dataesr/scientific-paragraphs-categorization huggingface dataset:
Routing defines the path that packets must follow inside the NoC. Only the works of  [5, 9]  propose the use of adaptive routing to select disjoint paths inside the NoC in order to protect the system. However, this technique may lead to longer and probably higher risk sensitive paths. Consequently, it may degrade the performance and security of the system. In order to overcome this drawback, in this work we propose for the first time a Global Risk aware NoC (GRaNoC), a secure-enhanced NoC able to protect sensitive data by using low-risk paths. The risk is defined in this work as the probability that a malicious process spies, denials the communication or corrupts the data in a NoC hop. The risk is measured by the amount of firewall notifications due the violation of security rules. The risk is associated to each hop of the NoC.
IV. GLOBAL RISK-AWARE NOC ARCHITECTURE (GRANOC) The general overview of the GRaNoC architecture is shown in Fig. 1. It is composed of four main components: i) Secure network interfaces (SNI), which implement the communication protocol and security checking by means of firewalls; ii) Hops, which integrate the Data Routers (DR) and Control Routers (CR) to exchange data and control signals; and iii) Security manager (SM), which configures the firewalls and controls the recovery mechanism under a possible attack.
A. Secure network interfaces The SNI microarchitecture is shown in Fig. 2  The communication protocol is enforced by the pack/unpack and control blocks. They wrap data as packets (at the injection to the hops), and retrieve data from packets (at ejection from the hops). Packets are composed of seven fields: i) source, to identify IP that inject the data; ii) destination, to identify the target of the communication; iii) route, correspond to the value stored at the route table of the source SNI. It contains the path that the packet must follow when the source routing is activated; iv) risk, to store the accumulated risk of the path; v) operation, to identify the type of packet (control, data write, data read); vi) path, intended for authentication purposes by storing the marks of each router used by the packet; and vii) payload, that corresponds to the data generated by the IP source.
Data communication: The source IP A generates the data which is wrapped as a packet and checked by the firewall of the source SNI at step (1a). If the packet is compliant with the security rules, it will be injected and transmitted through the data NoC by means of the DRs at step  (2). Sensitive packets capture the hop risk of the sensitive path by upgrading the packet risk field. Packets can be commuted by distributed XY routing algorithm, when packets follow the original path, or source routing, when packets follow alternative low-risk paths. When the destination IP is reached at step (3), the packet is evaluated by the destination firewall in step (4a). It also retrieves the total risk of the sensitive path from the packet risk field and compares with the risk threshold. If the packet meets all the security requirements it is consumed at the destination.
Low-risk route: At the arrival of the search packet to the source SNI of the sensitive data (SNI 6 ), the path field of the packet is retrieved. It contains the route information of the packet. This route is stored into the route table of the SNI 6. Packets whose destination is the destination IP (IP 16 ) will include the new low-risk route at the route field of the packet and will be communicated by source routing algorithm. Remaining packets will be communicated by means of XY routing.
As the qutrit is operated in a dilution refrigerator at 20 mK, its thermal state is close to the ground state. To avoid residual thermal population, we begin each experimental trial by applying an initialisation readout tone to the cavity to project and post-select the desired ground state, rejecting approximately 10% of all data points. A further delay of 565 ns allows the cavity to ring down before the measurement sequence begins. The whole sequence is repeated 2 21 times, triggered every 100 µs. For each observable A i , the same procedure (set of pulses) was used to implement the measurement independent of measurement context, with the cavity transmission signal detected, integrated and discriminated to assign a dichotomic outcome ±1. The outcomes were recorded to the hard drive and were later used to calculate expectation values A i and cor- relations A i A j.
To measure transmission, a signal from a microwave generator (RF) was applied to the input port of the cavity. Microwaves transmitted through the cavity were amplified by a Josephson parametric amplifier (JPA), high-electron-mobility transistor (HEMT) amplifier at 4 K and a chain of room-temperature (RT) amplifiers. The sample at 20 mK was isolated from the higher-temperature fridge stages by three circulators (C) in series. The amplified transmission signal was down-converted to an intermediate frequency of 25 MHz in an IQ mixer driven by a dedicated LO, and digitized by an analogue-to-digital converter (ADC) for data analysis.
The weights update In the training phase, the training data is fed into inputs, then the output of a neuron is calculated as described in Eq. (  1 ). This procedure is repeated for all neurons at the input layer, then at the hidden layer(s), and finally at the output layer. Afterwards, the error values are calculated based on the desired output value and the actual output value. This error is used to update the weights of all the connections in the ANN. This update is done by a back propagation of the error value, meaning that the weights connecting the output layer neurons to the last hidden layer neurons are updated in the first place. When all the weights are updated, the ANN is ready for the next epoch of the training phase. The maximum number of epochs is predefined depending on the specific problem and the available dataset.
