Fine-grained Data Research Articles

Gut microbiome responds compositionally and functionally to the seasonal diet variations in wild gibbons

Wild animals may encounter multiple challenges especially food shortage and altered diet composition in their suboptimal ranges. Yet, how the gut microbiome responds to dietary changes remains poorly understood. Prior studies on wild animal microbiomes have typically leaned upon relatively coarse dietary records and individually unresolved fecal samples. Here, we conducted a longitudinal study integrating 514 time-series individually recognized fecal samples with parallel fine-grained dietary data from two Skywalker hoolock gibbon (Hoolock tianxing) groups populating high-altitude mountainous forests in western Yunnan Province, China. 16S rRNA gene amplicon sequencing showed a remarkable seasonal fluctuation in the gibbons’ gut microbial community structure both across individuals and between the social groups, especially driven by the relative abundances of Lanchnospiraceae and Oscillospiraceae associated with fluctuating consumption of leaf. Metagenomic functional profiling revealed that diverse metabolisms associated with cellulose degradation and short-chain fatty acids (SCFAs) production were enriched in the high-leaf periods possibly to compensate for energy intake. Genome-resolved metagenomics further enabled the resolving metabolic capacities associated with carbohydrate breakdown among community members which exhibited a high degree of functional redundancy. Our results highlight a taxonomically and functionally sensitive gut microbiome actively responding to the seasonally shifting diet, facilitating the survival and reproduction of the endangered gibbon species in their suboptimal habitats.

Security requirement classification of electricity trading data based on hierarchical fuzzy Petri network

With the emergence of spot electricity trading, market-oriented trading has been intensively carried out, forming a multi-cycle trading system. In this process, a large amount of fine-grained electricity trading data circulates on the trading platform. Trading data is an important basis for decision-making in the electricity spot market, which directly affects the trading profits of market entities, proper disclosure of this information is very important for market enterprises. Information disclosure must ensure the validity and security. However, it is hard to judge the security demand for trading data, and there is no suitable evaluation system for determining the security requirement of data, which will limit the electricity market’s further development. In this paper, we first design an indicator system for security requirements classification, which manages data risks from three aspects: data classification, data risk, and entity demand. This system will guide us in determining data security requirements and further help identify differentiated data security protection schemes. Then, based on this system, we propose the classification method of data security requirements through a hierarchical fuzzy Petri net. The lower network realizes a reasonable assessment of data risk with reference to the index system, and the upper network finally determines the level of security requirements through the fuzzy rule base. Last, two types of data are selected to judge their security requirements. The results show that our method can provide a reference for privacy protection in electricity market data.

Natural Hazards, Social Policy, and Electoral Performance: Evidence from the 2017 Earthquake in Mexico City

AbstractDo large-scale and unexpected events, such as natural disasters, affect elections? This article studies the political dimension of the 19-S earthquake that hit Mexico City in 2017, a few months before the 2018 elections. Using fine-grained geospatial data, the results show that candidates from the city-level incumbent Partido de la Revolución Democrática (PRD) had a small increase in vote share in 2018 compared to the previous election in precincts more exposed to damaged caused by the earthquake (in terms of both distance-based and per capita measures), accounting for the seismic profile and socioeconomic characteristics of the neighborhood. The article shows that the implementation of disaster-recovery policy explains part of this relationship. Moreover, voters were as electorally responsive to a future risk reduction strategy as to a reconstruction credit.

