dataesr/scientific-paragraphs-categorization huggingface dataset:
The authors evaluated their framework by training AlexNet on 10 Xilinx VC709 Connectivity Kits. The results indicated that compared with other frameworks  [21] , the throughput obtained by this framework was increased by a factor of 5; compared with Titan X, the energy efficiency of the framework was up to 7. 6 times higher. Moreover, the framework exhibits good scalability, as it can scale up to 60 FPGAs to accelerate DNNs. However, such multiple-FPGA platforms adopting pipeline models gain high throughput while sacrificing latency. Jiang et al. [92]  developed a general framework called Super-LIP to support concurrent processing for both single-and multi-layer deployment on FPGAs. To achieve communication between two FPGAs, the authors employ a novel methodology in Super-LIP to achieve linear speedup by balancing computation workloads and distributing the shared data across FPGAs to avoid traffic heaviness on the FPGA memory bus.
The authors evaluated ViTAL by implementing LeNet, AlexNet, and VGG-16 on a Xilinx UltraScale+ FPGA. The experimental results indicated that ViTAL achieved good DNN mapping quality with a short compilation time (1. 6% of the total). Furthermore, ViTAL can dynamically relocate the DNNs to different positions in the FPGA. The experimental results also indicated that with FPGA virtualization methods, ViTAL significantly shortened the response time (by 82%) in the cloud environment. Fowers et al. [94]  proposed a full-system architecture with virtualization at a multi-node level to serve DNN inferences in a cloud environment. The critical feature of the architecture is the dedicated neural processing units (NPUs), which implement an SIMD ISA containing a matrix-vector multiplier. This DNN-specific ISA offers a highlevel abstraction between the underlying FPGA infrastructure and DNN software development, thereby simplifying FPGA programming for software developers.
Isolation With the increasing efforts to provide a cloud environment for multiple tenants to deploy DNNs on the shared FPGAs, resources and performance isolation have become a concern in the cloud. DNN accelerators on the FPGA usually run under full hardware access and may share resources. Therefore, malicious code can attack the entire platform for other tenancies  [112, 113]. Additionally, dataset collection can be time-consuming and expensive-particularly in industrial cases where datasets are of significant commercial value. Providing strict data and resource isolation for multiple tenants can prevent unauthorized access to the dataset and avoid data leakage  [114, 115]. Additionally, a DNN application may affect the performance of other DNN applications during concurrent execution  [112, 116] , which causes unreliable performance.
Industrial solution To keep pace with the development of DNN accelerator design, novel platforms have been used in industry to enhance the hardware computing power. In 2019, Xilinx proposed a new SoC family called Versal, which is based on an adaptive compute acceleration platform, for accelerating applications such as DNNs. Versal tightly integrates software-programmable accelerators through the NoC structure, making accelerators scalable with flexible connections and achieving a high level of software abstraction for the rapid development of accelerators. Xilinx also proposed a novel framework called Vitis AI  [117]. The framework can be interfaced with Caffe and TensorFlow and provides a unified solution, e. g. , quantization, optimization, and pruning. Moreover, it allows the deployment of DNNs based on the ISA and can compile the latest DNNs into deep-learning processor unit instruction codes.
) implements an entire DNN on the side of the Programmable logic (PL) of the FPGA from the first convolutional layer to the final fully connected layer. On the PL side, it deploys a chain of sequential DNN intellectual property (IP) to process the dataset in the pipeline mode. The intermediate results (DNN feature maps) are stored on the chip on the PL side. This architecture enables an efficient data stream without frequent data exchange with external memory, significantly reducing the latency and obtaining throughput at a high frequency. A specific DNN model using a streaming architecture must be defined before generating the bitstream. Whenever the DNN model changes, architecture re-compilation and bitstream regeneration are inevitable. According to the selected DNN algorithm, the re-compilation of this architecture may be time-and resource-consuming.
This paper reports a preliminary investigation on the ability of Machine Learning algorithms to relate the morphology of third body particles to the rheology of the contact interface that created them. A testing campaign is performed on a pin-on-disc tribometer, followed by a comprehensive observation of the worn surfaces. Several Machine Learning algorithms are then used to establish and quantify the logical relations between the rheological and a morphological databases built from this campaign. Success rates and thorough analysis of their predictions are used to validate the general approach and to propose possible improvements. It appears that Machine Learning presents an interesting potential in quantitative tribological analysis if the morphological and rheological databases are properly enriched.
