dataesr/scientific-paragraphs-categorization huggingface dataset:
The value type of each data item is actually an URI. It not only works as the value type's dereferenceable URI for indicating the semantic meaning, but also integrates the resource under LHR with other Linked Data, where the same URI were referenced. The query result shows that the FHIR blood glucose data resource and the Fitbit daily steps performed data resource have been successfully integrated and are retrieved by a single SPARQL query. From the data queried, Alice finds that the steps walked today is not enough, so she decides to do some exercise at home. To have a better performance and outcome for the exercise, Alice wants to make sure that the home environment temperature and humidity are within a comfortable range. So, another SPARQL query in Listing 6 is executed to query for the home environment resources.
The purpose of this SPARQL query is to test that if the WoT temperature and humidity data resource are successfully integrated into the resource graph and can be retrieved in a single SPARQL query. The query result is presented in Figure  7. We can see that both the humidity and temperature resources are retrieved, since both were modeled as lhr:EnvResource, through the mapping between lhr:EnvSSNResource and sosa:Observation. In addition, the value, date, time, and value type also have been mapped into the LHR ontology (through the class lhr:DataItem), so they can be retrieved in this query. The query result shows that the WoT temperature and humidity data resource have been successfully integrated into the resource graph and are retrieved by a single SPARQL query. To present the experiment results in a simple clear way and to avoid repetition, the health resources, and home environment resources are retrieved in two separate SPARQL queries.
Conclusions In this paper, we presented a method to integrate HL7 FHIR interoperability standard implemented health services, SSN and SOSA ontology-described WoT services, ordinary Web-based health services, and WoT services with a few semantic annotations. The proposed OWL-based LHR ontology modeled different categories of health resources and environment resources together in a clear and simple way for upper-level unified use. Thanks to the application of Semantic Web technologies and the well-aligned conceptual model with Linked Data, this method can also integrate health resources from the Linked Open Data on the Web without much effort. With the capability to integrate different categories of health services and WoT services, which usually contain data from healthcare providers, consumer-facing products, and health research, the presented method has the potential to support self-management of health especially for people with chronic diseases.
In addition, the data integrated through this method are annotated with semantics and ontological links, which make them machine-understandable and reusable across systems. It provides possibilities for imaginative applications with the collaborative use in the semantic-rich and cross-sources health and home environment data. To the best of our knowledge, the work in this paper is the first attempt to semantically integrate data from the aforementioned heterogeneous services. In addition, it also tries to keep the flexibility for tolerance of data with different interoperability levels, which is the actual situation of the real-world service environment. To achieve a satisfiable interoperable integration, it would be ideal that the involved health services implement interoperability standards such as HL7 FHIR, and WoT devices could implement description ontology such as SSN and SOSA.
However, obstacles including services with legacy system problems exist. That makes it impossible for all the services to realize in the near future. Diversity may exist among the different health services. The approaches such as the one we proposed can act as a bridge to connect the heterogeneously implemented services. Integrating health data as an ontological model with semantic meaning is beneficial for upper-level use in a more unified way. Limitations, however, exist in this work. Though this method tries to make the integration easy and flexible, it still requires extra semantic annotation by the service providers for integrating the ordinary Web and WoT services. Ontology mapping by extra links to LHR ontology or mapping extension is also needed for the resources from the data aggregation layer to be integrated on the integration layer.
Otherwise, it is unable to identify which part of the resource should be integrated. For the current integration method using LHR ontology, it is possible to lose some of the context or related information by only looking at the data in integrated resource graph. It is difficult for LHR ontology to map to the many different data models and context information such as the unit of a data value. It is important to keep the access to the data resource that has all the information for possible look up. If the service annotates all the relevant data field with semantics, then it would be probable to have the complete data on the aggregation layer available for query with semantics. The more semantic annotations embedded in a resource's representation, the more machine-understandable integration it will be, which can promote more useful applications.