Designing attribute-based verifiable data storage and retrieval scheme in cloud computing environment

The cloud computing technology is a novel storage and computing paradigm that enables individuals and organizations to store data, share data with intended group of users and retrieve data when require. It greatly improves peoples’ data storage and sharing, and data retrieval capabilities by providing flexible, less expensive and quality services. For data security and privacy concerns, secure and authenticated data storage, fine-grained access control of encrypted data, secure search for the outsourced data and search results verification are of critical importance. However, achieving the aforementioned functionalities simultaneously is quite challenging. In this paper, for the first time, we propose a secure lightweight Attribute-Based verifiable Data Storage and data Retrieval Scheme (ABDSRS) for cloud environments that attains the following features: (i) lightweight design, (ii) provably secure, (iii) fine-grained data access control, (iv) data owner (DO) anonymity, (v) data and DO authenticity, (vi) keyword policy search over encrypted data, (vii) keyword privacy, and (viii) search results verification. ABDSRS employs attribute-based online-offline mechanism in which only authorized DOs can anonymously upload data to the cloud. And, a data user (DU) can search over encrypted data using keyword policy. ABDSRS enables a DU to verify the correctness of the search results (i.e., the correctness of the operations performed by the cloud) without interacting with any authority. ABDSRS is lightweight in the sense that the heavy computations are offloaded either to the cloud or to offline phase, while only lightweight operations are executed at the DU device. We formalize more general security definitions of ABDSRS by considering various possible adversarial capabilities and present rigorous security analysis. We also conduct experiments to evaluate ABDSRS’s performance.

From bad to worse? How protest can foster armed conflict in autocracies

Many protest movements have brought down dictatorships and paved the way for democracy. However, protests can also foster large-scale violence at the level of civil war. How can we explain the development from protest to armed conflict? In this paper, we use geographically fine-grained data to examine how collective mobilization leads to civil war violence at the local level. We argue that two mechanisms can explain this. First, in a protest escalation dynamic, confrontations between protesters and state security forces increase the willingness of protesters to ramp up the use of force. Second, in a protest capture mechanism, protests attract attention and resources from the state, thereby providing other local non-state actors with the opportunity to use violence. We test our theoretical expectations in a spatial analysis of protests and armed conflict in autocracies from 2003 to 2014. Our results show that protests increase the risk of local armed conflict when violently repressed. Further analysis reveals that the second mechanism, protest capture, accounts for the majority of escalations to armed conflict we see in our data.

HE-Booster: An Efficient Polynomial Arithmetic Acceleration on GPUs for Fully Homomorphic Encryption

Fully Homomorphic Encryption (FHE) enables secure offloading of computations to untrusted cloud servers as it allows computing on encrypted data. However, existing well-known FHE schemes suffer from heavy performance overheads. Thus numerous accelerations based on FPGAs, ASICs, and GPUs have been proposed. Compared to FPGAs and ASICs, GPUs have obvious advantages in productivity and development costs. And also, GPUs have already been widely deployed in commercial cloud or supercomputing centers. Therefore, we present HE-Booster, an efficient GPU-based FHE acceleration design. For single-GPU acceleration, a thorough systematic design is exploited to map five common phases in typical FHE schemes to the GPU parallel architecture. In particular, inspired by the regular architecture of NTT/INTT, a novel inter-thread local synchronization is proposed to exploit thread-level parallelism. For multi-GPU acceleration, we propose a scalable parallelization design that exploits <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">data-level parallelism</i> through fine-grained data partition under different representations. Finally, experiments on 1 NVIDIA GPU demonstrate that our work outperforms 251.7×, 78.5× and 164.9× than three mainstream CPU-based libraries HElib, SEAL, and PALISADE, and up to 170.5× speedup is obtained compared to the GPU-accelerated library cuHE. What's more, performing 8 homomorphic multiplications on 8 GPUs can deliver up to a 7.66× performance boost compared to a single-GPU implementation.

A Management Method of Multi-Granularity Dimensions for Spatiotemporal Data

To understand the complex phenomena in social space and monitor the dynamic changes in people’s tracks, we need more cross-scale data. However, when we retrieve data, we often ignore the impact of multi-scale, resulting in incomplete results. To solve this problem, we proposed a management method of multi-granularity dimensions for spatiotemporal data. This method systematically described dimension granularity and the fuzzy caused by dimension granularity, and used multi-scale integer coding technology to organize and manage multi-granularity dimensions, and realized the integrity of the data query results according to the correlation between the different scale codes. We simulated the time and band data for the experiment. The experimental results showed that: (1) this method effectively solves the problem of incomplete query results of the intersection query method. (2) Compared with traditional string encoding, the query efficiency of multiscale integer encoding is twice as high. (3) The proportion of different dimension granularity has an impact on the query effect of multi-scale integer coding. When the proportion of fine-grained data is high, the advantage of multi-scale integer coding is greater.

