dataesr/scientific-paragraphs-categorization huggingface dataset:
VIKING in CDN As mentioned in Section 4, the upper ontologies, namely, VIKING-F and VIKING-NF, both revolve around dimensions that encompass core abstract concepts for NFV, regardless of any application domain. To produce a dedicated domain ontology, all abstract concepts in VIKING-F and VIKING-NF should be refined into concrete concepts. In this work, we target CDN as the illustrative application domain. Hereafter, we first discuss VIKING's refinement to obtain the CDN ontology, named VIKING-CDN and then, describe how to populate VIKING-CDN with instances. In the following text, abstract concepts are in italic while concrete concepts and instances are in script with upper-and lower-case first letter, respectively. Table  6    Table  7  shows an excerpt of VIKING-CDN's population. There are 3 instance types.
Software integration to ETSI NFV MANO For dissemination and normalization purposes, Fig. 8  depicts the integration plan for the Mastermyr Chest tool built around VIKING into ETSI NFV standards. As mentioned in Section 1, the ETSI MANO framework supports VNF provisioning according to the procedures defined by ETSI and described in  [24]. MANO mainly consists of three key components namely, NFV Orchestrator (NFVO), VNF Manager (VNFM), and Virtualized Infrastructure Manager (VIM). Broadly speaking, NFVO is responsible of instantiating, deploying and executing VNFs according to the strategies established by the OSS/BSS. To each provisioned VNF, MANO associates a dedicated VNFM that manages the VNF lifecycle at runtime (e. g. starting, scaling, migrating, and terminating). VNFM closely interacts with VIM that maintains the necessary compute and storage appliances from the NFV Infrastructure (NFVI) for the proper functioning of the VNFs.
As is presently the case for regular VNFD storage in the MANO, the NFVO stores the VIKING descriptors in appropriate VNF catalogue (e. g. , document-based database) and the associated atefacts in the NFV repository (e. g. , VM image). As for the VNFs discovery, the ETSI standard procedure is kept. Specifically, the discovery tool first extracts a valid VNFD from a VIKING-compliant request and forwards it to the NFVO. The latter processes the request according to the standard VNFD discovery procedure supported by the MANO and sends back, to the discovery tool, a list of VNF candidates (action2). This list is refined thanks to our semantic matchmaker implemented within the discovery tool (eee Section 6. 1). The most relevant VNF with regard to the request is forwarded to the deployment tool.
Supported by the  IDEX "Chaire d'attractivité" program  of the  Université Fédérale Toulouse Midi-Pyréenées  under Grant  2014-345. No IETF SFC [41] Yes No No No T-NOVA [71] Yes Partially Yes No Cloud4NFV [56] Yes No No No Hoyos et al. [30] Yes No No  No Oliver  et al. [44] Yes No No No.
We propose a pipeline (SegMyO -Segment my object) to automatically extract segmented objects in images based on given labels and / or bounding boxes. When providing the expected label, our system looks for the closest label in the list of outputs, using a measure of semantic similarity. And when providing the bounding box, it looks for the output object with the best coverage, based on several geometric criteria. Associated with a semantic segmentation model trained on a similar dataset, or a good region proposal algorithm, this pipeline provides a simple solution to segment efficiently a dataset without requiring specific training, but also to the problem of weakly-supervised segmentation. This is particularly useful to segment public datasets available with weak object annotations (e. g.
, its bounding box), etc. Solutions relying on bounding box and labels are detailed in the following. Semantic segmentation appeared with the rise of (deep) machine learning, with CNN models like R-CNN or FCN. It relies on supervision with annotations at the pixel level, which is really tedious to obtain on large datasets. Thus, several datasets have been made available, like the well-known PASCAL VOC or COCO, but they are still limited to some specific applications. Beside that, "collecting bounding boxes around each object in the image is 15 times faster than labeling images at the pixel level"  (Lin et al. , 2014). In this context, weakly-supervised solutions appeared as a cheap alternative, using only weak annotations like bounding boxes or image captions for the training. And when they are available at the inference step, these annotations can be used to help the segmentation, what we call "segmentation with test clues".
This results in tasks with variable difficulty and specific solutions. Finally, it can be noticed that segmentation is closely related to region proposal, which consists in extracting coherent regions in the image. Benefiting from the development of huge panoptic datasets, it is now possible to find numerous pretrained segmentation models and to use them directly on new data with good performance, for many applications. The output can be a segmented image with different levels, or several candidate masks with possible overlap. In both cases, it might be interesting to automatically extract a particular object among all the output segments, given some clues on it, so as to integrate this step into a larger process. This is the problem we consider here. Weakly-supervised segmentation is a natural extension of this problem, since a simple solution consists in using segment proposal and selection to generate pixel-level supervision from weakly-annotated images.
