dataesr/scientific-paragraphs-categorization huggingface dataset:
, GAT) with 64, 128, and 256 hidden units using Citeseer dataset. We then launch Type I attacks using a fixed surrogate model (i. e. , GraphSAGE) with 64, 128, and 256 hidden Table  11 : The accuracy and fidelity score of Type II attacks using different response information on all the 6 datasets. Both average values and standard deviations are reported. The accuracy differences (in parenthesis) of the surrogate models to the target models are also reported. We use GAT as the surrogate model. units. In this way, we can observe the potential performance changes in different circumstances. The results are shown in Figure  9a  and Figure  9b , respectively. We can see that different hidden unit sizes adopted by the surrogate models have a limited impact on the accuracy performance. Our hypothesis is that the inductive GNN models employed as surrogate models are powerful enough to extract information from the responses.
Number of Epochs and Batch Size. The number of epochs and batch size are the other two hyperparameters that may affect attack performance but can be controlled by the adversary. Respectively they control the number of complete passes through the training dataset and the number of samples processed before the GNN model is updated. Batch size may affect the speed and stability of the learning process, while the number of epochs may lead to overfitting. To this end, we fix the surrogate model to GraphSAGE and the target models to GIN and GAT to understand the impact of both hyperparameters. We use 150, 200, 250, 300 for the number of epochs, and 400, 600, 800, 1000 for bath sizes. The results are summarized in Figure  10  and Figure  11 , respectively. Regarding the number of epochs, the accuracy is relatively stable with respect to different numbers of epochs (see Figure  10 ).
Summary of datasets. Coauthor is a user interaction network where nodes represent the users and edges represent interactions between them. ACM and Amazon are cooperative networks where nodes represent the papers/items and there is an edge between two nodes if they have the same author or purchased together. We use these datasets to verify the efficacy of our attacks given different graph characteristics (e. g. , graph size, node feature size, number of classes, etc. ). Statistics of these datasets are summarized in Table3.
The accuracy and fidelity score of Type II attacks using different response information on all the 6 datasets. Both average values and standard deviations are reported. The accuracy differences (in parenthesis) of the surrogate models to the target models are also reported. We use GIN as the surrogate model. 885±0. 004 (-0. 039) 0. 913±0. 004 0. 882±0. 001 (-0. 023) 0. 910±0. 003 0. 882±0. 004 (-0. 027) 0. 933±0. 007 Projection 0. 665±0. 026 (-0. 246) 0. 673±0. 023 0. 673±0. 021 (-0. 238) 0. 679±0. 014 0. 726±0. 030 (-0. 193) 0. 740±0. 026 Prediction 0. 841±0. 014 (-0. 070) 0. 877±0. 010 0. 880±0. 008 (-0. 031) 0. 922±0. 003 0. 896±0. 005 (-0. 023) 0. 952±0. 006 Citeseer Embedding 0. 814±0. 014 (-0. 097) 0. 836±0. 012 0. 889±0. 005 (-0. 022) 0. 924±0. 004 0. 884±0. 004 (-0. 035) 0. 930±0. 003 Projection 0.
Acknowledgement We thank the anonymous shepherd and reviewers for their feedback in improving this paper. This work is partially funded by the  Helmholtz Association  within the project " Trustworthy Federated Data Analytics " (TFDA)  (funding number  ZT-I-OO1 4 ). This work is also supported by the  Helmholtz Association's Initiative and Networking Fund  on the HAICORE@FZJ partition.
Among them, DBLP, Pubmed, and Cite- seer are citation networks with nodes representing publica- tions and edges indicating citations among these publica- tions. Dataset Configuration. For each dataset, we split them into three parts. The first part consists of 20% randomly sampled nodes that are used to train the target model M T. The second part consists of 30% randomly sampled nodes, forming our query graph G Q. We further show that our attacks are still effective with fewer nodes to form the query graph (seeFigure.
203) 0. 668±0. 058 0. 657±0. 039 (-0. 296) 0. 656±0. 041 0. 764±0. 029 (-0. 173) 0. 774±0. 028 Prediction 0. 892±0. 018 (0. 036) 0. 854±0. 016 0. 907±0. 010 (-0. 046) 0. 914±0. 005 0. 943±0. 005 (0. 006) 0. 953±0. 006 Amazon Embedding 0. 912±0. 010 (0. 056) 0. 860±0. 006 0. 933±0. 005 (-0. 020) 0. 941±0. 010 0. 939±0. 002 (0. 002) 0. 956±0. 004 Dataset M S (GIN).