To maximize computational use, there must always be some threads doing computations, while to maximize bandwidth use, there must always be some threads requesting memory contents. For 2018 GPU architectures, at peak bandwidth (excluding cache) and peak computing, reading a float in memory can cost as much as doing 100 floating point operations. Some GPUs can have more than 100K simultaneous threads (though only a small portion would actually use the computational resources at the same time). In the case of B-spline interpolation, not many operations are required per memory access, thus the algorithm will be mainly bandwidth limited. Advanced techniques can be used to maximize bandwidth usage. As we mentioned previously, while groups of threads are waiting on memory operations, which can have high latency if the data are not in cache, other groups of threads can be executed.
This enables to reduce the impact of the latency of these memory operations, but in some cases, it is better in addition to interleave computation between the data loading and the use of the data. In addition, for optimal bandwidth usage, the threads inside a warp/wavefront must read consecutive elements in memory, ideally aligned to fit in as few cache lines as possible. Essentially, if one thread needs data inside a cache line, the whole line is loaded (which typically is 64B for the lowest cache, but is higher if loading from the GPU dedicated memory). Thus the cost is minimized if all threads need the same cache lines. More details can be found in technical documents such as  [8, 2, 10].
The fast path from Line 9 to Line 15 does not perform boundary handling while the slow path from Line 17 to Line 31 performs boundary handling for every coefficient. In the provided code (available at the IPOL web page of this article 2 ) a geometric transformation ϕ : R 2 → R 2 is applied to the input image I. For an input image of size K × L, the interpolated values are computed at locations ϕ -1 (k, l) for (k, l) ∈ {0,. , K -1} × {0,. , L -1}. Depending on the application, global shift, homography, zoom, flow warping or others, the memory access pattern could be optimized, and in some cases the kernel coefficients precomputed. Moreover the last prefiltering transposition could be integrated to the interpolation (by inverting the read indices). Our code doesn't implement these optimizations.
Float Versus Double Precision In this section we would like to answer the following question: Should float precision or double precision be used for B-spline interpolation? Tables  4  and 5  show experimentally that up to a target precision ε of 1e -6 , floats are enough to guarantee the target precision, as the measured maximum relative error is below the target for the tested image. However, for smaller ε, all storage and computations must use double precision. The table shows that if any computation or storage uses float precision, the gain of using double precision for the other computations and storage is lost. The limit of double precision is reached around ε of 1e -13. of having a single code run well on all accelerators. As for orders 0 and 1, while supported in our code, they are not optimized to use the direct hardware support for nearest neighbor and bilinear interpolation.
Acknowledgements The authors would like to thank  Prof. Jean-Michel Morel  for his support, suggestions, and many fruitful discussions. Work partly financed by Office of Naval research grant  N00014-17-1-2552 ,  DGA Astrid project "filmer la Terre " n o  ANR-17-ASTR-0013-01 ,  MENRT  and  Fondation Mathématique Jacques Hadamard.
ACKNOWLEDGMENTS We thank all members of the  Gene Expression and Biophysics Laboratory (Mhlanga Lab ). We thank  N. Crosetto , and members of the  L. Pelkmans lab , members of the  J-C. Olivo-Marin lab ,  K. Schauer  and members of the  B. Goud lab  for comments on this manuscript. A. F. S is a recipient of the postdoctoral fellowship from the  Claude Leon Foundation ,  South Africa. This research has been supported by the following grants all to M. M. M. V2YGE81 (to A. F. S. ) and  PG-V2KYPO7 ,  TA 2011 011  from the  Council for Industrial and Scientific Research  ( CSIR, South Africa ) and by a grant from the  Emerging Research Area Program of The Department of Science and Technology (DST, South Africa) Department of Science & Technology Centre of Competence Grant, SA Medical Research Council   SHIP grant ,  CSIR Parliamentary Grant  and M.
