dataesr/scientific-paragraphs-categorization huggingface dataset:
Amplified impact on consumers and victims The metaverse involves a multisensory environment. Due to complex and sophisticated features such as more graphic, 3D design, immersive visual and auditory experience, when unwanted and privacy-invasive contents proliferate in the metaverse, they may be felt as more intrusive and are likely to have a greater negative impact on the users or victims. Thus, privacy violations in the metaverse are likely to lead to more severe consequences, which are also referred to as an amplified technical impact  (ISACA, 2014; Kshetri, 2014). Users in the metaverse also face a higher susceptibility to manipulation by businesses. For instance, VR headsets can collect more and richer data about users compared to traditional screens. Companies thus have more incentives to collect user data and share such data with third parties which can be used for profiling to deliver customized advertising.
To take an example, Meta (Facebook) announced that it was working on a high-end VR headset Project Cambria, which will have capabilities that are not possible with headsets currently being used. New sensors in the device will allow the user's virtual avatar to maintain eye contact and reflect facial expressions  (Bonifacic, 2021). In this way, Project Cambria would have the capability to mirror a person's face and eye movements in VR. Meta can give this information to advertisers, which can help the latter measure users' attention more accurately. The measurement can help target users with ads and influence them to buy product. Meta has stated that the company currently does not share eye-tracking data with advertisers. The company, however, has not committed that it will not collect and share such data in the future  (Hunter, 2022).
Likewise, security breaches in the metaverse may be associated with more adverse consequences compared to the current Internet. For instance, in addition to personal data and financial data, perpetrators in the metaverse can also target crypto-assets  (Merre, 2022). In addition to financial harms, cyberattacks against metaverse systems may also cause physical harms. For instance, VR headsets, which are a gateway to user data, and AR headset can be hacked  (Creamer Media Engineering News, 2022) , which may lead to physical harms to victims. Researchers found that by exploiting VR systems, it is possible to control the activities of immersed users and physically move them to a location without their knowledge  (Casey et al. , 2021). For instance, by manipulating a VR platform and resetting the hardware's physical boundaries, an adversary can influence a user to take actions that make them fall down a flight of stairs and cause serious injuries.
Attribution models in metaverse. The attribution model is used to decide the digital advertising spend on different publisher sites  (Ghose & Todri-Adamopoulos, 2016). There are different attribution models in digital marketing like the last interaction model, first click model, time decay model besides, Google and Meta attribution model. There is a need to devise new attribution models for metaverse as to date this has not featured in the extant literature. The specific parameters regarding different customer touchpoints and their measurement issues that feed data to the attribution model also need to be explored. Future research should also consider the temporal and spatial aspects while finalizing the attribution model for the metaverse. 2. 10. 3. 6. Privacy issues in metaverse. The General Data Protection Regulation (GDPR) and its equivalent in different countries have brought privacy compliance as a key consideration for all organizations  (Ghosh, 2018).
Already 360 • tools provide an example of what can be done for visitors when selecting appropriate products and services. Equally Google Street View also integrates multimedia to provide realistic representations of geotagged content. Videos and content from destinations will also support route and itinerary development to meet the needs of the criteria of customers and this will be updated in real time based on contextual data on location  (Buhalis & Sinarta, 2019). During travel and whilst at the destination, metaverse will be used for navigation and guidance processes. Visitors will use AR and VR to interact with physical space and local resources. For example, AR also different layers of information on artefacts and cultural heritage monument. VR and AR will be used interchangeably on location to help the interpretation of destinations and historic places.
For example, with Walmart, Nike, and McDonald's set to enter the metaverse, consider a real-time metaverse scenario where a digital avatar glances at virtual restaurant shopfronts enough times to trigger personalised advertising for food. Although mundane, ephemeral actions have long proven difficult to quantify, in the metaverse AI will learn to generate value from the ephemeral. The repetitive role of ephemeral paratexts (Pesce & Noto, 2016) -such as pop-up ads, short-lived announcements, and hypertexts will also likely be transformed in the metaverse. Be it for good or dystopian ill (e. g. : Zuboff, 2019) , the metaverse will present unique and superlative datasets for study of the ephemerally repetitive.