The commonly used error function is the mean squares error (MSE) that is defined by MSE = 1 2 ( M m=1 k i=1 (desired_output m i -current_output m i )) ( 2 ) where M is the number of training datasets. When the calculated value of the MSE is less or equal to the predefined Fig. 4  The structure of an artificial neuron desired MSE (MSE des ), the training is stopped and the ANN is considered as sufficiently trained. Furthermore, the stop point may be controlled by other customized metrics.
Overview on the proposed solution As shown in Fig. 7 , each device has to send a registration request to the controller. Upon receipt of this request, the controller creates a registration context specific to the requesting device. The controller affirms or denies the registration with an appropriate registration response. A newly associated device can have the latest optimized parameters via this response. At a predefined moment, the controller sends a collection start command to all the registered devices. The collection procedure is described in details in the next section. After collecting all the datasets, the controller performs an online training for the previously created neural network. Then, the trained neural network is used to adapt the parameters of the devices. The optimization procedure is described later in this paper.
The different procedures of the proposed algorithm After examining every procedure apart from others, the overall algorithm is shown in Fig. 8. The optimization round consists of returning to the start step after running through the different steps depicted in the flowchart. An optimization round n begins by an initialization phase where the ANN is created and configured (Table  1 ). Then, the current version of the training dataset is fetched. As it will be described in detail later on, initially, the offline dataset is divided randomly into two parts, one is a part of the training dataset and the other constitutes the testing dataset. The fetched dataset is the offline training part appended to the previously collected dataset entries Fig. 10  Testing procedure details during past optimization rounds (< n).
Then, a new collection procedure starts and the resulting dataset entry is appended to the fetched training dataset. At this point, we are ready to proceed to the training phase described in Section 1. 5. 3. After that, the ANN is tested using the testing dataset as outlined in Section 1. 5. 4. If the resulting testing MSE increases' compared to that of the previous optimization round (n -1), the process quits the training phase and enters the optimization procedure (see Section 1. 5. 5). At the end of the optimization procedure, the process returns to the start point and a new optimization round (n + 1) starts.
Training procedure In this section, we describe the training procedure of the ANN. The latter is based on two types of datasets, the first is collected offline (when the real network is not in operational mode) and the second is the result of an online collection (while the normal system operation). The offline dataset is divided into two separate datasets. The first part is used as the initial part of the training dataset, while the second part is used to test the ANN during the training process. The testing procedure is an important player in determining the end of the training process and the beginning of the optimization process. The online dataset is the complementary part of the training data set. After every optimization round, the collected dataset entry is appended to the latest training dataset.
Accordingly, the ANN is trained with an incremental training dataset, increasing in size after each optimization round. This assures an adaptive behavior of the proposed solution. The detailed training procedure is depicted in Fig. 9. To increase the robustness of the training phase, we integrate two test levels to verify if the network is successfully trained or not. To implement our approach, we consider two different criteria. One of them is the well-known desired mean square error (MSE des ). The other criteria is the number of output errors exceeding certain absolute value (the desired fail limit FL des ) that is equivalent to the difference between the output neuron value and the related value in the dataset. We define the desired fail number ratio FNr des as the ratio of output errors exceeding FL des to the total number of output values in the training dataset (number of ANN's outputs K times the number of dataset entries DSe nb ).
This task consists in identifying users which are influential in real-life, based on their Twitter account and related data. We show that most features deemed efficient to predict online influence, such as the numbers of retweets and followers, are not relevant to this problem. However, we propose several content-based approaches to label Twitter users as Influencers or not. We also rank them according to a predicted influence level. Our proposals are evaluated over the CLEF RepLab 2014 dataset, and outmatch state-of-the-art methods.
Of course, performing an exhaustive survey seems hardly possible, due to the number of concerned works. We however consider a wide range of studies and adopt a high level approach, focusing on the meaning of the features while also describing the different forms they can take. We organize them in a new, trans-domain typology. As an illustration of how our review can be used, we then apply a selection of these features to a real-world problem: the detection of offline influential users. In other words, we aim to solve the problem consisting in detecting people which are influential in real-life, based on their Twitter profile and activity. To answer this question, we conduct experiments on the CLEF RepLab 2014 dataset, which was designed specifically for this task. Indeed, it contains Twitter data including Twitter profiles annotated in terms of offline influence.