Only two SNRs, one associated with SN 1957D  (Long et al. 1989 (Long et al. , 2012) )  and one object, B12-174a, that appears to be the remnant of a missed SN from the last century  (Blair et al. 2015) , have lines with the (>1000 km s -1 ) velocity widths that are seen in very young Galactic SNRs. Here we describe an analysis of a new homogeneous set of data on SNRs and SNR candidates in the M83, using the Multi Unit Spectroscopic Explorer (MUSE) on the VLT  (Bacon et al. 2010). These observations provide a uniform set of spectroimaging data covering a significant fraction of the inner bright disk of M83, allowing us to sample all of the SNR candidates in the observed region (both those with and without previous spectra), while also providing more extended spectral coverage than was previously available.
Combining the MUSE results with previous data, M83 has the most spectroscopically confirmed SNRs of any galaxy studied to date. The remainder of this paper is organized as follows: In Section 2, we describe the data used. In Section 3, we compile a new catalog of SNRs and SNR candidates, including a revisit to earlier candidate lists, as well as adding new MUSE and other previously unpublished candidates. In Section 4, we discuss the methodology used to extract the MUSE spectra and compare to previous spectroscopy that overlaps the MUSE mosaic region. In Section 5, we present an overview of the MUSE results, and in Section 6, we discuss the limitations that have developed in applying the primary [S II]:Hα ratio criterion for separating SNRs from H II regions. In Section 7, we highlight several individual objects of special interest; in Section 8, we investigate global trends in the MUSE spectra; and in Section 9, we compare the spectra to shock model grids.
644 μm as a new shock diagnostic and published a number of M83 SNRs showing this line. However, the situation in the complicated nucleus was set aside and not reported in that analysis. For completeness here, we include 20 new SNR candidates selected using the presence of strong [Fe II] emission but little or no optical emission, the vast majority of which are in the nuclear region. Most of these objects are seen in projection against dust lanes and are thought to represent heavily reddened SNRs seen primarily in [Fe II]. 11   (Blair et al. 2012) , and the bottom two panels show the corresponding data from MUSE. The scaling on the ratio maps is from 0 (black) to 2. 0 (white). Thus, lighter values indicate elevated values of the ratio. The magenta circles mark two previously identified SNRs (B12-101 and B12-088), while the cyan squares indicate two additional SNRs identified with MUSE (L22-013 and L22-009).
The improved exposure depth and higher S/N in the MUSE data are evident for this relatively sparse interarm field. The previous objects have elevated ratios in the Magellan data but even higher ratios in the MUSE data. The new objects stand out in the MUSE ratio and have identifiable emission counterparts, but they are too marginally detected in the Magellan data to have been identified previously as SNR candidates. For scale, the square regions are 5″ on a side. Among the SNR candidates, detailed studies of individual objects have identified a few as microquasars (or candidates) whose spectra also involve shocks. The microquasar MQ1 = D10-N-16  (Soria et al. 2014 ) is found just NE of the bright nuclear region, and more recently  Soria et al. (2020)  suggested that two closely spaced SNRs, B12-096 and B12-098, may be two shock-heated lobes of another single microquasar.
The New Catalog Our current list of SNRs and SNR candidates in M83 now consists of 366 objects as indicated in Table  1. The catalog includes the 278 nebulae listed by  Williams et al. (2019). To this, we have added a total of 87 other nebulae, 81 of which are reported here for the first time: 44 from our search of the MUSE data, and 37 new, previously unpublished candidates from reinspection of the earlier imaging data of the M83 nucleus. Six other objects previously published by  Dopita et al. (2010)  or  Blair et al. (2014)  that were considered too marginal to be included in the  Williams et al. (2019)  list have been reinstated here for completeness. This new catalog composes the largest optical SNR sample available for any single galaxy. For each object, we list a recommended name; coordinates; an estimated diameter; the distance of the object from the nucleus; the identification of any X-ray sources that are cospatial with the SNR candidate; an indication of whether the object was included in the list of  Williams et al.
