dataesr/scientific-paragraphs-categorization huggingface dataset:
Crowdsourcing Visual User Profile Ratings We collect manual ratings m(U i ) for users U i in situation S via crowdsourcing. Like object rating, visual profiles are evaluated using a 7-point Likert scale that goes from -3 (strongly unappealing) to +3 (strongly appealing). Ratings are collected from 9 participants for 500 users from the YFCC dataset  [46]  with 100 images per profile. YFCC was sampled because it includes images that were shared publicly under Creative Commons licenses which allow reuse. The images of each profile are shown on a single page, along with the possible situation rating. Participants are asked to look at all the photos and provide a global rating for each user in each situation. As illustrated in the supplementary material, the evaluated situations are presented in the interface.
Profiles were presented randomly to participants to avoid ordering bias. Similar to Subsection 3. 3, inter-rater agreement is analyzed using the AD index  [6]. AD values are 0. 86 for ACC, 0. 77 for BANK, 0. 74 for IT and 0. 83 for WAIT. These values are within the acceptability bounds defined in  [5]  (AD ≤ 1. 2). There were at least two expert annotators for each situation whose ratings were well aligned with those of other participants. A clustering based on the similarity of profile ratings across situations which minimizes silhouette is presented in Fig. 2. There are 57 patterns discovered for the 500 profiles in the dataset. The ratings of the same users vary significantly from a situation to another. Our hypothesis that profile perception is context-dependent is confirmed. Unlike the object rating patterns from Fig.
Baseline Rating of Visual User Profile We attempt to obtain a reliable estimation of profile ratings in the modeled situations by using: (1) object ratings from Subsection 3. 3, (2) object detections proposed by models learned with dataset D and (3) the set C of visual profiles rated in Subsection 3. 5. Given a photo P i j , detectors search for objects from D and scores are predicted for all these objects by default. In practice, a threshold is needed to decide if an object is actually present. We define: dη (O l k , P i j ) = d(O l k , P i j ), if d(O l k , P i j ) ≥ η(O l ) 0, otherwise (2) where: d(O l k , P i j ) is the detector for object O l which evaluates the confidence of the k th raw prediction of O l in photo P i j ; η(O l ) is a detection threshold for O l. The threshold value η(O l ) in Eq.
LERVUP training Visual profile rating is modeled as a regression problem that exploits the user-level descriptor described in Algorithm 1. LERV U P training is deployed as a pipeline process. First, individual object detections are validated within each image. Second, the image-level descriptor is constructed per image. Third, clustering is applied to group together similar image descriptors and discover relevant patterns for the entire training set. Fourth, the discovered patterns are concatenated to build the user descriptor. Finally, a random forest regression model is used to learn the rating of visual user profiles. Random forest was chosen because it is robust to data that contain non-linear relationships between features and target variables  [23, 44, 53]. Note that the training is scalable since its optimization takes less than an hour per situation on a standard Intel Core i7 processor.
Evaluation The main objective of this first evaluation is to assess the feasibility of the task. Note that the user profiles dataset is not large enough to split it into train, validation and test subsets which have sufficient size. We thus split the dataset in training and validation sets L, and V which include 400 and 100 profiles, respectively. The optimal configuration of each method on V is obtained using grid search and reported below. Details about optimized parameters and their ranges are given in the supp. material. Results are also provided for the ablation of 50% of user profiles or 50% visual objects.
Object Detection Dataset and Models The coverage ensured by the detection dataset is important to enable processing of different types of visual content. As we mentioned, we merge three existing datasets: Open-Images  [27] , ImageNet  [40]  and COCO  [29]. Whenever an object is present in more than one dataset, a balanced sampling is performed. The resulting dataset includes 269 objects and 137976 images. We limit imbalance by retaining at most 1000 images per object. The average and standard deviation of the distribution are 513 and 305, respectively. Detectors are trained with mobile and generic models. The mobile model (MOBI) is a MobileNetV2  [41]  with depthwise convolutions, which offer a good precision/speed tradeoff. The detection head is a Single Shot MultiBox Detector  [30] , a fast single-stage method that is adapted for edge computation.
