, Tell Her Nothing You Only Get Chicken Soup.": Understanding Children's Perception of datafication Online in China

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INTERNATIONAL JOURNAL OF HUMAN-COMPUTER INTERACTION 2025年第10期41卷 5992-6010页

作者： Zhu, Yumeng Wang, Ge Li, Yan Zhao, Jun Zhejiang Univ Coll Educ Hangzhou Peoples R China Univ Oxford Dept Comp Sci Oxford England

datafication, the process where users' actions online are pervasively recorded, tracked, aggregated, analysed, and exploited by online services in multiple ways, is becoming increasingly common today. However, we know little about how children, especially non-Western children, perceive such practices. Through one-to-one semi-structured interviews with 36 children aged 11-14 from Chinese middle schools, we examined how Chinese children perceive datafication practices. We identified three knowledge gaps in children's current perceptions of datafication practices online, including their lack of recognition of (i) their data ownership, (ii) data being transmitted across platforms, and (iii) datafication could go beyond video recommendation and include inferences and profiling of their personal aspects. Through contextualising these observations within the Chinese context and its unique online ecosystem, we identified cultural traits in Chinese children's perceptions of datafication. We drew on education theories to discuss how to support the future digital literacy development and design online platforms for Chinese children.

关键词： datafication data inference online platforms children

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The Design of Access Control by using data Dependency To Reduce the inference of Sensitive data 25

The Design of Access Control by using Data Dependency To Red...

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25th International Conference on Technologies and Applications of Artificial Intelligence (TAAI)

作者： Lee, Kuei-Sheng Lai, Yen-Cheng Chen, Shao-Yu Lin, Yi-Shin Tsai, Meng-Feng Natl Cent Univ Dept Comp Sci & Informat Engn Taoyuan Taiwan

ISBN: (纸本)9781665403801

From the back-end data system point of view, the primary personal information protection mechanism is to block the direct accessing of sensitive data. The possibility that sensitive data may be indirectly inferenced by public information, have not been addressed. In United States, there are cases and discussions about "Mosaic theory". And responsibilities of data holders were legally stated. But no known researches were invested to create a responsible mechanism This research explores the functional dependencies, and compute risky column sets based on them. We can then process users' queries and initiate protection operation if risky data are involved.

关键词： data inference functional dependency information system security control mosaic theory personal information protection

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