dataesr/scientific-paragraphs-categorization huggingface dataset:
Evaluating the Filtering Steps The second conceptual optimization aims to filter the OpQHL in order to provide the consolidation and the detection modules (see Sections 6. 2 and 6. 3, respectively) with the minimal set of MKUs to consider for the currently processed query. In order to evaluate the impact of those two steps, we measure the evolution of the computation time for both the detection and the consolidation modules, with and without the filtering modules. We focus on this specific metric due to the requirement of detecting inference attacks at query-time. To measure the evolution of the computation time, we generate an increasing volume of MKUs inserted into the OpQHL. The dataset is composed of 19 queries, containing each 500 MKUs, for a total number of MKUs equal to 9500. We assume that no inference attack occurs in the dataset in order to control the quantity of MKUs stored in the OpQHL, since when a query leads to an inference it is not tracked, see Section 5.
Thus, for each query, at most 500 new MKUs are inserted into the OpQHL after processing the query. Each query in this dataset is provided as an input to RICE-Sy and the computation time of the consolidation module and the detection module are measured in order to assess the query-based filtering module and the search space filtering module, respectively. Those two temporal measures are performed once with the two filtering modules disabled and once with the two filtering modules enabled. In the case where the two modules are disabled, we provide to the system the first 10 queries of the dataset only, since the computation of the detection quickly grows. We observe that the computation time of each module is quadratic, cf. Figure  7a, c , which corresponds to the computed theoretical complexity.
We explain the difference in the computation time between the consolidation and detection by the usage of backtracking in the latter. The detection goes through the search space of P(Q u ∪ QHL(u)) (i. e. , the powerset of the set of MKUs received as input, see in Figure  4 ) for each modeled inference channel in order to identify if a subset of MKUs satisfies its constraints. In the case where the two filtering modules are enabled, we perform the same measurements by inserting all the queries from the dataset. We observe a similar order of improvement in the computation time of both modules, cf. Figure  7b, d , which is once again consistent with our theoretical algorithms. For the consolidation module, the improvement is explained by the fact that only a subset of MKUs from the OpQHL are now considered with the MKUs of the query instead of processing the whole OpQHL for each query.
Similarly, the detection module now has to go through a smaller search space which does not include the whole OpQHL anymore. We finally illustrate the computation time of each module in Figure  7e  and we observe that the two filtering modules (i. e. , the search space filtering and the query-based filtering) are linear and have become the most expensive computation performed by our system. In conclusion to these experiments, we observed that the proposed conceptual optimization steps reduced the overhead of RICE-Sy and make it linear. Since the search space filtering takes up to 10 s, further optimization steps still remain mandatory. However, optimizing RICE-Sy with conceptual optimization seems unlikely to yield more improvements due to the highly frequent queries on data streams. Hence, in order for our InfDS to be used at query-time, it must perform the detection under an acceptable time, especially to fit the context of highly frequent queries on data streams.
Hence, in the following section we demonstrate the possible improvement of the detection time via two mechanisms that reduce the volume of data process by RICE-Sy. The continuous generation of sensor data leads users to issue queries at a high frequency to obtain up to date measurements. This result in a constant increase in the QHL size, which leads the two filtering modules of RICE-Sy to become more and more computationally expensive, as observed in Figure  7e. The first approach aims at partitioning the QHL into smaller search space based on logical criteria so that when RICE-Sy process a query, the two filtering modules only process a part of the QHL thus reducing the computation time. The second approach focus on reducing the detection performed at query-time. In fact, based on the premise that most of the users of such database systems are not potential attackers, the inference detection at query-time should concern only users identified as suspicious.
Strategies to Partition the QHL The high frequency of queries issued to sensor databases leads to huge quantities of MKUs stored in the QHL. To perform the detection, RICE-Sy retrieves MKUs obtained by a user for a specific time window (i. e. , (t b , t e )). To efficiently perform the detection, the extraction of those MKUs can leverage the usage of indices on the three criteria: the user identity, the temporal beginning, and the temporal end of the window. However, a single criterion is considered while the others are searched sequentially. Indices using multiple criteria are efficient only when the query uses all of them. Therefore, partitioning the QHL and finding the right parts allows a direct access to the selected MKUs and a filtering of a smaller search space compared to considering the whole QHL.