Organization. The rest of this paper is organized as follows. In the next section (Section 2), we review the features related to the classification or characterization of Twitter users, with an emphasis on their meaning in this context. We also propose a typology for these features, in an effort to highlights how they are connected. We then focus on Twitter-based offline influence detection in Section 3. We describe the problem at hand, the RepLab data we used in our experiments, and the methods we propose to solve this problem. In Section 4, we present the results we obtained and discuss them. Finally, we highlight the main aspects of our work in the conclusion, and give some perspectives regarding how it can be extended.
Feature 26 is symmetrical to Feature 25: it is the number of times the user is mentioned by others. It can be averaged (or any other statistic) for a given period of time (e. g. number of mentions by day). It can also be divided by the number of tweets published by the user, to get an average number of answers by user's tweet (mentions generally express the will to answer another user's tweet). This feature is interesting, but computationally hard to process, because for a given user, it basically requires parsing all tweets published by the other users. So, it is treatable only for small datasets.
External Data This category contains features corresponding to data not retrieved directly from Twitter. Feature 39 is simply the number of results returned by some Web search engine, which point at the user's Webpage. The next two features are scores computed by private companies independent from Twitter, and aim at measuring (in one way or another) the influence of users. Of course, they differ in the definition of the notion of influence they rely upon. Feature 40 is the Klout score, that takes into account both Tweeter-related and external data gathered from other social networking services and various search engines  [60]. The precise list of the features used to compute the Klout score was not published, though. The algorithm behind the Kred Influence Measurement  [40]  is open source (Feature 41).
General Remarks We conclude our review with three remarks concerning all features. First, an important fact regarding the selection of features is their availability. Depending on the context of the considered study, all the features we listed cannot necessarily be used, for several reasons. First, the dataset given for the study might be incomplete, relatively to the features one wants to process. For instance, if one has access to a collection of Tweets, he still has to retrieve the subscription information to be able to use Features from category Local Connections. But the Twitter API queries limitations might prevent him to access these data, or the concerned accounts may no longer exist, or the users may have changed their privacy settings. Some users also do not fill all the available fields, making it hard to use certain features from category User Profile, unless the tool used to analyze the data is able to handle missing values.
There are also time-related constraints: the data collected in practice only correspond to those that can be obtained in a reasonable amount of time. Moreover, even if one manages to retrieve all the necessary data, the computation of certain features can be very demanding if the dataset is too large, as we explained for Feature 26. Certain authors focus on the evolution of a given feature, by opposition to using a single value to summarize it. For instance, in  [46] , Lee et al. measure the change rate of the number of followees  (Feature 17). This can significantly complicate the data retrieval task, since this requires measuring the feature at different moments. In our list, we omitted features one cannot compute in a normal context. For instance, when treating influence, Ramirez-de-la-Rosa et al.
use a feature corresponding to the type of job a user holds  [62]. However, this feature comes from the RepLab dataset (see Section 3. 2) and was manually defined by a specialized agency. In practice, it is hardly possible to replicate exactly the same process on new data. Our second remark concerns the way features are computed. We tried to stay general, and focus on what each feature represents conceptually. However, in practice, there are most of the times a number of ways to process a feature, which differ in various aspects. We indicated the main variants in the Descriptors column of Table  1. However, we should emphasize that this aspect is much more important for content-related features, especially those from categories Lexical Aspects and Stylistic Traits. Indeed, those features coming from the NLP and IR fields are very sensitive to the way the content is pre-processed.
RepLab Challenge The RepLab Challenge 2014 dataset  [3]  was designed for an influence ranking challenge organized in the context of the Conference and Labs of the Evaluation Forum 1 (CLEF). Based on the online profiles and activity of a collection of Twitter users, the goal of this challenge was to rank these users in terms of offline (i. e. real-world) influence. This is exactly the task we want to perform here, which makes this dataset particularly relevant to us. We therefore use these data for our own experiments. In this subsection, we first describe the context of the challenge and the data. Then, we explain how the performance was evaluated, and we discuss the results obtained during the challenge, as a reference for later comparisons. Finally, we present a classification variant of the problem, which we will tackle in addition to the ranking task.