More recently, a coupled approach using both conventional ANN and Deep Learning was applied to the same problem with promising results  [33]. For all these studies, wear particles were first extracted from the contact and isolated by techniques such as ferrography, in order to acquire the microscopic images and to build relevant databases. This is a limitation, because (i) this operation involves a large amount of human work and expertise and (ii) the displacement of the particles may have caused the modification of their morphology due to their fragility. For these reasons, the number of particles analysed was typically pretty low. However, this research proved the efficiency of using wear debris morphology to understand the wear that takes place in the contact. As a continuation of these pioneering works, a preliminary methodological study is proposed in this paper, which aims to further explore the experimental gap between two classes of experimental results regarding third body: Rheology and Morphology.
Morphology, on the other hand, will describe any information regarding the shape and the aspect of the third body as observed after opening the contact and observing it at the micrometric scale. Following the classification proposed in the previous paragraph, rheological data is "global and dynamic" while morphological data is "local and static". To build this link, the following methodology is employed: a tribological rig (described in Section II) is used to run an experimental campaign while systematically varying several key parameters, and a structured rheological database is built from these results (Section III). The worn surfaces of the first bodies are then systematically submitted to microscopic observation in order to extract morphological properties of the created third bodies, and to build a morphological database (Section IV).
III. RHEOLOGICAL DATABASE In Fig. 2 , illustrative experimental results obtained during test 4 are gathered. Fig. 2A  shows the friction signal as well as discrete values of the average friction for each disc revolution. Three main stages can be inferred from this curve. For a sliding distance of 0-1 m, a rapid rise of the friction takes place, followed by a slower friction increase in a second stage (From 1 m to 10 m of sliding), accompanied by intense friction fluctuations and sharp peaks of tangential resistance to sliding. In a third stage (from 10 m to 18 m of sliding distance), friction appears to reach a plateau, with a continuously decreasing variability, and it can be considered that the contact has reached a steady state at the end of the test. These three stages are observed in the 9 tests reported here, with minor differences.
However, provided that the prow is located outside of the main contact area (Fig. 2F ), it does not carry any significant load and is not responsible for any significant part of the friction force. It will thus be disregarded in the morphological database reported in the next section. From these raw data, several descriptors are extracted in order to build the rheological database for further use in a Machine Learning context: Average friction coefficient, Standard deviation of friction coefficient, size of the contact area on the pin, width of the wear track, etc. However, for the sake of simplicity, only results related to the first one will be reported here. This choice is made because similar qualitative results are obtained for all descriptors, as will be elaborated later in the present paper.
IV. MORPHOLOGICAL DATABASE Conventional analysis of the third body particles has traditionally been performed based on the judgment of tribology experts. However, even though such interpretations are precise and accurate, it is not an effective method when having a large quantitative data set. Besides, the knowledge extracted from such a non-quantitative post-test analysis is difficult to transmit to non-experts, and consequently to use in other fields of the scientific or industrial community. Therefore it was decided to develop an image-processing algorithm that describes the third body particles created during a tribological test, using their microscopic images. This is performed by calculating a number of selected morphological descriptors. Using this method, the issue subjective to expert knowledge is expected to be reduced and the repeatability of the tribological analysis is expected to be improved.
The morphology of an object refers to a description of its appearance, which may include its shape and texture. This study only focuses on shapes but does not take into consideration the texture aspects, although they represent a promising idea for future work. In order to be able to calculate the morphological descriptors of each particle, a semi-automatic image-processing tool was developed in a Matlab environment. Such an algorithm has two intermediate steps that need to pass by. First, it must differentiate between the different particles present in the image and its background that is, in our case, either one of the surfaces of the pin or the disk or some other much larger particles. This separation is grey-level based. In all the microscopic images acquired during the study, the background is darker than the particles on it as can be seen in Fig.
This procedure is already programmed in Matlab. However, it is left to the user to prepare the proper matrix for its input. The raw SEM image needs to be treated before starting the watershed processing. The input image for the watershed function needs to be a grey-level image from where it can detect the appropriate minima for each particle. Fig. 3  illustrates the different transformations that the raw image (A. ) goes through and the result of the watershed algorithm. After manual section of a given region of interest (B. ), a filter is applied to clean the background noise and remove all the elements that are connected to the borders of the images (C. ). Then, the image's histogram is adjusted to increase its contrast (D. ). This filter saturates the bottom 1% and the top 1% of all the pixels values.
Principles Establishing a logical link between the rheology of the third body and its morphology would enable to enrich and strengthen existing tribological methods, and is therefore highly desirable. However, the size and the complexity of the databases make it almost impossible to achieve this goal manually, and an automated methodology is necessary. This is where machine learning comes into play. Several studies have used machine learning in a tribological framework for various purposes, like for example to optimize metal composites for a better tribological behaviour of their There are many methods to teach machines how to learn from their experiences. One of the main differences between those techniques is the feedback. The type of the feedback determines the learning mode. In the supervised learning, the feedback matches the action the agent chooses to apply depending on the set of percepts it receives.