A. EXPERIMENTAL ENVIRONMENTS To implement our experiments, a realistic urban scenario extracted from Nanjing city, Jiangsu province, China, is considered in the simulations, and its area size is set to 4 kilometers (km) × 3. 7 km, as shown in Fig. 8 , and practical traffic dataset of mobile vehicles moving on the above region is given by Nanjing Transport Bureau from January 1 to April 5, 2018. In addition, we assume that the type of mobile EVs is TESLA Model S, and EV battery capacity C and charging power PW are set to 85 KiloWatt-hour (KWh) and 17. 2 KiloWatts (KW), respectively, initial state of remaining energy in the battery for each EV is uniformly selected from  [15, 80]  KWh, and the penetration level of EVs is give as 10% -20%. Moreover, we consider that maximum charging waiting time T max cw is given as 4 hours (h), traffic information predicted interval u is 0.
This work was supported in part by the  Fundamental Research Funds for the Central Universities  under Grant  30918014107  and Grant  30919011228 , in part by the  Natural Science Foundation of Jiangsu  under Grant  BK20190444  and Grant  BK20170480 , in part by the  National Natural Science Foundation of China  under Grant  11801494 , and in part by the  Fund from Jiangsu Key Laboratory of Image and Video Understanding for Social Safety  under Grant  KFKT201805.
Local descriptors are aggregated into global image representations by employing feature encoding techniques such as Fisher Vector (FV)  [7]  and VLAD  [8]. A DCNN can be trained on a large-scale dataset and then can be used in two different ways to annotate new test keyframes with semantic concepts. a) As standalone classifier: Each test keyframe is forward propagated by the network and the network's output is used as the final class distribution assigned to the keyframe [9, 11], a process a. k. a. direct classification. b) As feature generator: The training set is forward propagated by the network and the features extracted from one or more layers of the network are used as feature vectors to subsequently train one supervised classifier (concept detector) per concept. Then, each test keyframe is firstly described by the DCNN-based features and subsequently served as input to the trained classifiers.
DCNN training requires the learning of millions of parameters, which means that a small-sized training set, which is the case for video datasets, could easily over-fit the DCNN on the training data. It has been proven that the bottom layers of a DCNN learn rather generic features, useful for different domains, while the top layers are task-specific  [15]. Transferring a pre-trained network in a new dataset by fine-tuning its parameters is a common strategy that can take advantage of the bottom generic layers and adjust the top layers to the target dataset and the new target concepts. Fine-tuning is a process where the weights of a pre-trained DCNN are used as the starting point for a new target training set and they are modified in order to adapt the pre-trained DCNN to the new target dataset [16].
In this work, for performing dimensionality reduction efficiently, we use a nonlinear discriminant analysis technique called accelerated generalised subclass discriminant analysis (AGSDA)  [17]  along with its GPU implementation  [22, 23]. This method identifies a discriminant subspace of the input space in three steps: a) Gram matrix computation, b) eigenvalue decomposition of the between subclass factor matrix, and c) computation of the solution of a linear matrix system with symmetric positive semidefinite (SPSD) matrix of coefficients. Based on the fact that the computationally intensive parts of AGSDA, i. e. Gram matrix computation and identification of the SPSD linear matrix system solution, are highly parallelisable, a GPU implementation of AGSDA is employed  [23]. The experimental evaluation of this method (GPU-AGSDA) on large-scale datasets of TRECVID for concept and event detection show that, combined with LSVM, outperforms LSVM alone in training time, memory consumption, and detection accuracy.
Dataset and Experimental Setup TRECVID Semantic Indexing (SIN) task  [45]  is a popular benchmarking activity that provides a large-scale dataset for video concept detection. The task is as follows. Given a set of shot boundaries for the SIN test dataset and a pre-defined list of concepts, participants are asked to return for each concept, the top 2000 video shots from the test set, ranked according to the highest possibility of depicting the concept. The presence of each concept is assumed to be binary, i. e. , it is either present or absent in the given standard video shot. If the concept was true for some frame within the shot, then it was true for the shot. This is a simplification adopted for the benefits it affords in pooling of results and approximating the basis for calculating recall.