Data and task The main goal of the RepLab challenge is to detect offline influence using online Twitter data. The Re-pLab dataset contains users manually labeled by specialists from Llorente & Cuenca 2 , a leading Spanish e-Reputation firm. These users were annotated according to their perceived real-world influence, and not by considering specifically their Twitter account,although annotators only considered users with at least 1, 000 followers. The annotation is binary: a user is either an Influencer or a Not-Influencer. The dataset contains a training set of 2500 users, including 796 Influencers, and a testing set of 5900 users, including 1563 Influencers. It also includes the 600 last tweet IDs of each user, at the crawling and annotation time. This represents a total of 4, 379, 621 tweets, i.
e. around 750 megabytes of data. These tweets can be written either in English or in Spanish. The dataset is publicly available 3. RepLab finally provides a bounded and well designed framework to efficiency evaluate features and automatic influence detection systems. Given the low number of real Influencers, the Re-pLab organizers modeled the issue as a search problem restrained to the Automotive and Banking domains. In other words, the dataset was split in two, depending on the main activity domain of the considered users. The domains are mutually exclusive, i. e. one user belongs to exactly one domain. The objective was to rank the users in both domains in the decreasing order of influence. Both domains are balanced, with 2353 (testing, The organizers proposed a baseline consisting in ranking the users by descending number of followers.
Evaluation The RepLab framework  [3]  uses the traditional Mean Average Precision (MAP) to evaluate the estimated rankings. The MAP allows comparing an ordered vector (output of a submitted method) to a binary reference (manually annotated data). In the case of RepLab, it was computed independently from the language, and separately for each domain. For a given domain, the Mean Average Precision M AP is computed as follows  [12] : M AP = 1 n N i=1 P (i)q(i) (1) where N is the total number of users, n the number of Influencers correctly found (i. e. true positives), P (i) the precision at rank i (i. e. when considering the first i users detected) and q(i) is 1 if the i th user is influential, and 0 otherwise. RepLab participants were compared according to the Average MAP processed over both Automotive and Banking domains.
The performance is considered for each domain (Banking and Automotive), as well as averaged over both domains. It gives an overview of the system ability to recover information from each class. Ramirez et al. do not use any baseline to assess their results. Nevertheless, the imbalance between the influencer (31%) and non-influencer (69%) in the dataset leads to a strong non-informative baseline which simply consists in putting all users in the majority class (noninfluencers). This baseline, called MF-Baseline (most frequent class baseline) achieves a 0. 50 Macro Averaged F -Score. For this classification task, Ramirez et al. reached a MAP of 0. 696 and 0. 693 for Automotive and Banking domains, respectively, and a 0. 694 Macro Averaged Fscore. On the same task, our team (Cossu et al. [18] ) proposed a classification method based on tweet content, but obtained relatively low results (0.
Experimental Setup In order to tackle the offline influence problem, we adopted an exploratory approach: we do not know a priori which features from Table  1  are relevant for the considered problem. So, we selected as many of them as possible. However, we could not take advantage of all of them, or use all the descriptors available for a given feature, be it for computational or time issues, because the necessary data were not available, or simply for practical reasons. In this subsection, we list the selected features, which include both scalars and vectors. We also describe how we processed them, in function of their nature. The scripts 4  corresponding to this processing are publicly available online, as well as the resulting outputs 5.
For Feature 19 (neighbors ids), we considered the standard deviation of the ids of the 5000 most recent followers, and did the same for the followees. The topology of the follower-followee network has proven to be an important feature for the prediction of online influence, so it is worth a try when dealing with offline influence. We investigated Feature 33 (tweet length) considering average values expressed in terms of both number of characters and number of words. We discarded min and max values, because in our dataset they tend be the same (0 and 140) for all users. We think tweet length is likely to be relevant to identify autorities, which we suppose have more to say than noninfluential people. For Feature 25 (mentions), we used the number of mentions by tweet, number of unique mentions by tweet, proportion of tweets that contain mentions, and total number of distinct usernames mentioned.
Regarding Favorites (Features 23 and 23), we hypothesized that tweets from influential users are often marked as favorites by other users while influencers do not use this functionality. For Feature 36 (hashtags), we used the number of unique hashtags, the number of hashtags by tweet, the number of unique hashtags by tweet, and the proportion of tweets that contain hashtags. We selected these features because previous results such as  [2]  indicate that user activity on trending topics is a great indicator of influence. Similarly, for Feature 37 (URLs), we distinguished the numbers of URLs by tweet, of unique URLs by tweet, and the proportion of tweets that contain URLs. Note that for the last 3 features, the uniqueness was determined over all the user's tweets (in the limit of the RepLab dataset), and not tweet-by-tweet.
Our assumption here was that influential users tend to share links towards websites related with their profession or the activity domain, and possibly aiming at specific types of medias. However, for technical reasons, it was not possible to expend short URLs or to follow links, so we could not completely put this idea to the test. We used non-linear classifiers under the form of kernelized SVMs (RBF, Polynomial and Sigmoid kernels) and logistic regression. We trained them using three distinct approaches: first with each scalar feature alone, second with all combinations of scalar features within each category defined by us (as described in Table  1 , and third with all the scalar features at once. The two domains from the dataset (Banking and Automotive) were considered together and separately.