ProtoSimi: label correction for fine-grained visual categorization

Deep models trained by using clean data have achieved tremendous success in fine-grained image classification. Yet, they generally suffer from significant performance degradation when encountering noisy labels. Existing approaches to handle label noise, though proved to be effective for generic object recognition, usually fail on fine-grained data. The reason is that, on fine-grained data, the category difference is subtle and the training sample size is small. Then deep models could easily overfit the noisy labels. To improve the robustness of deep models on noisy data for fine-grained visual categorization, in this paper, we propose a novel learning framework named ProtoSimi. Our method employs an adaptive label correction strategy, ensuring effective learning on limited data. Specifically, our approach considers the criteria of exploring the effectiveness of both global class-prototype and part class-prototype similarities in identifying and correcting labels of samples. We evaluate our method on three standard benchmarks of fine-grained recognition. Experimental results show that our method outperforms the existing label noisy methods by a large margin. In ablation studies, we also verify that our method is non-sensitive to hyper-parameters selection and can be integrated with other FGVC methods to increase the generalization performance.

Privacy-Preserving federated learning: An application for big data load forecast in buildings

Internet of Everything (IoE) is playing key role to enable smart energy management in buildings. A huge amount of load data are accumulated continuously to control the energy systems inside buildings. Big data analysis and prediction play increasingly vital roles in making the building energy systems smarter. Meanwhile, the big data forecasting technique represents the key element for an efficient energy control mechanism in buildings. However, the load forecast models require a huge amount of consumption data with enough variety to achieve high accuracy. Moreover, the collected IoE data are privacy-sensitive. That is, the fine-grained load data collected at the level of buildings may reveal information about the occupant’s behaviors. In this paper, by using Federating Learning (FL) as a decentralized machine learning technique, buildings occupants’ information can efficiently participate in the decision-making without revealing privacy through a cloud/Edge computing framework. In the FL approach, the big data are widely distributed between buildings, where a shared learning model is trained locally on each of the buildings. Nonetheless, the FL approach does not provide provable privacy guarantees. Moreover, FL requires participating buildings to regularly exchange huge updates resulting in huge bandwidth consumption. The paper presents a new privacy-preserving bandwidth-efficient FL that relies on Differential Privacy to theoretically provide privacy. Furthermore, to ensure the fast convergence of the FL model, a POWER-SELECTION protocol is introduced to select between buildings to participate in the FL round. By numerical analysis, the proposed FL approach is demonstrated to improve the accuracy of load forecast while compromising provable privacy.

Predicting road flooding risk with crowdsourced reports and fine-grained traffic data

The objective of this study is to predict road flooding risks based on topographic, hydrologic, and temporal precipitation features using machine learning models. Existing road inundation studies either lack empirical data for model validations or focus mainly on road inundation exposure assessment based on flood maps. This study addresses this limitation by using crowdsourced and fine-grained traffic data as an indicator of road inundation, and topographic, hydrologic, and temporal precipitation features as predictor variables. Two tree-based machine learning models (random forest and AdaBoost) were then tested and trained for predicting road inundations in the contexts of 2017 Hurricane Harvey and 2019 Tropical Storm Imelda in Harris County, Texas. The findings from Hurricane Harvey indicate that precipitation is the most important feature for predicting road inundation susceptibility, and that topographic features are more critical than hydrologic features for predicting road inundations in both storm cases. The random forest and AdaBoost models had relatively high AUC scores (0.860 and 0.810 for Harvey respectively and 0.790 and 0.720 for Imelda respectively) with the random forest model performing better in both cases. The random forest model showed stable performance for Harvey, while varying significantly for Imelda. This study advances the emerging field of smart flood resilience in terms of predictive flood risk mapping at the road level. In particular, such models could help impacted communities and emergency management agencies develop better preparedness and response strategies with improved situational awareness of road inundation likelihood as an extreme weather event unfolds.