Our method is a practical extension of the Latent Dirichlet Allocation and is used for this Double Clustering task (LDA-DC). We first validate the method on artificial datasets, then we apply our method to two real problems of patients stratification from cytometry and microbiota data. We observe that the LDA-DC returns clusters of patients and simultaneously clusters of cells related to patients' conditions. We also construct a graphical representation of the results which can be easily understood and a great help for human experts.
Background Human disorders have a highly multifactorial nature and depend on genetic, behavioral, socio-economic, and environmental factors. Many examples of such complex diseases such as cardiovascular diseases, non-alcoholic liver cirrhosis, type II diabetes, or even other pathologies such as autoimmune diseases  [1]. The number of metabolic diseases, cancer, and autoimmune pathologies has increased significantly in recent years, making research in this field a public health priority  [2]. In parallel, bioclinical routine datasets have expanded in conjunction with all kind of "omics" data, from both the host and microbiota, as well as metabolomic, proteomic, and cytometry data  [3]. All these types of data have some underlying structure on their own, taking values on different scales, with different variability, and are differently distributed.
The estimation of the model is done using the Expectation-Maximisation (EM) algorithm. Some attempts to adopt the Latent Dirichlet Allocation (LDA) to the single cell data were recently made. So,  [18]  applied the LDA to a database with approximately 50 human tissues to discover similarities between them; the LDA was also tested on single cell mouse data to discover variations in early embryonic development stages. An important characteristic of the single-cell data is that the data is structured;  [19]  states that any clustering method for the single-cell data should account for the hierarchical structure of cell types, and proposes new metrics to evaluate clustering performance. To construct tree structures which reflect the hierarchical nature of single cell data,  [20]  explore a hierarchical extension of the LDA to identify clusters of cells.
Validation of the proposed method on simulated data We tested the double clustering approach on difference scenarios. We tested 2 and 4 phenotypes, and we varied the distance between the probability vectors to vary the difficulty of the clustering problem. If the Euclidean distance between the phenotype probability vectors is small, the clusters are not well-separable, there is a significant overlap between the groups. If the Euclidean distance between the probability vectors associated with the phenotypes is big, the clusters are easily separable, and we can expect a reasonable performance. The overlap between the clusters can be controlled by the variance. Figure  1  illustrates the results on the synthetic dataset. The subplots A and B show our results for the case with 2 phenotypes, and C and D illustrate the setting with 4 phenotypes.
Acute Myeloid Leukemia: AML Dataset The AML benchmark dataset is an unbalanced dataset of people with AML syndrome and healthy subjects. This dataset includes flow cytometry measurements for several batches (tubes) of different biomarkers set. In order to balance the learning procedure, for each tube, we selected the same number of AML and non-AML patients excluding several non-AML patients. First, we applied the K-means with 2 D clusters, where D is the number of markers in a tube. Then, we annotate each cell according to its origin (patient) and its cluster. For each patient, we obtain a list of cell types. We also define the vocabulary equivalent to the number of clusters of the K-means as well as the number of topics (here 2, since there are 2 conditions: AML and non-AML). At this point, each patient (observation) is a list of cell types, and we apply the LDA on the count data.
Also, we applied an arbitrary threshold of 30%, and remove the links between patients that occur less than 30% of experiments. This matrix can be considered as one describing connectivity in the data, and we visualise a network where nodes are patients, edges are the connections between the patients, and edge weights are the connection frequencies. The obtained networks are shown on Figure  4. Adding more information to such a graph, we modify the size of the nodes so that it reflects the connectivity (the bigger the node, the bigger node degree). The patients are coloured according to their clinical condition (CD or Healthy), or to their enterotypes which are also provided with the dataset. So, a network generated from the cytometry data, clusterises patients into 3 groups: one group with data of sick patients only, and two other groups containing both sick and healthy subjects (Figure  4 B ).