The objective of the strategies is to partition the QHL into parts containing MKUs chosen based on logical criteria. Hence, when a query is processed by RICE-Sy, the criteria are used to retrieve the part of the QHL which must be considered for the detection. As illustrated in Figure  8 , the selector module enables the filtering modules in the pre-processing stage to correctly target the relevant partition. The challenge of this approach is to base strategies on criteria that most efficiently reduce the search space of the filtering modules, while maintaining an easy procedure to select the suitable part of a QHL for a given query. For instance, a good criterion avoids situations where the selector module has to insert MKUs into two partitions, which can result in the duplication of data.
e. , |QHL(u)| ≥ t q , she obtains her own partition in the QHL, denoted QHL u. All the MKUs associated to u in QHL are moved into QHL u , i. e. , QHL u = QHL(u) and QHL = QHL -QHL u. In this strategy, the QHL is defined as a set of partition QHL = QHL main ∪ {QHL u 1 ,. , QHL u n }, where QHL main represents the partition containing MKUs of users which have not been partitioned, and QHL u i representing the partition created for the user u i. The partitions associated to a user can be retrieved by the filtering modules thanks to the following function: selector : U → MK, where selector(u) = QHL u if ∃ QHL u ∈ QHL otherwise QHL main. Later on, when the same user u issues a query, Figure  8 , the filtering modules of RICE-Sy will consider only the search space of QHL u thanks to the Selector module.
Experiment: Evaluating the User-Oriented Strategy To demonstrate the benefits of partitioning the QHL, we evaluate in this section the user-oriented strategy by measuring the evolution of the detection time of RICE-Sy. Like for the experimentation of the conceptual optimizations in Section 7. 2, we focus on this metric to evaluate the proposed optimization with respect to our main requirement which is to detect inference attacks at query-time. We perform this evaluation by measuring this evolution with and without the strategy on the same dataset. For this experimentation, we generate a dataset D containing m queries issued by n users. The strategy is triggered when the median detection time reaches at least t t when processing a query and the quantity of MKUs obtained by the user is above the threshold t q.
At the end of the evaluation, we observe a decrease in the time at the level of a user when she obtains her own part of the QHL. In this case, the filtering modules have a smaller search space to consider. Globally, we note that the strategy allows the QHL to balance the search space between QHL parts affected to users having large quantities of MKUs and the main part containing users with a few MKUs. The resulting median detection time noticeably decreases with the strategy the more queries are processed by RICE-Sy. All measures have been performed on a VMware instance, using an Intel(R) Xeon(R) Gold 6126 CPU @ 2. 60 GHz, and 16 GB of RAM. Figure  9  shows the result of the evaluation performed for the user-oriented strategy, where D contains 19,003 queries divided over 10 users (for a total of 188,793 MKUs), the threshold is set to 125 ms and 700 MKUs, for t t and t q , respectively.
In Figure  9a, b , each point corresponds to the detection time of RICE-Sy when processing a query issued by user u 2 , respectively, u 8. The orange dashed vertical line shows when the MKUs of the concerned user are moved from the main part to her own part of the QHL. The dark blue filled points on the right side of the orange line corresponds to the detection time when extracting MKUs from the smaller search space of both users' new QHL part. The hollow red points correspond to the detection time without the user-oriented strategy. We observe that the detection time noticeably decreases after the partitioning due to the smaller amount of MKUs that RICE-Sy has to consider. Figure  10  compares for the same dataset D, the median detection time with (bottom curve) and without (top curve) the partitioning strategy, considering the total number of queries issued by all the users.