In this article, we propose complementary criteria to extract a particular output among several proposals, based on its bounding box and / or its label. Concerning the bounding box, we use a mix of geometric criteria on the covering of the targeted bounding box by each proposal. And concerning the label, we use semantic information to find the closest to the researched one. We propose to use this selection in association with a segmentation model to generate the proposals, leading to a complete pipeline to segment datasets with weak object annotations. Moreover, it can be easily integrated as the first step of a weaklysupervised segmentation framework, so as to segment new data without annotation at inference step, using the weak annotations for the training. Thus, the main contributions of this paper are: • a proposition of geometric and semantic criteria to select an object in the image content, given its label and / or bounding box; • an integration of these criteria into a pipeline (SegMyO -Segment my object) aiming to segment any dataset from weak annotations (labels and / or bounding boxes); • an insight of how to use these criteria for weaklysupervised segmentation.
Segmentation transfer Modern segmentation models, whatever their supervision level, are trained on representative data, making them data/task-dependent, with variable generalization capacity. Using such a model for another dataset or another task is possible, but it requires some adaptations to make it efficient, by exploiting the knowledge available on the new test case. This general solution called "transfer learning" is known as "domain adaptation" when the task is modified, and "finetuning" when the model is just adapted to new classes (by modifying the last layer) and re-trained on new data. Fine-tuning is much used for object detection and recognition, but much less for semantic segmentation, which requires dense annotations. In this context,  (Hong et al. , 2016)  proposes to use transfer learning for weakly-supervised semantic segmentation, by using a encoder-decoder with a visual attention model to transfer knowledge from categories with strong annotations to unseen categories with weak annotations.
For instance, Box-Sup  (Dai et al. , 2015)  is based on Multiscale Combinatorial Grouping (MCG)  (Arbeláez et al. , 2014)  and evaluates several other solutions. (Khoreva et al. , 2017)  is based on MCG too, but it combines it with GrabCut, and allows to handle several instances of the same object. As for over-segmentation, it is used in  (Chen et al. , 2012)  solution for object / background segmentation, with an adaptive Mean-Shift algorithm. In our solution, we suggest to use an over-segmentation made of a full segmentation model trained on a similar dataset, using for instance the efficient Mask R-CNN  (He et al. , 2017). Other tracks were also explored, particularly for weakly-supervised segmentation. (Papandreou et al. , 2015)  constrains its CRF to consider the center area of the bounding box (a percentage of pixels within the box) as foreground, and the pixels outside the bounding box as background.
As for it,  (Hsu et al. , 2019)  uses a Multiple Instance Learning (MIL) framework, and integrates the tightness prior on the bounding box. More recently, BB-UNet  (Jurdi et al. , 2020) , which is based on U-Net, exploits shape priors by introducing a novel convolution layer, to segment medical images. Image segmentation with image-level labels: Many solutions exploiting image-level labels exist in the literature, particularly for weakly-supervised segmentation  (Kolesnikov and Lampert, 2016; Zhou et al. , 2018; Ahn et al. , 2019; Wang et al. , 2020). These solutions are usually based on class-activation maps (CAM)  (Zhou et al. , 2016)  and visual attention to generate the pixel-level ground-truth. The approach of  (Guillaumin et al. , 2014)  can be mentioned here too, since its goal is to segment a complete dataset containing weak annotations like us (ImageNet in their case), but with a totally different idea.
Workflow description We propose a solution to the task of object segmentation based on a bounding box and / or label test clue, on an image containing several objects and not only "stuff" background. It relies on a two-stage pipeline called SegMyO (Segment My Object): first a set of regions is extracted with a classical (instance) image segmentation solution, or with region proposal, and then the best output is selected for the given bounding box, and label if using semantic segmentation, according to the criteria described in Section 3. 2. Concerning the segmentation step, using a pretrained model supposes that the latter was trained on objects and labels similar to the target ones. Yet, there now exists good segmentation models pre-trained on huge and varied datasets, for many applications.
Semantic criterion When using semantic segmentation, each segment is assigned a label from the set of labels learned by the model. So knowing the label of the item we look for is useful, but it may not be part of this set. In fact, a major problem lies in the specificity and the low number of classes in most of the academic standard datasets published on the Internet. The segmentation models pre-trained on these datasets are thus limited to the segmentation of existing object classes in these datasets. For example, if the "cat" class is learned in the initial dataset, it will be difficult to provide as an input label "bobcat" in the target dataset. However the "cat" segmentation model might be used to segment a "bobcat" since these objects are visually close and semantically related. This is why we propose to use semantic label matching to extend the vocabulary to labels outside the set.