The net spectrum (with units --ergs cm s Å 2 1 1 spaxel -1 ) was then obtained by straight subtraction after scaling to the same areal size as the object. As an indication of the resulting data quality, a selection of spectra is shown in Figure  3. Applying this procedure allowed us to treat the spectral extractions in an automated way, and in general this procedure worked well. (See below for comparison to objects that had prior spectra with Gemini GMOS. ) However, we do note that, particularly for faint objects, a different choice of background region would result in different line ratios for individual objects. However, this criticism would apply to almost any choice of background, even one made "by eye. " With the spectra in hand, we extracted central wavelengths, fluxes, and FWHM for the lines in the various spectra using a custom-built PYTHON script, assuming that the lines had Gaussian shapes.
Our purpose-built fitting routine makes use of the CURVE_FIT module of SCIPY. OPTIMIZE, which returns not only the best-fit values but also the covariance matrix. We have used the covariance matrix to establish 1σ error estimates under the assumption that the errors for the various values were uncorrelated. For the doublets, [O III] λλ4959, 5007, [O I] λλ6300, 6364, and [S II] λλ6716, 6731, we fixed the separation of the lines and fit a single FWHM to both lines, which particularly in the case of [O I] and [O III] produced more accurate results for the weaker of the lines in the doublet. Hβ and [S III] λ9069 were treated as singlets. After some experimentation, we decided to treat the [N II] λλ6548, 6583 and Hα complex as a single system, fixing the separation between the lines and using a single FWHM.
Although we have only presented the FWHM from the Hα fits, the FWHMs of all the lines were, within the errors, consistent with one another. In addition to the spectra of the SNR and SNR candidates, we extracted spectra for 188 other emission nebulae, listed in Table  3 , that are more likely to be H II regions. These objects were selected by displaying the MUSE Hα image and randomly selecting small isolated patches of Hα emission that spanned the surface brightness regime of the SNR candidates in the  Williams et al. (2019)  list. In choosing the comparison H II regions in this way, we have avoided the bias that might occur from selecting bright H II regions that are less typical. We also took care to select objects scattered throughout the portion of M83 covered by the MUSE data cube so as not to be biased by local effects.
Egocentric vision data captures the first person perspective of a visual stimulus and helps study the gaze behavior in more natural contexts. In this work, we propose a new dataset collected in a free viewing style with an end-to-end data processing pipeline. A group of 25 participants provided their gaze information wearing Tobii Pro Glasses 2 set up at a museum. The gaze stream is post-processed for handling missing or incoherent information. The corresponding video stream is clipped into 20 videos corresponding to 20 museum exhibits and compensated for user's unwanted head movements. Based on the velocity of directional shifts of the eye, the I-VT algorithm classifies the eye movements into either fixations or saccades. Representative scanpaths are built by generalizing multiple viewers' gazing styles for all exhibits.
The wearable eye trackers allow movement of participants. This helps in few studies, unlike remote eye trackers, such as understanding human cognitive processes and the dynamics of gaze shifts  [5] , classroom teaching  [6]  and many more. Most of the eye gaze datasets  [7]  are acquired using remote eye trackers with a few tasks for participants. There are a very few datasets acquired using head mounted eye trackers, but have not post-processed the gaze and data streams  [8]. The egocentric gaze data are prone to sudden head movements, missing gaze information due to eye tracker issues (sudden illumination changes, dilated pupil, etc), eye blinks, etc. Hence, we address these issues with a newly proposed egocentric gaze dataset collected from multiple viewers in a free viewing style with an end-to-end data processing pipeline.
Such handy and ready-to-use datasets are necessary for addressing many vision related problems like semantic segmentation, classification, summarization, etc, using both conventional machine learning and deep neural networks (DNNs). In this work, we aim to address the following issues when building a new egocentric dataset: 1) Users' distraction by the surroundings: unwanted vision distractions may occur suddenly and capture irrelevant information futile for interpretation, 2) Huge and unnecessary head movements: since eye movements are identified by their velocities and duration, head movements add their part to that of eyes misleading fixations and saccades  [9] , 3) Clear identification of fixations and saccades: generally, Tobii's I-VT filters  [10]  are used for identifying fixations and saccades.