Regression problem In the previous sub-section, the output of the machine-learning algorithms is the number of the test from where the particle is generated. In this sub-section, as a second step, machine learning capability to predict the rheological measurements from the morphological descriptors of the particles is investigated. This changes the nature of the problem because it is no longer a classification objective but a regression one. More specifically, the algorithms would not predict a class to which the grains may belong to, but a numerical quantity. The database contains all the particles from the tests. As previously, it is divided into two databases: the training (75 %) and the test database (25 %). The input part of the algorithm is the morphological database, and the output is the rheological one (restricted in this preliminary study to the value of the average friction provided in Table  2 ).
The chosen approach is polynomial regression, with the following hypothesis function: ℎ $ % & = 0 0 -? ! -; • - ? @ ?; (3) The cost function used in that case has the following expression:. % & = - 1 / 0 A2 3 -4 56ℎ $ % 3 &7B = : 3; + < • 1 • C 0 0 -? = ! -; @ ?; (4) In this expression, 2 3 is the coefficient of friction measured in the test of the particle and the vector 3 contains its morphological descriptors, C is the polynomial order of the method, is the number of morphological descriptors, and / is the number of particles in the database. The • C weights are determined by a Gradient Descent Algorithm as in the previous sub-section. To estimate the success of the prediction, the success quantifier is the average absolute error on the friction coefficient (i. e. the average on all the particles of the absolute value of the difference between the predicted and actual friction coefficients).
The first reason is that the tests are too numerous (9, instead of 3 to 5 reported in  [30] [31] [32] [33] ) and too similar (only minor experimental parameters are modified) to produce particles that could be easily discriminated based on simple descriptors (this can be observed in Fig. 4 , where the error bars are larger than the inter-class variability). The second reason is that the geometric information that would be necessary to perform this discrimination is missing in the chosen descriptors. Considering these two reasons, it appears that the choices made in the construction of the database should be proportioned to the difficulty of the task assigned to the trained algorithm. For example, the chosen descriptors might have been sufficient for the dissimilar particles of study [32], but were not for those of the present study.
The strategy to build a sufficiently rich database for a given task remains to be investigated. However, despite being less good than some other reported results, the predictions obtained in the present study remain way above a purely random assessment (it would be one successful prediction over nine). As such, they are equally disappointing and promising. They demonstrate that, in some cases, the morphology of the grains possess a signature that is closely linked to the tribological conditions where they were formed. As such, this work brings a first quantitative validation to the common practice of tribological experts, who have been used for decade to interpret tribological scenarios based on the observation of the particles on worn surfaces. It is striking, for example, to consider the prediction results of test 6.
As shown in Fig. 8 , the morphological signature of this test is clearly the Regularity descriptor. This is confirmed by Table  3 , where the absence of this descriptor leads to a drop of the success rate. It is thus very likely that the lowest success rate of some other tests is related to the fact that some of the visual features characterizing their particles was absent from the morphological database. The choice of considering nine different classes of particles (instead of three to five in similar studies) coming from rather similar tests (same wear type, only varying minor operational parameters) also certainly led to the limited success rates. It can thus be foreseen that future enrichment of the database could only improve the prediction capability of the algorithms. This extended database could include more descriptors of the particles contours, but also of their texture and chemistry, and take advantage of different observation tools in a "multi-mode" framework (optical microscopy, topography measurements, etc.
). The idea that contour and texture descriptors are equally necessary to distinguish different classes of particles (contour descriptors for some classes, texture descriptors for some others) was already proposed in several studies  [26, 31] , and is a promising research avenue. A quantitative enrichment of the database, including a much larger number of particles, could also improve the training results. As demonstrated in [33] however, Deep Learning might be the only solution for the most complicated cases. The outcome of the regression problem, in contrast, seems to indicate that very little should be expected in terms of prediction of the rheology of a tribosystem based on the morphology of its generated particles, if the methodology used in this work is reproduced as such. There is, however, a simple explanation for this disappointing result.
The results obtained in this work did not allow to discriminate between different Machine Learning approaches. In the classification problem, Logistic Regression and Neural Networks led to similar results, albeit with some minor differences (Fig. 9 ), and a parametric study showed that the influence of the methods numerical parameters was not dramatic in the investigated range (Fig. 8 ). This is true as well for the regression approach, where the regularization parameter and the polynomial order did not appear to have sharp effects on the average absolute error (Fig. 10 ). At this point, it seems clear that the priority is the enrichment of the database (by adding morphological descriptor and making the rheological descriptors more local, while removing any sampling bias), more than a search for the most suited Machine Learning approach.
