dataesr/scientific-paragraphs-categorization huggingface dataset:
EXPERIMENTS We performed experiments on a dataset which is shared by the Open University of Humanities 5  (http://www. uoh. fr/front). We measure the performance of resource ranking based on the computation time with difference values of the convergence factor. Then, we evaluate the efficiency of our approach in two use-cases: searching and recommendation. We use Precision, Recall and Accuracy as metrics in our evaluation. Details of the obtained dataset (subsection 4. 1), the metrics (subsection 4. 2), our implementation (subsection 4. 3) and experimental results (subsection 4. 4) are as follows.
Dataset We crawled all resource descriptions that are published on the website of the French Open University of Humanities. Each description is presented under the LOM format and provides basic information of the resource such as title, abstract, keywords, discipline, types, creator and relations to other resources. After parsing the crawled data, we obtained 1294 descriptions, which indicate 62 publishers (universities, engineering schools, etc. ), 14 pedagogic types (slide, animation, lecture, tutorial, etc. ), 12 different formats (text/html, video/mpeg, application/pdf, etc), 10 different levels (school, secondary education, training, bac+1, bac+2, etc. ), 2 classification types (dewey, rameau) and 3 kinds of relation: 'ispartof', 'haspart' and 'isassociatedto'. Among 1294 resources, 880 resources have relations with other resources, in which 692 resources have relation 'ispartof', 333 resources have relation 'haspart' and 573 resources have relation 'isassociatedto'.
Implementation We developed a Java program to crawl and parse the obtained dataset. We used Apache Lucene 6 to remove stop words. We evaluate the efficiency of resource ranking in two cases: searching and recommendation. In the case of searching, we developed a search function that matches a search string with resource descriptions in two cases: matching one of words (using OR operator) and matching all of words (using AND operator) that appear in the search string. We evaluated our approach by comparing the search results with and without resource ranking to the actually selected resources after each search action. In the case of recommendation, we developed a content-based recommendation technique using the vector space model (VSM) and cosine similarity  (Salton et al. , 1975). We assumed that recently viewed resources reflect user interest.
In our approach, instead of statistical metadata, we compute the ranking based on the relation between LOs. Although the input and the objective of the two approaches are different, their common concern, which is LO ranking, is comparable. However, the limitation and availability of a common dataset do not allow us to perform comparable experiments. Our dataset do not contain statistical metadata, whereas their dataset is private and the system 7  is inaccessible. On the research stream of recommendation for e-learning, authors in  (Manouselis et al. , 2011)  and  (Verbert et al. , 2012)  have made deep surveys on existing approaches that apply recommendation techniques to support online education. Common used techniques such as collaborative filtering  (Lemire et al. , 2005; Tang and McCalla, 2005) , contentbased filtering  (Khribi et al.
, 2009; Koutrika et al. , 2008) , association rules  (Lemire et al. , 2005; Shen and Shen, 2004) , user ratings  (Drachsler et al. , 2009; Manouselis et al. , 2007) , context aware  (Verbert et al. , 2012) , feedback  (Janssen et al. , 2007)  analysis and ontological structure  (Tsai et al. , 2006)  have been exploited. However, none of existing approaches considers the resource ranking. In addition, most of them still remain at a design or prototyping stage  (Manouselis et al. , 2011). In our approach, we compute the resource ranking based on their relations. This ranking can be integrated into a search engine or a recommender system to improve the quality of results. Our previous work  (Chan et al. , 2014)  has mentioned the resource ranking. However, it placed the resource ranking in a combination with two other recommendation techniques and has not yet provided experiments on a real historical usage dataset.
ACKNOWLEDGEMENTS This work has been fully supported by the  French General Commission for Investment (Commissariat Général à l'Investissement) , the  Deposits and Consignments Fund (Caisse des Dépôts et Consignations)  and the  Ministry of Higher Education & Research (Ministère de l'Enseignement Supérieur et de la Recherche)  within the context of the  PERICLES  project (http://www. e-pericles. org).
To achieve this goal, our research has centred on enhancing a load balancer so that it selects the nodes which are not close to suffer a MaGC pause as optimal nodes for given workloads. This strategy (shown in Figure  1 ) can keep the system performance safe from MaGC pauses. In  [15]  we proposed a novel MaGC forecast algorithm and demonstrated how a load balancing strategy can exploit the MaGC forecast to improve the performance of a distributed system. However, as that strategy uses a static configuration, it mostly relies on human expert knowledge to optimise its settings to achieve the best system performance. In this paper we extend our previous work by proposing an adaptive GCaware load balancing strategy which utilises JVM data from the underlying application to self-configure according to the GC characteristics of the application, while also using the MaGC forecast to decide on the best way to balance the workload among the clustered applications.