They are sensitive to velocity threshold and need to be specifically tuned for an eye tracker, stimuli or environment  [10] , and 4) Building a generalized viewing trend from multiple users: the raw eye tracking data is then processed into scanpaths  [11]. A pattern that reflects the attention synchrony of different human subjects plays an important role in understanding how humans explore their surroundings  [11]. Subsequently, we employ a state space model (SSM) based statistical learning approach that characterizes the gaze pattern of human beings observing different category stimuli. This is part of our previous findings introduced as the state trajectory estimator based on ancestor sampling (STEAS) model and described in  [12]. In this work, we perform certain parameter tuning for adapting STEAS model to EG-SNIK dataset.
Since the computations with long and raw velocity vectors are computationally intensive, we uniformly sample gaze velocity from the available raw velocity vectors. This is done because the computations with the latter are intensive. STEAS model generates multiple estimates of state velocity vectors which are the inherent temporal gaze features associated with the category of visual stimuli. We also utilize such features for applications like classification of gaze data and video retrieval. It is to be noted that the main focus of this paper is to build an EC free viewing gaze dataset and show its diverse applications in a few studies. The adaptation of STEAS model to this dataset is done by optimizing a few SSM parameters and then used for classification and retrieval problems.
II. LITERATURE REVIEW A. GAZE DATASETS BY REMOTE AND WEARABLE EYE TRACKERS From literature, eye gaze datasets are grouped into two categories based on the type of the device being used: 1) USING REMOTE EYE TRACKER Greene et al. [13]  published eye-tracking data of 17 observers performing 4 different tasks (memorize, determine when the picture was taken, estimate if people know each other, estimate their wealth) on 20 different grayscale images for 60 sec viewing time. Greene et al. reported that replicating findings in  [14]  is harder than expected. Similarly, Koehler et al. [15]  published a dataset from 158 participants on 800 natural pictures. The effect of task differences on the ability of three models of saliency and the performance of humans on novel datasets have been investigated.
Behavior on the popular free viewing task was not best predicted by standard saliency models. Instead, they accurately predicted the explicit salient regions and eye movements made. Frame et al. published an eye tracking dataset  [16]  collected in a consistently well-lit environment using 2 display setup. The surveillance screeners are termed analysts here. Each display has full HD resolution and analysts were instructed to identify specific essential elements of information (EEI) from surveillance data. This helps in understanding problems that analysts face. For medical applications, Castner et al. [7]  published a dataset collected using a remote eye tracker. 57 dental students along with 30 experts were employed for dental radiograph interpretation. They were capable of distinguishing experts from novices with 93% accuracy while incorporating the image semantics.
2) USING WEARABLE DEVICES In a task specific data, Frame et al. [16]  published a dataset consisting of 17 sequences, performed by 14 different participants. They were asked to sit and prepare some food. They also equip participants with markers on hands which help in action identification. The beginning and ending time of the actions are manually annotated. The authors proposed a method to understand if the participant is baking, boiling, toasting, etc. Cherto et al. [8]  published EgoMon egocentric dataset consisting of 7 videos of 34 minutes each, on average. The videos were recorded in the city of Dublin (Ireland) in both indoor and outdoor environments. The authors have used these videos for evaluating the performance of a state-of-theart visual saliency prediction model. Lee et al. [18]  published the Univ.
of Texas at Austin Egocentric (UT Ego) dataset with 4 videos captured using head-mounted cameras by 4 participants. This is a first of its kind since there is no associated gaze data for these videos. The videos capture a variety of activities such as eating, shopping, attending a lecture, and driving. They proposed a method for generating a short summary of the day of camera wearer. The summary attends the most salient objects and people with which the human being interacts. Both UT Ego and our dataset are collected without imposing any viewing constraint on participants, whereas the former does not use eye-tracking device and hence does not provide eye gaze information unlike ours.
III. DATA ACQUISITION A. DEVICE COMPONENTS We have used Tobii Pro Glasses 2  [27]  for acquiring the gaze data of participants. Tobii Pro Glasses 2 provides information about the user's attention in real time while freely moving around in indoor or outdoor environments. Mainly, it consists of three parts: head unit (glasses), recording unit and controller software 1) HEAD UNIT Following are main parts of the head unit as shown in Fig. 3:  • HD scene camera: captures a full HD video of what is in front of the participant with 90 degree field of view, and • Eye tracking sensors: record eye gaze positions and gaze directions through IR sensors. They also record head and body orientations through accelerometer and gyroscopic information sensors. IR sensors focus IR radiation onto human eyes and the portion of light that is reflected from the retina is absorbed by sensors for computing parameters like gaze direction, pupil dilation, gaze position, etc.