LERV U P f r is also better than LERV U P , with up 15 points gained over it (BANK with MOBI detector). The boosting of highly-rated objects via the attention mechanism from Eq. 1 is thus validated. The four modeled situations have variable performance. WAIT is the easiest situation (correlation up to 0. 68) because the detection dataset contains a large number of food and beverage-related objects, which are often easy to detect. WAIT approximates an upper-bound performance one can expect with the available detection dataset. BANK is the most challenging situation tested, particularly for MOBI, which offers a maximum correlation of 0. 42. More object detectors are required to improve results for this situation. Among the baselines, BASE f r η is better than BASE η and BASE. The introduction of focal rating has a smaller effect on the baseline compared to LERV U P.
Object selection (Eq. 4) and the use of individual detection thresholds (Eq. 3) have a larger impact than focal rating. Results for REG raw and REG pca are particularly interesting since these methods exploit a random forest training like LERV U P. The difference is that raw user representations, complete or compressed, are fed into REG, while LERV U P exploits the proposed compact descriptor. The difference of performance in favor of LERV U P validates the relevance of the proposed descriptor. To test the influence of the amount of training data, we ablate half of the user profiles in LERV U P f r U=50% and half of the visual objects in LERV U P f r O=50% , respectively. The comparison of results obtained with the full training set (LERV U P f r ) to the ablated versions confirms that additional data is clearly beneficial.
This topic will be part of our future work. Technical biases are due to imperfections in the detection model, the available data and the LERV U P model. Detection model imperfection can be reduced via the use of more powerful deep detection architectures  [28, 36]. However, since the rating is most useful if done on the users' devices, models should remain tractable at the edge. A second technical bias is due to detector availability. Three existing datasets are merged to improve detector coverage. They seem sufficient for WAIT, which is well mapped in the detection dataset, but probably not for the other situations. We will extend the dataset with priority given to new objects which are highly rated in at least one situation. A third technical bias is due to data imbalance. We limited the maximum number of images per object to reduce imbalance while also preserving accuracy.
The imbalance will be further reduced with new annotations for both existing and new detectors. A fourth bias is related to the focus on images. The approach is extensible to other relevant data types, such as likes and texts studied in  [24]  and  [34]. We intend to exploit them in order to obtain more relevant and broader profile ratings. Finally, LERV U P provides performance gains compared to a series of baselines. The proposed method constitutes a first attempt to tackle profile rating, and important improvements over it are possible. LERV U P is implemented to mimic real decisionmaking processes and make them more transparent. A key challenge regarding transparency is related to the explainability of the decision-making process. The experiment presented in Section 3 indicates that the high-level explanation of situation-based feedback is more efficient than existing feedback.
Future work will focus on: (1) adding sufficient profiles to the dataset in order to obtain train, validation and test splits which are large enough and (2) improving the explainability of learned models, especially that of deep object detectors, which is the most challenging  [52]. The rest of the processing is easier to explain from a technical perspective but still requires a fair amount of AI-related knowledge. The code is available  2. An anonymized and modified version of the dataset is provided 3.
Each visual profile includes 100 photos published by the same user. You should browse through the entire set of images until you feel confident about the rating associated to each situation. You will assume that you are in a decision making position to: Ratings are provided on a seven points scale and range from "strongly repelling profile" to "strongly appealing profile". After reading this description of the task, each participant went through a training session whose objective was to discuss task-related questions with the experimenter. Then, participants provided basic demographic information and started the actual task. The interface used for collecting visual profile ratings is presented in Figure  3  with a subset of photos for one of the users included in the dataset.