Based only on morphological features extracted from pictures collected using ferrography, they first applied Neural Networks to classify various kinds of particles in five classes. This part of their work was similar to the present work in many aspects, and led to a satisfactory level of prediction. However, detailed analyses showed that the Neural Network was very predictive for three different classes (success rate larger than 80%), but was unsuccessful for two other classes (36% and 45% of success for Severe Sliding and Fatigue Wear, respectively). This is in good agreement with the results reported here, since it indicates that a given algorithm can give very different results with the same training, depending on the database: some cases are simply more difficult to discriminate based on the available data.
mm) ω(rpm) V(mm/s) Gas L(m) T(°C) H(%) Set 1 automatically build a tribological scenario based solely on post-mortem views of the third body layer (although this can be considered as a long-term purpose of this preliminary work). As such, the nine different tests are chosen in order to generate a sufficiently large database of rheological and morphological information, but no conclusion is expected at that stage on the effect of the varied parameters (slip velocity, gaseous environment, slip distance) on the tribological scenario. Table 1 summarizes the different tests that were carried out. Set R(Influence Test F(N) 1 10 20 2 10 15 6 6 12. 57 9. 42 Air Air 25 24 22 20 51 49 of V 3 10 10 6 6. 28 Air 25 23 50 Set 2 4 10 20 5 10. 47 Air 18 22 44 Influence 5 10 20 5 10. 47 Argon 15 20 50 of gas 6 10 20 5 10.
friction curve (taken after a sliding distance of 10 m) is considered for all tests. Results are reported in Table2. They show a large variability in the quantitative results but do not seem to follow any clear trend, possibly revealing an important experimental variability. Test 1 2 3 4 5 6 7 8 9 F 0. 44 0. 54 0. 49 0. 26 0. 43 0. 35 0. 29 0. 38 0. 20 Table 2. Rheological database (restricted to one descriptor in this report); F stands for the average friction coefficient measured during steady state sliding (Stage 3).
Finally, these VNF descriptions and publication models are not comprehensive. Indeed, they do include details on VNF deployment but fail to cover their associated functional and non-functional specifications. Functional characteristics of VNFs refer to the business functionality that a given VNF supports/implements (e. g. video compression, data mixer). By definition, non-functional characteristics refer to what the VNF needs/requires for proper functioning. In other words, these characteristics aim to describe the optimal use state and/or the requirements of the VNF in terms of security, reliability, performance, maintainability and so on. All these limitations are due to several reasons. In NFV providers and technologies' broad landscape, a lack of a shared understanding of VNF descriptions can undoubtedly be observed due to technologies' and providers' heterogeneity.
Standardization bodies and research projects Besides the previously discussed ETSI VNFD model, one of the most known and used approaches is Topology and Orchestration Specification for Cloud Applications TOSCAbased, namely TOSCA-NFV  [60]. TOSCA is a data model standard managed by the OASIS industry group. This data model is used to describe services' operations and requirements  [11]. It also explains how services can be deployed and managed at runtime through management plans (workflows). TOSCA-NFV is the concrete implementation of the model applied to NFV for VNFs provisioning and management. It proposes a model to describe topologies, dependencies, and relationships between virtual applications and simplify these services' complexities rather than define VNFs capabilities and requirements.
Additional fields enable business interaction among actors that communicate through the T-NOVA Marketplace (e. g. , SLA specification, pricing), besides deployment details needed to deploy the network services. The VNF/NS discovery process is conducted through the brokerage module  [71] , which permits consumers to search for VNFs/NSs while specifying their specific requirement in terms of network SLA. Cloud4NFV  [56]  is a virtualized platform for VNFs provisioning. It aims to deliver NF-as-a-service to end customers. Cloud4NFV is ETSI-compliant with significant contributions on the modeling and orchestration aspects. On one side, Cloud4NFV processes a front-end database that stores collections of VNFs along with a high-level description (e. g. , ID, name, description, location). On the other side, it handles a back-end database that stores specific VNF information necessary for the VNF deployment and configuration.
An alignment contains a set of correspondences between entities and properties of such ontologies. Correspondences refer to semantic connections between concepts used to describe microservices data. The most important difference from the traditional ontology alignment is that equivalence statements can only be obtained at runtime. Indeed, entities are created during interactions between microservices and their consumers. Therefore, it is not possible to directly access a predefined comprehensive dataset. The proposed framework dynamically loads entities provided by registered microservices so, that, alignment statements can be inferred.