It is easy to see that some cellular phenotypes are strongly related to the true patient status. The cells identified are because of their abundance associated with the disease and are identified by specifics fluorescence value and therefore biological markers that can be investigated further. One of our main results is shown on Figure  3 , panel D: the identified cell populations are well distinguished according to the clinical condition (AML versus non-AML). Finally, we applied our approach to microbiota data where we have access to two data type: cytometry data and bacterial abundance. The first data type is a cytometry dataset targeting mainly bacterial membrane proteins. The second is a count matrix for each patient, where each bacteria is identified (genus data). Our main result on the cytometry dataset is the patients' stratification according to their clinical status using two topics, the separation is reasonable but not perfect.
Simulated data Cell generation: We constructed an artificial dataset to validate the proposed method. In order to mimic real flow cytometry datasets, our main hypothesis for the data generation is that the underlying distribution of fluorescent data can be efficiently approximated by (multivariate) Gaussian distributions. So, each marker can be seen as a mixture of two Gaussians with different mean: one is associated with positive subsets (high mean), and the second one is associated with negative subsets (low mean). In order to test for robustness we can vary the standard deviation of the distribution with high standard deviation making more difficult to separate low from high. Therefore the phenotype of a cell is a real vector of N dimensions (think of N as the number of fluorescent marker under consideration).
Real Benchmarks To illustrate the efficiency of the proposed method, we selected two real annotated benchmarks. AML (Acute Myeloid Leukemia) dataset: This dataset  [32]  has 2872 samples of flow cytometry standards collected from 359 AML (n=43) and non-AML (n=316) individuals. It contains results from 8 experiments corresponding to different tubes with different markers (note that tube 1 is an isotope control, and tube 8 is unstained). Cytometry and genus data: The dataset we use contains FACS cytometry and 16rRNA sequencing data coming from two studies:  [25]  and  [24]  respectively. Note that the original cytometry data comes from  [24]  and are paired with the 16rRNA data. However, we use the data from  [25] , since this data set is pre-processed (noise reduction using various transformations and algorithmic methods, see Analysis part in  [25]  for more details).
Availability of data and materials: Our code (Python) is publicly available for scientific purposes at: https://github. com/ElieElHachem/LDADC. AML data from  [32]  are available at https://flowrepository. org under the accession FR-FCM-ZZYA. Cytometry data from  [25]  are available at https://flowrepository. org under the accession FR-FCM-ZYVH, and 16sRNA (genus data) from  [24]  are stored on github at https://github. com/prubbens/PhenoGMM CD.
( 3 ) Despite a somewhat different zenith angle dependence, both functions exhibit a monotonic decrease with z. In combination with the receiver clock offset, PCO changes can thus In practice, zenith-angle-dependent changes of the modeled pseudorange are also caused by variations in the tropospheric delays, which are commonly expressed as the product Δρ trop = ΔT • M(z) (4) of a zenith delay change ΔT and an elevation-or zenithangle-dependent mapping function M(z). For the present study, we compare three mapping functions of different complexity including two fully analytical models and a tabular model representative of more elaborate tropospheric mapping functions used in state-of-the-art GNSS processing software. Neglecting Earth curvature and assuming a plane atmosphere, the mapping function can be approximated as M planar (z) = 1/ cos(z).
Validation For validation of the semi-analytical model, dedicated PPP solutions were computed with two independent software packages, namely NAPEOS  (Springer 2009 ) and the Bernese GNSS Software  (Dach et al. 2015) , using code and carrier phase observations from a global network of 60 stations in 1day batches over a 10-day period. Tropospheric delays were consistently modeled in both cases with the GMF  (Böhm et al. 2006 ) and global pressure and temperature (GPT;  Böhm et al. 2007 ) models, adjusting one zenith delay per 2h interval along with daily gradients. Next to the nominal PCOs as given in the IGS antenna model, solutions with few-decimeter-level offsets on the PCOs of one constellation at a time were computed. Based on these, the corresponding ratios of station height and PCO changes were obtained (Table  3 ).