We choose to display the median in order to show the global evolution of the detection time. With partitioning, RICE-Sy is able to process the whole dataset in a shorter amount of time than without, hence the shorter line. We also observe, that each partitioning of the QHL for a user (depicted by the orange rectangles) further flatten the curve compared to the detection time without the strategy. The median time of the last query in the dataset is equal to 875 ms and 319 ms, without and with the strategy, respectively, resulting in an improvement of the detection time of 63% for this dataset. When considering the QHL as a single partition, the filtering modules go through all MKUs obtained by all users. Incidentally, the more queries have been processed by RICE-Sy, the larger the QHL. On the other hand, the quantity of MKUs is smaller after having created a partition, the filtering modules have a smaller search space to consider for future queries, hence the detection time of queries issued afterwards decreases.
To do so, we create 11 datasets (i. e. , to cover the range from no malicious users to only malicious users) representing synthetic queries, containing a similar quantity of MKUs referencing the 21 attributes of our case study MHEALTH. Each dataset has a different ratio of queries issued by users labeled as genuine or malicious. For instance, the dataset with a ratio of 10% contains 10% of queries issued by malicious users and 90% of queries issued by genuine users. Hence, a dataset is built for each step of the ratio (from 0% to 90%, with a step size of 10%) and contains 50 users having a similar volume of queries. More specific, each user has 22,000 MKUs divided into 6000 queries. In Figure  12 , we can observe the median online detection time that is measured for each configuration. As expected, we see that the higher the ratio of online processed queries (by users considered as malicious), the higher the median online time.
In order to perform this evaluation, we have once again generated datasets containing synthetic queries. The five datasets, labeled D 1 to D The median growth rate of the volume between the datasets is approximately equal to 0. 45. The growth rate of the median online detection time for the ratio of 5%, 30%, and 78% is approximately equal to 0. 13, 0. 18, and 0. 27, respectively. We observe that the higher the ratio of volume processed online is, the larger the growth rate of the median becomes. Focusing on the two realistic ratios (i. e. , 5% and 30%), we can estimate the amount of volume that can be processed online by using the observed growth rate. For 30%, we deduce that for a volume of approximately 1. 9 × 10 6 MKUs, the median online detection time is 85 ms, just above the threshold of the acceptable time (i.
e. , 80 ms). In order to evaluate this estimation, we have generated a sixth dataset, D 6 , containing a volume of MKUs of the same order of magnitude as the estimated volume. We have not performed measurements for 78% since we are focusing on the more realistic ratios. We can observe that for 30%, a volume of 1. 2 × 10 6 MKUs leads to a median online detection time of 85 ms. We conclude that our estimation is adequately accurate to provide relevant insights. For 5%, we deduce that for a volume of approximately 8. 6 × 10 6 MKUs, the median online detection time reaches 85 ms. We need to continue the evaluation on larger datasets in order to verify that we reach the estimated volume for the ratio of 5%. Finally, we compare the improvement provided by H-RICE-Sy with respect to the detection time, using RICE-Sy as the baseline.
To do so, we measure for both systems the evolution of the median detection time when processing a small and a medium sized dataset: D 1 and D 3 , see Figure  13 , in order to see if the improvement provided by H-RICE-Sy scales with the amount of processed queries. For both dataset, 30% of the MKUs are affected to malicious users, thus they are processed online by H-RICE-Sy. Choosing 5% or 30% both match our assumption that only a small quantity of users are malicious, however we choose the second value to consider a worst situation where more queries have to be processed online. The peaks at the beginning of each curve depicted in Figure  14  are present since the computation of the median detection time is performed on only the first measurement of the first query. Figure  14a  shows that, as expected, the possibility provided by H-RICE-Sy to process only queries of malicious users online reduces the median detection time.