Optimization of detection models Two neural networks are trained using the Tensorflow Object Detection API v1 on a Tesla v100 GPU which were  originally designed for edge computing and for classical GPU hardware respectively. In order to accelerate the training process and to reduce imbalance between visual object representations, we limit the number of images to 1000 per class. We also transfer parameters from pretrained models to train the final models faster. Both networks are trained with random horizontal flips as data augmentation technique. The larger network is a Faster-RCNN Inception Resnet v2  [2] , atrous version, pretrained on the COCO dataset. We use a momentum optimizer with a manual learning rate schedule starting at 0. 006 for 300000 steps, then 0. 0006 for 300000 more and finally 0.
00006 for the remainder of the training. The small network is a MobileNet v2  [3] , also pretrained on the COCO dataset. The learning rate schedule is 0. 005 for the initial learning rate, 0. 0005 at step 100000 and 0. 00005 at 200000. We use 8-Bit quantization aware training applied starting from step 100000, in order to speed up inference on mobile.
We investigate the microphysics of contrail formation behind commercial aircraft by means of the particle-based LCM (Lagrangian Cloud Module) box model. We extend the original LCM to cover the basic pathway of contrail formation on soot particles being activated into liquid droplets that soon after freeze into ice crystals. In our particle-based microphysical approach, simulation particles are used to represent different particle types (soot, droplets, ice crystals) and properties (mass/radius, number). The box model is applied in two frameworks. In the classical framework, we prescribe the dilution along one average trajectory in a single box model run. In the second framework, we perform a large ensemble of box model runs using 25 000 different trajectories inside an expanding exhaust jet as simulated by the LES (large-eddy simulation) model FLUDILES.
Trajectory ensemble data Vancassel et al. (  2014 ) used the LES solver FLUDILES to simulate the plume evolution in a 3D domain over 10 s starting behind the CFM56 engine nozzle of an A340-300 aircraft without treatment of the bypass. They sampled the plume with 25 000 trajectories that were randomly positioned inside the initial plume area A 0 = π r 0 2 with radius r 0 = 0. 5 m. We assume that all trajectories represent an equal share of the plume, e. g. in terms of air mass and number of soot particles. For each trajectory, the temperature evolution T 3D,k (t) has been tracked. We use these data to infer the dilution factor (or equivalently the entrainment rate) along each trajectory k by assuming that temperature is a passive tracer: D k (t) = T 3D,k (t) -T 3D,a T 3D,0 -T 3D,a. (17) T 3D,0 = 580 K and T 3D,a = 220 K are the plume exit and ambient temperature of the FLUDILES simulation.
25 s instead of 0. 6 s for the FLUDILES dilution. One reason for Figure  6. Temporal evolution of the apparent ice number emission index under baseline conditions of Lewellen (2020, "Lew20") as summarized in the bottom half of Table  1. The simulation initialization uses a monodisperse (dashed), bidisperse (dotted) or log-normal (solid) size distribution of soot particles (see also inserted legend). Panel (a) juxtaposes the FLUDILES based average traj evolution (blue) with the evolution using Lew20's dilution (green) and with the results of Lew20's box model (orange). Lew20's data are available up to a plume age of 0. 4 s. Note that the dashed and dotted green lines are mostly covered by the solid green line. Panel (b) depicts the FLUDILES ensemble mean evolution (red lines) with the LES results from Lew20's Fig.
2  for three different turbulent realizations (remaining colours). that is the FLUDILES dilution being too weak since temperature is treated as a passive tracer (Sect. 2. 3). Another aspect of physical nature is our higher initial plume radius (i. e. 0. 5 m instead of 0. 3 m in Lew20), causing the jet plume to dilute on a longer timescale. Lew20 analysed the effects of increasing engine size and found significant impacts on contrail ice crystal formation, particularly for higher aerosol number emission indices (see his Figs. 13  and 14 ). Using the bidisperse soot initialization (dashed blue), we find AEI ice = 0. 5EI s for the FLUDILES dilution as the 10 nm soot particles are not activated into water droplets. Yet, all soot particles are activated for Lew20's dilution. This is remarkable as for both dilution data the maximum plume water supersaturation would be the same if the microphysical processes were suppressed.