A 5 • elevation mask and the w 1 = cos 2 (z) observation weighting were applied in these PPP tests in correspondence to Case 4 of Table  2. The PPP simulation results obtained with the two software packages show a good agreement at the ±0. 001level among each other and with Table  2  for all constellations, thus indicating the overall validity of the semi-analytical model. Complementary to the PPP solutions, dedicated POD solutions with the Bernese S/W were computed to study the impact of PCO changes in the joint adjustment of GNSS satellite orbits and monitoring stations. Considering ten consecutive 1-day data sets, changes of less than 3 mm in orbital radius per meter of PCO change were observed for the individual GNSSs. This confirms the finding of  Zhu et al. (2003) , that the orbital radius is largely unaffected by the adopted PCO due to the dynamical constraints of the employed orbit models and provides additional justification that orbit changes can be neglected in the simplified semi-analytical model.
a b s t r a c t The visual scoring of gold standard polysomnography (PSG) is known to present inter-and intra-scorer variability. Previously, Somno-Art Software, a cardiac based sleep scoring algorithm, has been validated in comparison to 2 expert visual PSG scorers. The goal of this research is to evaluate the performances of the algorithm against a pool of scorers. Sixty PSG and actimetry recording nights, representative of clinical practice (healthy subjects and patients suffering from obstructive sleep apnea [OSA], insomnia or major depressive disorder), were scored by 5 different sleep scoring centers and by the Somno-Art Software. Intra-class correlation coefficient (ICC) and Wilcoxon Signed-Rank Test were calculated between each scorer and the average value of the 6 scorers, including Somno-Art Software.
In addition, epoch-by-epoch agreement between scorers were analyzed. Somno-Art Software estimation of sleep efficiency, wake, N1þN2, N3 and REM sleep fit within the interscorer range for the full dataset and the subgroups, except for underestimating N3 sleep in OSA patients. Additionally, Somno-Art Software overestimated sleep latency compared to the average scoring for insomniacs (þ4. 7 ± 1. 6min). On the full dataset, Somno-Art Software had good (0. 75 < ICC<0. 90) or excellent (ICC>0. 90) ICC scores for all sleep parameters except N3 sleep (moderate score, 0. 50 < ICC<0. 75). For the 4-stages epoch-by-epoch agreement, Somno-Art Software was slightly below that of the visual scorers except for the healthy sub-group where an overlap was demonstrated. Somno-Art Software sleep scoring shows a good interscorer reliability in the range of the 5 visual polysomnography scorers.
Introduction Polysomnography (PSG) visual scoring is the gold standard for measuring and characterizing sleep continuity and architecture. However, this task is known to suffer from inter-and intra-expert variability  [1, 2]. Although the scoring rules are revised and specified on a regular basis, they are still not specific enough to prevent variability in scoring, the mean agreement between 2 visual scorers being around 83%  [3, 4]. In parallel, automatic scoring algorithm do not suffer from intra-scorer variability as the scoring is highly reproducible. These new generation of scoring algorithms have drastically improved in the last years and gained interest as a possible option, even alternative to analyze sleep in specific settings  [5]. Somno-Art Software is 1 of these algorithms, analyzing sleep from cardiac (electrocardiogram  [ECG] ) and body movement signal (actimetry), physiological signals that have been shown to vary with sleep states  [6].
Somno-Art Software performances have been evaluated in healthy subjects and patients suffering from obstructive sleep apnea (OSA), insomnia (ISM) and major depressive disorder (MDD) with promising performances in sleep stages characterization against PSG  [7, 8]. However, Somno-Art Software was compared to a maximum of 2 visual PSG scorers and as previously mentioned, the interscorer reliability between visual scorers may impact the observed performances. The goal of this publication is to evaluate the performances of Somno-Art Software in scoring sleep against a panel of visual scorers. To do so, 60 PSG recordings representative of clinical practice (healthy subjects and patients suffering from OSA, ISM or MDD) have been scored by 5 different international sleep scoring centers and by Somno-Art Software.
Study sample To obtain a dataset representative of clinical practice, PSG recordings from 5 studies were randomly selected to be used as the dataset. All study protocols have been approved by institutional review boards in accordance with the Declaration of Helsinki and the guidelines on Good Clinical Practice. Written consent was obtained from all participants according to local requirements. In total, 60 PSG recordings from 15 healthy, 15 OSA, 15 ISM and 15 MDD were included in the analysis.