Moreover, we also observe that the constant increase in the QHL leads to a constant increase in the median detection time which is similar for both RICE-Sy and H-RICE-Sy. We can observe in Figure  14b  that the improvement of H-RICE-Sy scales with the size of the dataset. In the first experimentation, we have observed the limits under which the trade-off can be avoided. With a small number of malicious users (5%) and a short detection time (80 ms), H-RICE-Sy is capable of processing a high amount of MKUs without having to deploy further strategies to satisfy the trade-off. Although the system continues to process queries, or if it changes its parameters, it will always reach the maximum volume which can be processed. Therefore, as a future perspective, the H-RICE-Sy must be endowed with a mechanism deployed in the dispatcher module which detects when the limit is reached by the system.
The obtained personal data by itself can be non-sensitive in most situations and does not breach someone's privacy, but combined with external data from other databases, it can help to reach more sensitive data, escaping the vigilance of InfDSs which act locally on the databases. A complete use case illustrating this issue is shown in  [11]. In this use case, distinct services are collecting both personal and sensor data of their customers. Authorized access to those data is provided by services to external users, with respect to the customers' consent. Dishonest users, however, can obtain indirectly access to sensitive personal data stored by the service by performing inference attacks that leverage the Distributed Dependency Strategy (DDS)  [29].
Conclusions In this paper we have presented RICE-Sy, a novel inference detection system suitable to capturing inference attacks on raw sensor data. RICE-Sy is based on RICE-M (Raw sensor data-based Inference ChannEls Model), an extensible model that represents both inference channels created by new learning algorithms on sensor data and the corresponding knowledge capturing metadata on the queried sensor data. A proof of concept of RICE-Sy, considering the MHEALTH inference channel, has been realized and validates the possibility of detecting inference attacks involving raw sensor data. Sensor data become large very quickly. As a consequence, efficiently detecting inference attacks at query-time is mandatory. To reduce the detection cost, we have first proposed a set of conceptual optimizations which aim at reducing the user knowledge to process.
5, contain different volumes of MKUs, see the table in Figure13. Evolution of median online time on datasets with fixed volume of MKUs and varying ratio of queries affected to malicious users, and thus processed online. The table on the right side describes the volume of queries and MKUs for each dataset. 5%, 30%, 78% of malicious users. Median online time [ms] 20 40 60 80 Acceptable time 12 14 17 20 62 76 16 25 101 19 33 204 22 38 284 45 85 #Query #MKU D1 6037 23837 D2 11887 46439 D3 19875 78976 D4 59688 234336 D5 79454 311323 D6 296180 1167886 0 D1 D2 D3 D4 D5 D6 Datasets identifier Figure 13.
tephra is a unique volcanic product with an unparalleled role in understanding past eruptions, longterm behavior of volcanoes, and the effects of volcanism on climate and the environment. Tephra deposits also provide spatially widespread, high-resolution time-stratigraphic markers across a range of sedimentary settings and thus are used in numerous disciplines (e. g. , volcanology, climate science, archaeology). Nonetheless, the study of tephra deposits is challenged by a lack of standardization that inhibits data integration across geographic regions and disciplines. We present comprehensive recommendations for tephra data gathering and reporting that were developed by the tephra science community to guide future investigators and to ensure that sufficient data are gathered for interoperability.
Introduction The term 'tephra' here indicates any airborne pyroclast produced by an eruption, regardless of grain size, shape, or composition  1. Tephra plays a critical role in understanding past eruptions  [2] [3] [4] [5] [6] [7] [8] [9] [10]  , long-term behavior of volcanoes  11  , including fundamental processes from the deep earth to the atmosphere, effects of volcanism on climate  [12] [13] [14]  and the environment  15  , and in providing time-stratigraphic constraints for both geologic events (e. g. , geomagnetic reversals  16  , earthquakes, tsunamis  17  ) and human history  18, 19. Refer to Table  1  for definitions of terms used herein. Interdisciplinary in its nature, research involving tephra spans a diverse array of scientific and societal interests (Table  2 ). Because of tephra's critical role in numerous fields and the diversity of data types involved in its study, standardization of practices and reporting standards, both within and among disciplines, would greatly facilitate mutual understanding, enable the introduction of new research frontiers, and allow for reusability of results even by practitioners outside of the scholarly domain in which the data were originally gathered and results reported.