Integration of Farm Financial Accounting and Farm Management Information Systems for Better Sustainability Reporting

Farmers face an increasing administrative burden as agricultural policies and certification systems of trade partners ask for more sustainability reporting. Several indicator frameworks have been developed to measure sustainability, but they often lack empirical operationalization and are not always measured at the farm level. The research gap we address in this paper is the empirical link between the data needs for sustainability reporting and the developments in data management at the farm level. Family farms do not collect much data for internal management, but external demand for sustainability data can partly be fulfilled by reorganizing data management in the farm office. The Farm Financial Accounts (FFAs) and Farm Management Information Systems (FMISs) are the main data sources in the farm office. They originate from the same source of note-taking by farmers but became separated when formalized and computerized. Nearly all European farms have a bank account and must keep financial accounts (e.g., for Value-Added Tax or income tax) that can be audited. Financial accounts are not designed for environmental accounting or calculating sustainability metrics but provide a wealth of information to make assessments on these subjects. FMISs are much less frequently used but collect more technical and fine-grained data at crop or enterprise level for different fields. FMISs are also strong in integrating sensor and satellite data. Integrating data availability and workflows of FFAs and FMISs makes sustainability reporting less cumbersome regarding data entry and adds valuable data to environmental accounts. This paper applies a design science approach to design an artifact, a dashboard for sustainability reporting based on the integration of information flows from farm financial accounting systems and farm management information systems. The design developed in this paper illustrates that if invoices were digitized, most data-gathering needed for external sustainability reporting would automatically be done when the invoices is paid by a bank transfer. Data on the use of inputs and production could be added with procedures as in current FMISs, but with less data entry, fewer risks of differences in outcomes, and possibilities of cross-checking the results.

Operating characteristics of the factor flow networks in rural areas: A case study of a typical industrial town in China.

The networks of factor flows in rural areas are the main support for rural revitalization, which has become one of the research trends in rural geography. Taking a typical industrial town in China as an example, the study explored the operating characteristics of rural factor flow networks and the relations of multi-factor flows based on the social survey method and fine-grained flows data. Results showed that population flows, capital flows and policy flows increased significantly in rural areas. Thereinto, population flows, especially labor flows, mainly ran into the townships and industrial cluster villages, so did capital inflows and outflows, while policy flows ran around the township. The villages with dense population and capital flows formed the "central villages", which had exceeded the township in the two flow networks. Policy flows and capital flows played a guiding role in population flows, so did the policy flows on the capital flows. Meanwhile, the population flows and the capital flows could reinforce each other. In conclusion, a multi-center structure network with the separation of economic center and administrative center had been formed in rural areas. And there was a close interaction between these factor flows. Furthermore, the theoretical model of town-village symbiotic network was constructed.

DAC4SH: A Novel Data Access Control Scheme for Smart Home Using Smart Contracts

Smart home (s-home) is an important Internet of Things (IoT)-based application in improving the living environment. Many cryptography-based protection schemes have been proposed to guarantee data confidentiality in s-home. Access control is a promising method to protect generated data from unauthorized access, and it is also urgently needed in s-home. Current centralized access control schemes are not excellent in security and performance. For instance, it still faces the problems of a single point of failure, low reliability, and poor scalability. This study proposes a decentralized and reliable access control scheme for s-home using smart contracts, named DAC4SH. To be specific, this proposed framework consists of an access policy management contract (APMC), a data attribute management contract (DAMC), a subject attribute management contract (SAMC), and a data access control contract (DACC) for realizing the fine-grained data access control. Meanwhile, we record all the access activities into the immutable distributed ledgers for auditing. To verify the feasibility of our DAC4SH, we construct an Ethereum-based prototype system and evaluate the performance of DAC4SH in terms of computational and communication costs. According to the experimental results, we can conclude that the performance of DAC4SH is appropriate.