The reason for the different activation fraction is that the time period between droplet formation and freezing is longer, and correspondingly also the cooling rates are lower within this period in the FLUDILES case. This leads to a reduced peak saturation ratio, which is smaller than the critical saturation ratio for the activation of the 10 nm particles. For the log-normal size distribution (solid blue), some smaller soot particles are only temporarily activated but then shrink again as the larger droplets originating from the larger soot particles deplete water vapour. With Lew20's dilution (solid green), there is not enough time between activation and freezing to deactivate the smaller droplets. This difference originating from the different dilution data implies that the deactivation phenomenon, which we frequently encountered in our results, may not occur that frequently if plume cooling proceeds faster.
Moreover, the deactivation phenomenon leading to the large difference between FLUDILES average traj and ensemble mean results may be exaggerated with the FLUDILES dilution. This implies that the difference between the average traj and the ensemble mean could be smaller with a stronger dilution. We test this hypothesis in the following discussion section. Similar to our study,  Paoli et al. (2008)  compared average traj and ensemble mean results for two specific parameter settings. They also used a set of 25 000 trajectories, even though it is not the same data set provided by  Vancassel et al. (2014). Several aspects are similar to ours, for example, the quasi pulse-like activation versus a formation period. Interestingly, they find that for the average traj, the final ice crystal number is even 3 times lower than for the ensemble mean.
Discussion In the previous section, we have shown that ice crystal formation is initiated significantly earlier, and the number of formed ice crystals may be even larger if we use Lew20's data instead of the FLUDILES dilution data. Furthermore, we stressed that some differences found between the ensemble mean and the average traj results may be particularly due to too weak a dilution. In the following, we will estimate the time lag of the plume mixing and discuss potential improvements in our results by "accelerated" trajectory data. While 38 % of the total combustion energy E tot is used for the propulsion of the aircraft (according to the A340-300 propulsion efficiency), the remaining energy (62 %) is transferred into the jet plume in the form of kinetic energy E kin and thermal energy E therm.
More specifically, we artificially increase the dilution rate of each FLUDILES trajectory such that in the end the average dilution of this corrected trajectory ensemble, D AT,F2L , matches the dilution of Lew20, D AT,Lew20. At each time step, we diagnose c F2L (t) := D AT,Lew20 /D AT,FLUDILES , where D AT,FLUDILES is the average dilution defined at the end of Sect. 2. 3, and "F2L" stands for "FLUDILES to Lewellen". For each trajectory of the existing database, we use a modified dilution evolution defined as D k,F2L (t) := c F2L (t) • D k,FLUDILES (t). It is easy to show that the average traj dilution D AT,F2L of the modified trajectory ensemble equals D AT,Lew20. Figure  7  shows the sensitivity of the final freezing fraction of soot particles (φ frz,f ) to (a) EI s and (b) T a for the original FLUDILES simulations (blue and red lines, as already depicted in Fig.
Conclusions and outlook In this paper, the particle-based Lagrangian Cloud Module (LCM;  Sölch and Kärcher, 2010)  has been extended to cover the specific microphysics of contrail formation. One of our objectives was to test the newly extended LCM model in a simple box model framework prior to its coupling to the 3D LES model EULAG. Furthermore, our aim was to analyse the most relevant sensitivities of contrail ice crystal numbers to various soot properties and ambient conditions and to compare our results with previous studies. We juxtapose the original FLUDILES trajectory ensemble (red) with the sped-up ensemble (magenta), whose average dilution matches that of  Lewellen (2020). The blue and green lines show the corresponding average traj evolution of both ensembles. Additionally, the results from the parameterization of  Kärcher et al.
Acknowledgements. The scientific work has been funded by the  Deutsche Forschungsgemeinschaft (DFG)  within the project " BI   2128/1-1 ". We thank  David Lewellen  for providing us with simulation and dilution data published in Lewellen (2020) and his constructive review comments, and we thank  Klaus Gierens  for his internal review. Financial support. This research has been supported by the  Deutsche Forschungsgemeinschaft  (grant no. BI 2128/1-1 ). The article processing charges for this open-access publication were covered by the  German Aerospace Center (DLR). Review statement. This paper was edited by  Martina Krämer  and reviewed by  David Lewellen  and one anonymous referee.