To highlight the importance of documentation and reporting practices, we offer a few examples of data reuse both within and outside the original domain, and introduction of new research possibilities based on legacy data. In 1971, Walker and Croasdale 2 published a detailed set of grain-size, thickness, and stratigraphic data that were shown on a geocoded map, for the prehistoric Fogo A deposit, Azores. This provided the first case study of a tephra deposit in which comprehensive, well-documented data collection produced a dataset that could be utilized by the broader scientific community. The comprehensive nature of the Fogo A dataset allowed numerous further uses for volcanological analysis, including dispersal index (a measure of deposit spread)  20  and characterization of phreatomagmatism  21  , eruption rate  22  , thickness-area calculation (another measure of range)  [23] [24] [25]  total grain size  26  , error analysis and isopach construction  27, 28  , and eruption dynamics  24, 26  over multiple decades following the original work.
This example shows that comprehensive, well-documented data collection can facilitate wide reuse that leads to insights (likely) unforeseen by the original investigators; we thus credit Walker and Croasdale 2 with the earliest use of a form of best practices in data collection and reporting for tephra studies. Saxby et al. 29  used distal cryptotephra found by tephrochronologists and archaeologists to constrain physical volcanological parameters, such as cloud height, from the major, past (unwitnessed) eruption that created the Vedde ash in Iceland. This example shows the use of well-documented data collected for two widely different scientific purposes, in yet a third discipline. Recent studies on the use of well-documented tephra deposit information  [30] [31] [32] [33] [34] [35] [36] [37] [38]  coupled with visual observation of volcanic plume height, to improve estimation of eruption source parameters (i.
e. , mass flux of gas and ash from a volcanic vent)  39  further illustrates the value in collecting comprehensive datasets that follow some consistent guidelines, and that can therefore be reused to advance the understanding of how, when and why tephras are produced. Future innovation and discovery in disciplines that use tephra may thus see phenomenal growth if standardization is widely implemented to generate datasets that are compatible across disciplines. This will improve interdisciplinary collaboration and the interoperability of regional and global tephra datasets. Current, more fragmented, research practices are typically based on, and limited by, physical volcanological datasets local to a volcano  [40] [41] [42]  , regional datasets stored in disconnected databases and offline files of potentially inconsistent geochemical and lithostratigraphic data  [43] [44] [45] [46] [47] [48] [49]  , and a few sparse datasets of volcanic ash distributions at continental and transcontinental scales  [50] [51] [52] [53] [54] [55] [56]  , which are utilized by a diverse community of researchers from different disciplinary backgrounds.
With datasets in their current fragmented and inconsistent state, the tephrochronologist struggles to determine the volcanic sources and distribution of isolated tephra or cryptotephra (non-visible ash  57  ) layers, especially those transported thousands of kilometers from potential volcanic sources. Jensen et al. 55  , for example, showed that the White River ash was long known as a regional stratigraphic marker in Greenland and northern Europe well before it was correlated to an eruption in Alaska. The process of finding potential matches or correlations for a tephra deposit is daunting and determining a unique match may be impossible with the currently available, disconnected datasets. In such circumstances, the paleoclimatologist who found tephra might be unable to link paleoclimate records, and the archaeologist might be unable to use tephrochronology to date a critical cultural site because of uncertainty over the source of a tephra.
Similarly, the volcanologist may be faced with data discovery and integration challenges when characterizing the physical and microphysical processes that took place in a volcanic cloud as it dispersed downwind to continental and global scales. The global nature of tephra studies and the rapid expansion of cryptotephra studies  [58] [59] [60]  have made advances increasingly dependent on the ability to identify and correlate tephra deposits across broad regions, but this ability is limited by traditional data gathering practices, lack of standardization and inaccessibility of tephra data. These factors, coupled with our growing knowledge of the potential limitations in characterizing tephra using traditional techniques, indicate that implementation of standard practices in tephra sample collection,.