However we experimentally found that the proposed combination was already satisfactory (see Section 4). This is worth mentioning that all these criteria might be impacted if several instances of the object are present in the image and the segmentation is not able to separate them. So they should be used only with instance segmentation, or with datasets including only one instance of each object. It is also interesting to note that these criteria can be computed on the masks with "fuzzy" raw scores (in [0, 1]) rather than on binary masks, so as to take into account the score for each pixel in the computation. This is what we did for the criteria C2 and C3 in our experiments.
Data and experimental protocol We evaluate our pipeline on the PASCAL VOC 2012 segmentation dataset  (Everingham et al. , 2010) , using the validation set. It is made of 1 449 images, containing 3 427 objects from 20 classes in the segmentation masks. As a pre-segmentation model to generate the candidate regions, we used Mask R-CNN ResNet-50 FPN torchvision model 1   (He et al. , 2017) , which was trained on COCO 2017  (Lin et al. , 2014). This other dataset is made of 80 classes, including the 20 classes from PASCAL VOC, but sometimes with a different name, like "couch" in COCO which is "sofa" in PAS-CAL VOC, or "tv" vs. "tv monitor". The output of the pre-segmentation is a list of objects proposals made of a label, a segmentation score and a mask with values in [0, 1]. We only keep the proposals with a segmentation score above a threshold (set to 0.
25). We take this list as input of our selection process, and return the best proposal for each object, given its bounding box and its label, according to our criteria. We also test a region proposal solution with Multiscale Combinatorial Grouping (MCG)  (Arbeláez et al. , 2014)  to see if it can be efficient, by using the pre-computed proposals available online 2. We evaluate each criterion individually and the two combined scores to compare their performance. For the semantic similarity, we used WordNet taxonomy and wpath method  (Zhu and Iglesias, 2016) , with sematch python framework 3. As a segmentation evaluation metric, we compute the intersection-overunion (IoU) score for each object by using the instance segmentation annotations provided for the validation set, and after a binarization of the image (we used a threshold of 0.
3). We repeat this process for each object with such annotations, i. e. , for the 3 427 objects of this dataset, and report the mean over all objects as mIoU ob j in Table  1. So as to compare to other methods (weaklysupervised or fully-supervised), we also compute the mean IoU for each class of objects, after transforming our outputs to conform to image segmentation. To do so, we added a step of fusion of the individual selected segments, by inserting them in the output image from the largest to the smallest, so as to deal with overlapping segments. We report as mIoU the mean over all the classes, including the background class. We also compare to GrabCut  (Rother et al. , 2004)  as a stateof-the-art unsupervised solution for our task.
Results and discussion The results of our experiments on segmentation with test clues are reported in Table  1 , for several solutions and various selection criteria, while Table  2  provides comparisons with some recent weakly and fully supervised models from the literature. For each solution, we indicate either the level of the test clues available at the inference step in the first case, or the level of annotations used to train the model in the second case. It can be "pixels" when pixel-level annotations are used, "caption" when image-level labels are considered, and "b. box" or "b. box+labels" for bounding box level annotations. In our tests (Table  1 ), we distinguish between totally unsupervised solutions, i. e. , not using any data to elaborate the model, and solutions not using PAS-CAL VOC train data but another similar dataset, here COCO, in a transfer learning manner.
As a low baseline for the bounding box test clues, we use the solution to fill this one, totally or partially. The scores reported (solution "filled b. box") are obtained by filling the bounding box at 90%, which gave a better score than 80% or 100%. Concerning solutions based on our pipeline with a pre-segmentation and output selection, an upper bound baseline can also be obtained for the output selection by using the IoU as criterion instead of ours, which supposes to have access to pixel-level annotations. Thus, this baseline allows to get the best score for the considered pre-segmentation model (solution SegMyO mIoU ). These results show that totally unsupervised segmentation with test clues does not give satisfactory results, and that it cannot segment efficiently a dataset despite the bounding box information.
On the contrary, using a model pre-trained on another dataset allows to reach a good performance, ranking between weakly-supervised and fully-supervised models, without training on the considered dataset. Then it is possible to segment a dataset without a specific training, by benefiting from pre-trained models and test clues, with an appropriate use of these clues. Concerning the different proposed criteria (see Section 3. 2), they reach variable performance when taken individually, with a good behaviour of the criteria C3 and C1, and in a lower extent of the semantic criterion (which shows an important gap between the two computation modes of the mIoU). Combining several criteria allows to get closer to the upper bound given by the mIoU, for instance by combining the semantic criterion and criterion C3.
For the combined criteria score 1 and score 2 , we evaluated both with the bounding box only and with the bounding box + label. All results are quite similar, but the differences allow to conclude on the interest of each variation. Thus, we can infer that score 2 is not better than score 1 on average, contrary to expectations, showing also that the weights used in score 1 are quite adapted. More importantly, it can be noticed that the gain provided by the label is not very high, so that it is much better to annotate datasets with bounding boxes only than with labels only. Finally, a last solution is given by using score 1 and filling the bounding box (at 90%) when the computed score is below a threshold (set to 0. 5), which reaches the best performance, really close to the mIoU upper bound.