A list of 346 concepts is provided that has been collaboratively annotated by the participants and by Quaero annotators. In this study our experiments were performed on the TRECVID 2013 SIN dataset  [45]  that provides the following materials: • a development set that contains roughly 800 hours of Internet archive videos comprising more than 500000 shots; • a test set that contains roughly 200 hours of videos, comprising 112677 shots; • shot boundaries (for both sets); • a set of 346 concepts for training; • elements of ground-truth: some shots were collaboratively annotated. TRECVID 2013 SIN task was evaluated for 38 semantic concepts by calculating the Mean Extended Inferred Average Precision (MXinfAP) at depth 2000  [46]. MXinfAP is an approximation of the Mean Average Precision (MAP) that has been adopted by TRECVID  [45]  because it is suitable for the partial ground-truth that accompanies the TRECVID dataset  [45].
We present results both on the TRECVID SIN 2013 dataset but also on the TRECVID SIN 2015 dataset that consists of another test set and is evaluated on a different subset of the 346 available concepts. We observe that the proposedcascade performs slightly better in terms of MXinfAP compared to the late fusion method, achieving 0. 8% and 0. 4% relative improvement for the SIN 2015 and SIN 2013 dataset, respectively. At the same time it is computationally less expensive during classification as we will see in the next section. Dimensionality reduction with AGSDA leads to more discriminative features which is indicated by an increase of the MXInfAP from 23. 9% to 24. 98% and 29. 96% to 30. 03% for the SIN 2015 and SIN 2013 dataset, respectively. Using also the MTL-Lasso method in the conceptdetection pipeline, instead of STL techniques, significantly outperforms all the previous methods.
Finally, exploiting label relations results to further improvement. We conclude that the methods presented in this section, i. e, cascades of detectors, MTL, two-layer stacking architecture that exploits label relations and dimensionality reduction using the AGSDA algorithm presented in section 1. 2. 2, are complementary and combining all of them in a concept detection system can boost concept-based video search reaching the best overall MXInfAP of 26. 03% and 32. 57% for the SIN 2015 and SIN 2013 dataset, respectively. These numbers are among the state of the art results for these specific datasets and, although seemingly low (since they are much lower than 100%), a qualitative analysis shows how good results they represent. For example, considering the concept "chair" that reaches an infAP of 32.
84% on the SIN 2013 dataset, we observe in the resulting ranked list of concept-based retrieval results that among the top-20 retrieved keyframes all of them are positive; among the top-50 retrieved keyframes 46 are positive; and among the top-100 ones 86 of them are positive. To investigate the statistical significance of the differences between the results of the various methods/combinations reported in Table  1. 5 we used a paired t-test as suggested by  [50] ; in Table  1. 5, the absence of * suggests statistical significance. We found that differences between the complete method (Cascade+AGSDA+MTL+LP) and all the other methods are significant (at 5% significance level), except for the run that combines Cascade+AGSDA+MTL. Investigating label correlations in a system that already combines cascades, AGSDA and MTL does not lead to significant improvement.
2 minutes in our experiments, which is about three orders of magnitude slower than the proposed second-layer methods. As a result of the inevitable computational complexity of the first layer of the stack, the execution time that P-LP requires can be considered negligible. This is in sharp contrast to building a multi-label classifier directly from the low-level visual features of video shots, where the high requirements for memory space and computation time that the latter methods exhibit make their application to our dataset practically infeasible. Specifically, the computational complexity LP when used in a single-layer architecture depends on the complexity of the base classifier, in our case the LSVMs, and on the parameters of the learning problem (e. g. , number of training examples, feature video dimensionality).
LP would build a multi-class model, with the number of classes being equal to the number of distinct label sets in the training set; this is in order of N 2 in our dataset. Taking into consideration the dimensionality of the utilised feature vectors, using any such multi-label learning method in a single-layer architecture would require several orders of magnitude more computations compared to the BR alternative that we employ as the first layer in our presented stacking architecture. We conclude that the major obstacle of using multi-label classification algorithms in a one-layer architecture is the computation time requirements, and this finding further stresses the merit of using a multi-label stacking architecture. Finally, the training of the MTL algorithms, compared in Table  1. 3, is computationally more expensive than the training of linear STL alternatives (e.