Xiaomi is the worldwide fitness tracking leader. In 2020, Xiaomi sold 13. 5 million devices and had 24. 5% of the market share  [Cor20]. Nevertheless, the Xiaomi fitness tracking ecosystem received little attention from security researchers, despite being pervasive and promising security and privacy guarantees to its users  [Hua20]. Currently, there is only incomplete and outdated information about Xiaomi security mechanisms [HPK16, FFM + 17]. On the other side, Fitbit (its main competitor) has received more consideration from security researchers. For example, recent work demonstrated that popular Fitbit devices are susceptible to attacks such as packet sniffing, data exfiltration, and code injection through firmware updates [RCB13, CHMS14, GDS16, SJMdR16, CWP + 18]. In this work, we perform an extensive and up-to-date security evaluation of the Xiaomi fitness tracking ecosystem that is currently lacking.
As the vulnerabilities target the protocols' design, they can be exploited regardless of the hardware and software details of the target (e. g. , firmware, operating system, app, and BLE versions). Additionally, those issues might even be exploitable on other Xiaomi products sharing the same application-layer security mechanisms. To demonstrate the impact of the presented vulnerabilities, we develop and evaluate six practical attacks on actual devices. With our over-the-air (OTA) attacks, an attacker in Bluetooth range with a victim can impersonate a tracker to an app, an app to a tracker, man-in-the-middle them, and eavesdrop on their communication. Alternatively, with our remote attacks, indicated in the paper as software-based (SB), an attacker can remotely eavesdrop on data from a tracker or impersonate an app by abusing Android BLE API within a malicious app.
Our attacks are high impact because they affect the whole Xiaomi ecosystem and enable the attacker to achieve valuable goals such as eavesdropping on sensitive data exchanged by a tracker and a smartphone (e. g. , health data, SMS, and notifications) or sending arbitrary commands to the tracker and the smartphone. We developed breakmi, a security evaluation toolkit for fitness tracker ecosystems to automate our RE efforts and attacks. breakmi has three modules: protocol dissector, security mechanisms, and attacks. The protocol dissector module understands Xiaomi's proprietary application-layer protocols, allowing for fast and automated analysis of its application-layer packets. The security mechanisms module reimplements Xiaomi's custom security mechanisms, such as Pairing and Authentication.
The attacks module deploys our attacks automatically, including over-the-air or remote impersonation and MitM on arbitrary trackers and apps. We will release breakmi in the open after responsible disclosure. To show the effectiveness of our attacks, we present an extensive evaluation of Xiaomi trackers and apps. In particular, we successfully attacked all Xiaomi trackers released since 2016 (i. e. , Mi Band 2/3/4/5/6 and Amazfit Cor 2) and the latest versions of the Xiaomi fitness mobile apps (i. e. , Mi Fit version 4. 8. 1 and Zepp version 5. 9. 2). Two of the presented attacks are remotely targeting the Android platform. We successfully conduct them on six popular Android versions (i. e. , Android 6/8/9/10/11/12) to confirm their widespread impact. According to  [Sta21] , these versions represent 90% of the Android ecosystem.
Bluetooth Low Energy (BLE) BLE is the de-facto standard wireless technology for low-power wireless services, including fitness tracking. It is defined in the Bluetooth standard  [SIG19]  and provides a client-server architecture to exchange data using a specific format. Security-wise, BLE includes pairing and session establishment mechanisms that should provide confidentiality, integrity, and authenticity guarantees at the link-layer. The BLE client-server architecture is specified by the Generic Attribute Profile (GATT) and uses the Attribute Profile (ATT) protocol  [SIG19, p. 1531 ]. In Bluetooth terminology, the client is defined as the central, and the server as the peripheral. The client sends read, write, and notification requests to the server. The server answers accordingly to the request type and the availability of data.