As an additional experiment, we used this pipeline to segment SpatialSense  (Yang et al. , 2019) , a dataset containing annotations of spatial relations between objects depicted by their bounding boxes. This dataset is made of 2 162 images with persons, animals, everyday-life objects, but also stuff classes (like "sky", "ground", "wall". ). As a pre-segmentation model, we used a HRNet model trained on ADE20K 4  , since this dataset contains similar classes, and HRNet is one of the best models for segmentation, although it doesn't achieve instance segmentation by default. Figure  3  presents some samples of the outputs obtained. Such results highlight the interest of our pipeline in a real case, since SpatialSense is provided without any segmentation annotation, while such segmented objects can be useful for various computer vision tasks, as the computation of relative position descriptors between objects  (Deléarde et al.
CONCLUSION We introduced a turnkey pipeline (SegMyO) to automatically extract segmented objects in images based on given labels and / or bounding boxes. Ir relies on simple criteria to select the best segment among several proposals for a given object, by exploiting the knowledge of its class or its bounding box, with semantic similarity for the label and several geometric measures for the bounding box. SegMyO allows to easily and automatically segment and select any object knowing its bounding box and / or its label, making it a useful solution to segment a dataset without requiring dense annotation or specific training. This is also a promising improvement for weakly-supervised segmentation frameworks. As a perspective, we consider to push our experiments further in order to assess more precisely the complementarity of our criteria and optimize their aggregation (using for example voting or learning strategies), on other datasets of the literature, where the semantic differences between the classes could be more important.
Introduction Although OSN users are getting more cautious about their privacy, they remain vulnerable to attribute inference attacks where the principle is to illegitimately gain private attributes (such as age, gender or political orientation) from publicly available information. In general, attribute inference attacks are either based on data directly generated by the target user (such as pseudos) or data obtained by exploring the user vicinity network. The need to protect the sensitivity of some attributes compels users to conceal information that may disclose them. For instance, the authors of  [6]  investigate Facebook users' privacy awareness and show that age has the lowest exposure rate. Less than 3% of the users (about 495k users) reveal their age, which shows the sensitivity of this attribute.
We consider these comments as part of the publication metadata. We also consider alt text as metadata which is freely available textual information describing the picture contents (faces, objects, and other themes) and which is generated by some OSN platforms (like Facebook) for blind people who use screen readers. In this paper, we describe how to infer attributes from publications metadata. We suspect that OSN users' publications metadata convey sensitive information, even though the users did not contribute to generating them directly. We will show that a target user's attribute can indeed be leaked from other users' reactions (e. g. , comments) to the target user's publications, even without analyzing the publication content. Note that this new type of attack is difficult to defeat as the user has no control over other users' reactions.
As a result, there is a variation in style and usage of the same term in the context of different attribute values. To discover these variations, we apply a semantic space model, known as Distributional Semantic Model (DSM). Classical word embeddings (such as word2vec  [20] ) uncover the semantic relations among terms by scanning through the whole corpus and detecting co-occurrences in a fixed context window. They build a global view of terms co-occurrence in the entire dataset. In Example 1, baby is used as a romantic term of endearment in the female-owned picture, whereas in the male-owned picture it is about making fun of and teasing the picture owner. Hence generating a vector for each word using the entire dataset can mix and combine many possible word contexts. We need to adapt the distributional semantic model so that an overlapped term with non-overlapped context will get different corresponding vector representations.
These various representations should be comparable since attribute prediction often relies on computing similarities between vector representations of terms and users. However, due to different random initialization processes on the sub-datasets (corresponding to distinct attribute values), the generated vectors are not comparable by the standard similarity measures, such as cosine similarity  [13]. To avoid this problem, we apply Random Indexing (RI)  [26] , which is an incremental and scalable method for constructing a vector space model. RI requires few computational resources for similarity computations and allows comparison of word spaces created over different attribute values  [2]. Accordingly, from the same term, we can generate vectors in different attribute values and compare them.
As a case study, we conduct an intensive analysis of three Facebook attributes: age, gender, and relationship status, as they are recognized as key privacy concerns in the Internet era. Some Facebook users choose to hide gender to camouflage themselves against stalking, sexual harassment, or reducing discrimination  [28]. Our attack receives promising results as we preserve the vector representation of each word for each attribute value. The result confirms that splitting the dataset can boost the attacker's performance. Paper Organization. In Section 2 we review the related works. Section 3 presents our Divide-and-Learn methodology to incorporate attribute values in word vector generation. We outline our attribute inference attack steps in Section 4. We present a case study in Section 5 to evaluate our attacks.