Pain paths among post-COVID-19 condition subjects: A prospective cross-sectional study with in-person evaluation.

New-onset chronic pain has been acknowledged as part of the post-COVID-19 condition. However, available fine-grained data about its clinical phenotype, trajectories and main associated characteristics remain scarce. We described the distinct temporal evolutions of post-COVID-19 pain and their epidemiological and phenotypical features. A prospective cross-sectional study enrolled post-COVID-19 condition patients (i.e. who had persisting COVID-19-related symptoms over 30 days since their first positive laboratory test), whose COVID-19 diagnosis had been supported by RT-PCR of oral/nasopharyngeal swab or serology. They underwent in-person evaluations with a structured interview, pain and quality-of-life-related questionnaires and thorough physical examination. Chronic pain (CP) and probable neuropathic pain (NP) were defined according to IASP criteria. The present study included 226 individuals, 177 (78.3%) of whom presented over 3 months since their first COVID-19 symptom. New-onset pain occurred in 170 (75.2%) participants and was chronic in 116 (68.2%). A chronic course was associated with COVID-19-related hospitalization, new-onset fatigue, lower cognitive performance, motor and thermal sensory deficits, mood and sleep impairments and overall lower quality-of-life levels. Probable NP occurred in only 7.6% new-onset pain patients, and was associated with pain chronification, new-onset fatigue, motor and thermal sensory deficits, mechanical allodynia and lower rates of SARS-CoV-2 vaccination. Previous CP was reported by 86 (38.1%) individuals and had aggravated after the infection in 66 (76.7%) of them, which was associated with orthostatic hypotension. Post-COVID pain phenomena follow different paths, which are associated with specific clinical and epidemiological features, and possibly distinct underlying mechanisms, prognostic and therapeutic implications. COVID-19-related pain usually follows a chronic course and is non-neuropathic. Its possible courses and phenotypes are associated with distinct clinical and epidemiological features. This suggests differing underlying mechanisms, which may have significant prognostic and therapeutic implications.

Achieving Fine-Grained Data Sharing for Hierarchical Organizations in Clouds

Cloud computing has become an increasingly popular option for users to store and share data. Encryption prior to outsourcing data to the cloud is the best way to protect data security and privacy; however, it hinders sharing of the data that was encrypted. In addition, users in many real-world organizations (e.g., enterprises) have multiple level structures and a higher-level user should have the privilege to decide which data can be shared with a lower-level user. Most solutions in the literature suffer from inefficiency or inflexibility in tackling this problem. In this article, we propose a fine-grained hierarchical data sharing (FHDS) scheme in clouds. With FHDS, the data owner can encrypt data with his public key, and then selectively share encrypted data with users in a hierarchy; if necessary, the users can disseminate the owner's data to their subordinates in the lower levels by generating access keys. In particular, the higher-level users could puncture the keys with some tags such that the part of the owner's data which is labeled by the punctured tags will not be accessible to the lower-level users. The proposed scheme is provable secure under our security model and performance analyses show the efficiency of the scheme.

APDP: Attribute-Based Personalized Differential Privacy Data Publishing Scheme for Social Networks

In the Big Data era, the wide usage of mobile devices has led to large amounts of information release and sharing through social networks, where sensitive information of the data owners may be leaked. Traditional approaches that provide the identical privacy protection levels for all users result in poor quality of service, thus the concept of personalized privacy has been proposed in recent years. However, existing methods that add different noises to each user will require both high real-time performance and resource consumption. This paper presents a fine-grained personalized differential privacy data publishing scheme (APDP) for social networks. Specifically, we design a new mechanism that defines the privacy protection levels of different users based on their attribute values. In particular, we exploit the TOPSIS method to map the attribute values to the amount of noise required to add. Furthermore, to prevent illegal download of data, the access control is integrated with differential privacy. Compared with traditional attribute-based encryption data publishing schemes, our scheme can get rid of the expensive computation overhead. Theoretical analyses and simulations show that APDP can realize efficient personalized differential privacy data publishing with reasonable data utility.