Specifically, two types of visual information have been used for training the event detectors: motion features and DCNN-based features. We briefly describe the different visual modalities in the following: • Each video is decoded into a set of keyframes at fixed temporal intervals (approximately 2 keyframes per second). We annotated the video frames based on 12988 ImageNet  [47]  concepts, 345 TRECVID SIN  [63]  concepts, 500 event-related concepts  [64] , 487 sport-related concepts  [65]  and 205 place-related concepts  [66]. To obtain scores regarding the 12988 ImageNet concepts we used the pre-trained GoogLeNet provided by  [12]. We also experimented with a subset of the 12988 concepts; in order to do that we self-trained a GoogLeNet network  [67]  on 5055 ImageNet concepts (gnet5k). To obtain the scores regarding the 345 TRECVID SIN concepts and the 487 sport-related concepts we fine-tuned (FT) the gnet5k network on the TRECVID AVS development dataset and on the YouTube Sports-1M dataset  [65] , respectively.
We also used the EventNet  [64]  that consists of 500 events and the Places205-GoogLeNet, which was trained on 205 scene categories of Places Database  [66]. All the above networks were also used as feature generators. I. e. , the output of one or more hidden layers was used as a global frame representation. • For encoding motion information we use improved dense trajectories (DT)  [68]. Specifically, we employ the following four low-level feature descriptors: Histogram of Oriented Gradients (HOG), Histogram of Optical Flow (HOF) and Motion Boundary Histograms in both x (MBHx) and y (MBHy) directions. Hellinger kernel normalization is applied to the resulting feature vectors, followed by Fisher Vector (FV) encoding with 256 GMM codewords. Subsequently, the four feature.
The goal of MED task is to support the creation of event detection and retrieval technologies that will permit users to define their own complex events and to quickly and accurately search large collections of multimedia clips. For this reason MED provides large-scale datasets for training and evaluation purposes as well as a set of events which consists of an event name, definition, explication (textual exposition of the terms and concepts), evidential descriptions, and illustrative video exemplars. K ∩ (a, b) = k i=1 min(a i , b i ). For training purposes of learning form positive examples method we used the PS-Training video sets consisting of 2000 (80 hours) positive (or near-miss) videos as positive exemplars, and the Event-BG video set containing 5000 (200 hours) of background videos as negative ones.
To evaluate both systems (i. e. learning form positive examples and zero-example learning) a common video dataset was used. We processed the MED16-EvalSub set consisting of 32000 videos (960 hours). We evaluate all the methods on the 20 MED2016  [76]  Pre-Specified events (E021-E040) for which event kits are provided. We evaluate all the methods in terms of the Mean Average Precision (MAP) and Mean Inferred Average Precision (mInfAP).
5%). Similarly to the concept detection case, a qualitative analysis shows that the eventbased video search works satisfactorily. For example, considering the event "Horse riding competition" that reaches an InfAP@200 of 13. 9% and AP of 16. 4% on the MED16-EvalSub dataset, we observe that among the top-20 retrieved videos 16 of them are positive, and similarly among the top-50 retrieved ones the 32 are positive.
Conclusions In this chapter we surveyed the literature and presented in more detail some of the methods that we have developed for concept-based and event-based video search. In terms of concept-based video search we presented methods from three machinelearning areas (cascades of classifiers, multi-task learning and exploitation of label relations), that can improve the accuracy of concept-based video search and/or reduce computational complexity, in comparison to similar methods, thus enabling effective real-time video search. Our experiments on two large-scale datasets, i. e. , TRECVID SIN 2013 and TRECVID SIN 2015, show the effectiveness and scalability of the presented methods, and especially how combining cascades of detectors, MTL, a two-layer stacking architecture and dimensionality reduction with AGSDA in a concept detection system can boost concept-based video search.