C. DETAILS OF THE EXPERIMENT We have selected Nehru Museum of Science and Technology (NMST), IIT Kharagpur as our study location. The viewers can interact with the museum exhibits to understand them better. Hence, participants do simple tasks using their hands. We have considered two galleries: 1) Mathematics, and 2) Basic physics galleries containing 20 exhibits as a whole, for our work. A participant observes all the exhibits in a sequence without any break. We have 25 students aged between 18 and 30 from IIT Kharagpur as participants. All of them are certified having a normal or corrected to normal vision by an ophthalmologist at the local IIT Kharagpur hospital. We name it Egocentric Gaze dataset of Students collected at Nehru museum of IIT Kharagpur (EG-SNIK). EG-SNIK dataset will be made publicly available after acceptance.
1) GAZE DATA Table  1  contains various labels of the parameters provided by eye tracker in the gaze data (shown in Fig. 3 ): 2) VIDEO FRAMES Fig. 1  shows 20 exhibits of the museum with highlighted gaze positions (as dark spots) of a participant. These gaze positions are acquired over the period of time the user observes the exhibit. All the gaze positions corresponding to a particular exhibit are shown on a single frame for quicker reference. However, going by the operating frequency of the eye tracker (50 Hz) and the frame rate of the scene camera (25 frames per second), the number of gaze points available per video frame would be 2. Gaze position values obtained as normalized gaze coordinates are scaled up to HD resolution. We consider the gaze positions occurring for the whole duration of a participant watching an exhibit.
Each frame is connected to its neighbors with a temporal window of 11 frames by smoothing labesl using a markov random field (MRF). Depending on the similarity of color histograms, consecutive frames receiving different labels are penalized. The resulting smoothed labels define sub-shots: a sequence of consecutive labels with same label belong to the same subshot. This results in identified shots for all the 20 exhibits of an egocentric video. Our EG-SNIK dataset is challenging for the following reasons: 1) Two successive exhibits can be divided into two categories: 1) nonadjacent exhibits: exhibit-irrelevant frames existed between two events, and 2) adjacent exhibits: two exhibits appear seamlessly. 2) Exhibits numbered (  5 ) and (  6 ) (see Fig. 1 ) contain a knob to rotate which need similar head and hand movements.
2) COMPENSATION FOR HEAD MOVEMENTS For head-mounted eye tracker (Tobii pro glasses 2), the eye movements are not just because of eyes but also due to head movements (shifts and rotations)  [9]. The eye movement velocity or acceleration combined with that of the head movement results in a much higher value. This may mislead the correct identification of fixations and saccades. Hence, we compensate for the unwanted head movements for each of the video clips separated for 20 exhibits in the previous step. a: USING GYROSCOPE SENSOR DATA  [34]  Tobii pro glasses 2 gaze data stream also contains gyroscopic information along X, Y, and Z axes. The gyroscope records the head rotational velocities along the three axes. 19 ) requiring similar action, d) two exhibits appear seamlessly, e) exhibit irrelevant frames between two events, f) exhibits (  5 ) and (  6 ) are in a single video clip.
b: USING ACCELEROMETER SENSOR DATA  [35]  Tobii pro glasses 2 data stream also contains accelerometer information along X, Y, and Z axes. It records the head acceleration along the three axes. It is be noted that the accelerometers are affected by the linear acceleration and local gravitational field. They measure the difference between any linear acceleration in the accelerometer's reference frame and the earth's gravitational field vector  [35]. The accelerometer measures the rotated gravitational field vector which can be used to determine the Euler angles, pitch and roll orientation, as follows: φ(t) = tan -1 AccY (t) AccZ (t) (6) γ (t) = tan -1 -AccX (t) (AccY (t)) 2 + (AccZ (t)) 2 (7) where Acc(X (t)), Acc(Y (t)), and Acc(Z (t)) represent the accelerometer sensor data along X , Y , and Z axes, respectively.