The analysis of partisan differences in advanced democracies. The “independent variable problem” reconsidered

The paper challenges the idea that using governing parties’ policy positions is necessarily desirable in quantitative analyses of partisan differences in public policies. Rather, for some research questions parties’ affiliation with certain party families is the preferable solution. The paper shows that the use of policy positions instead of party family affiliation shifts the research question from asking whether parties make a difference in public policy to the question whether parties in government do what they promise. This shift can have considerable analytical costs and can potentially blindfold scholars for certain dynamics. Moreover, the use of party family affiliation has less drawbacks than is often claimed even under multidimensional party competition if the simple distinction between left, center and right parties is abandoned and more fine-grained data for various party families are used.

Does electricity price reduction bring a sustainable development of business: Evidence from fine-grained industrial electricity consumption data in China

This paper studies the impact of electricity price reduction policy on the economic activities and carbon emissions of different industries. Since 2018, the Chinese government has advocated reducing electricity price for industrial customers in order to alleviate the non-tax burden on businesses. Using monthly electricity consumption and user data from 2016 to 2019, we find that the electricity price reduction has significantly increased the industrial electricity consumption and users. Heterogeneity analyses show that the effect is greater in heavy polluted industries and industries in low and medium GDP regions. The results of threshold impact analysis show that the threshold value of heavy pollution industries was lower than that of general industry and commerce, while heavy pollution industries are more sensitive to electricity prices than general industries. Moreover, the reduction of electricity prices results in a higher percentage of carbon emissions growth in heavy pollution industries. Our result can also help the policymakers access the costs and benefits of electricity price reduction for industrial customers more accurately.

An integrated process-based framework for flood phase segmentation and assessment

From a process perspective, a flood includes several phases with distinguishable features. Fine-grained multisource data for different flood phases can be used to inform decision-making as flooding progresses. Therefore, the aim of this study was to develop an integrated framework based on human perceptions to progressively profile floods, including flood process segmentation rules (FPSR), flood severity index (FSI) and flood process perception ontology (FPPO). FPSR identifies flood phases based on specific signals in multisource data and provides spatiotemporal process information to FPPO consistent with flood perception. FSI follows FPSR to evaluate flooding throughout its evolution process. The comparison between FPSR and the flood monitoring index (IF) demonstrates that FPSR can detect flood events and segment the flooding process into latency, onset, development and recovery phases. The correlations between the standardized antecedent precipitation index (SAPI) and FSI show that FSI can assess flood severity with both natural and social effects in every flooding phase (R2 = 0.726 and 0.673 for the 2016 and 2020 floods, respectively). An experiment finds that flood events in Wuhan, China, usually begin in mid-to-late June and are the most severe in July, when more caution is needed for flood prevention and mitigation.

Detecting spatiotemporal propagation patterns of traffic congestion from fine-grained vehicle trajectory data

Traffic congestion on a road segment typically begins as a small-scale spatiotemporal event that can then propagate throughout a road network and produce large-scale disruptions to a transportation system. In current techniques for the analysis of network flow, data is often aggregated to relatively large (e.g. 5 min) discrete time steps that obscure the small-scale spatiotemporal interactions that drive larger-scale dynamics. We propose a new method that handles fine-grained data to better capture those dynamics. Propagation patterns of traffic congestion are represented as spatiotemporally connected events. Each event is captured as a time series at the temporal resolution of the available trajectory data and at the spatial resolution of the network edge. The spatiotemporal propagation patterns of traffic congestion are captured using Dynamic Time Warping and represented as a set of directed acyclic graphs of spatiotemporal events. Results from this method are compared to an existing method using fine-grained data derived from an agent-based model of traffic simulation. Our method outperforms the existing method. Our method also successfully detects congestion propagation patterns that were reported by media news using sparse real-world data derived from taxis.