Polysomnography Multiple PSG recording systems were used in the various studies (Compumedics ProFusion PSG 3; Compumedics Siesta 802a [Compumedics, Abbotsford, Australia]) but all used the American Academy of Sleep Medicine (AASM) recommended electroencephalogram derivations (C4-M1, F4-M1, O2-M1), 2 electrooculogram electrodes, 2 chin electromyogram and 2 ECG electrodes. Five expert visual scorers (VS1-VS5) from 5 international sleep scoring centers (St Luke's Hospital, Chesterfield, USA; Concordia University, Montr eal, Canada; The Siesta Group, Vienna, Austria; CIRCS research center, Strasbourg, France; PPRS, Colmar, France) scored the full dataset. All the scorers received identical information on the scoring nights, consisting only in the group characteristic (healthy, OSA, ISM, MDD). The expert scorer from the Siesta group was assisted by the Somnolyzer software  [9].
Somno-Art Software The Somno-Art Software v. 2. 7. 0 [3. 2. 0] analysis was performed on precisely synchronized actimetry and ECG signals. Using heart rate at a beat-to-beat resolution and actimetry data at a 1 Hz resolution, sleep stage classification (W, N1þN2, N3, REM) was performed at a 1-s epoch resolution. The latter 1-s epoch classification was merged into 30-s epochs in order to be compared to visual scoring. To do so, the dominant stage (or the first occurring stage, if they were equally represented) was selected. The sleep classification algorithm is based on expert rules associated to Support Vector Machine (SVM) detectors. Precise data processing methodology is described in Ref. [7].
Statistical analysis Agreements between scorers on sleep parameters (sleep efficiency (SE), sleep latency (SL), W, N1þN2, N3 and REM sleep duration) were assessed with intra-class correlation coefficient absolute agreement, average measures, 2-way random model (ICC AAAvg ): The degree of absolute agreement for measurements that are averages based on k independent measurements on randomly selected objects  [10]. Similarly to another multi-scorer study, the average sleep parameters of the 6 scorers, the 5 visual scorers and Somno-Art Software (Av6), were considered to represent the true values of the parameters for each recording  [11]. ICC and difference of the means were calculated between each scorer and Av6. Koo et al. provides commonly cited cut-offs for qualitative ratings of agreement based on ICC values < 0.
Results Fig. 1  illustrates the difference of the means of each scorer (VS1-VS5 and Somno-Art) compared to Av6 for the main sleep parameters. On the full dataset, Somno-Art Software showed a mean SE comparable to Av6 (difference of the mean: 0. 1 ± 0. 9%, NS) and comprised within the interscorer range (visual scorer range: À3 ± 0. 6 to 1. 7 ± 0. 4%). Similar results were observed for the 4 sub-groups (healthy, OSA, ISM and MDD). In addition, ICC scores of SE on the full dataset was judged as excellent agreement for each scorer compared to Av6 (Table  1 ). With a difference of the means of 1. 7 ± 2. 2 min on the full dataset, Somno-Art Software was comparable to the visual scorer range in the estimation of SL (visual scorer range: À2. 2 ± 1. 2 to 1. 4 ± 0. 6 min). Somno-Art Software estimation of SL was not significantly different from Av6 except for the ISM group with a difference of the means of 4.
7 ± 1. 6 min (visual scorer range: À3. 7 ± 3. 5 to 0. 6 ± 0. 8 min). Additionally, all scorers showed excellent ICC scores compared to Av6 on the full dataset. For the full dataset, W results for the visual scorers showed a difference of means ranging from À8. 2 ± 1. 8 min to 14. 1 ± 2. 7 min compared to Av6. Somno-Art Software results overlapped with the range with a not significantly different mean (À0. 6 ± 4. 6 min). Somno-Art Software also overlapped with the interscorer range in the sub-groups analysis. For the full dataset, all visual scorers and Somno-Art Software showed excellent ICC scores compared to Av6. For all analyzed groups, Somno-Art Software results were consistent with the visual scorer range in the estimation of N1þN2 sleep. For the full dataset where the visual scorer range of N1þN2 sleep was of À17.