II. BACKGROUND AND RELATED WORK Memory Management in Java. GC is a core feature of Java which offers significant software engineering benefits over explicit memory management. For example, it frees the programmers from the burden of manual memory management, preventing the most common sources of memory leaks and overwrites  [24]. Despite its advantages, the GC comes with a performance cost (as discussed in Section I). It is not possible to programmatically force the execution of a GC  [11] , only to suggest the JVM to execute it by calling the method System. gc() (or Runtime. getRuntime(). gc()). However, the JVM is not forced to fulfil this request and may choose to ignore it. Moreover, the usage of these methods is discouraged by the JVM vendors 1  because the JVM usually does a much better job on deciding when to do GC.
Then, the algorithm uses the most recent historical data, as delimited by a configurable Forecast Windows Size (FWS), to forecast the next MaGC event. This is done in two steps. Firstly, the algorithm forecasts how much memory allocation needs to occur in the Young Generation (YoungGen) before the memory in the Old Generation (OldGen) gets exhausted (hence triggering a MaGC). An example is shown in Figure  2. There, the algorithm uses the OldGen historical data within the FWS (represented as a rectangle) to feed a lineal regression model (LRM) to predict the amount of memory allocation which is pending to occur in the YoungGen (450MB in our example) before the required allocation in the OldGen memory occurs (200MB in our example, assuming there are currently 100MB of free OldGen memory and 100MB have been previously allocated, as per the historical OldGen data).
Secondly, the algorithm forecasts the MaGC event by feeding another LRM with the most recent YoungGen historical data and the result of the previous projection (450MB in our example). This is shown in Figure  3 : By using the historical data within our FWS (represented as a rectangle), the algorithm predicts that the next MaGC will occur in the millisecond 1,200. To assess the performance benefits of adapting the load balancing based on the MaGC forecasts, in  [15]  we also presented a modified version of the round-robin 3 load balancing algorithm. The main difference of our algorithm (compared against the normal round-robin) is that it performs an additional check in the selection of the next node. If the pre-selected node (as per the normal round-robin logic) would suffer a MaGC within a specified time threshold, that node is skipped and the next node is evaluated.
g. , GC and memory snapshots). After the collection occurs, the analyser process checks if the current program family suits the GC characteristics of the underlying program. If it is not the case, the evaluation criteria of the other program families is assessed to identify the new program family, which is then used until the next evaluation phase occurs. These actions retrieved their configurations from the database of program families (represented as dashed arrows in Figure  5 ). Furthermore, any exceptions are internally handled and reported.
IV. EXPERIMENT #1: ASSESSMENT OF FORECAST ACCURACY Here, the objective was to identify which program GC characteristics could work better for defining an initial set of program families. To achieve this, we evaluated the effectiveness of our MaGC forecast algorithm (proposed on  [15] ) against a broad set of Java programs and experimental configurations with the aim of analysing the conditions in which the algorithm performed better. The following sections describe this experiment and its results. Due to space constraint, we only present the most relevant results (as this experiment involved the execution of 69,000 different experimental configurations). Java Benchmarks. The DaCapo 4  benchmark 9. 12 and the SPECJVM 5  benchmark 2008 were chosen as application sets because they offer a wide range of different program behaviours to test (23 between the two benchmarks).
Moreover, they are two of the most widely used Java benchmarks in the literature. As the DaCapo benchmark offers different test workloads per program  6  , the largest workload for each program was used to stress the GC as much as possible. Also their number of iterations were set in such a way that MaGCs were triggered for all the tested heap sizes. This information is summarized in Table  I. Similarly, the SPECJVM programs were configured to trigger MaGCs. It involved setting the iteration time of each program to 60 minutes. As the sunflow program is present in both benchmarks, it was run only once. Finally, 5 different heap sizes were tested per program (100, 200, 400, 800 and 1600MB) with the aim of diversifying more the evaluated GC behaviours (as the heap size is a major factor affecting the GC behaviour  [3] ).
GC Strategies. Among the three most commonly used GC strategies in the industry 7  , we selected the two which tend to suffer the longest pauses  [13] : Serial (sGC) and Parallel (pGC). MaGC Forecast Algorithm. An extensive range of FWS values was tested:  [10. 3000] in increments of 10. Moreover, a value of 100ms was selected as Sampling Interval, assuming that no more than one MiGC would occur within that timeframe (hence not missing to sample any MiGC). Environment. All tests were done in a VM with 4 virtual CPUs, 3GB of RAM, and 50GB of HD; running Linux Ubuntu 12. 04L, and OpenJDK JVM 7u25-2. 3. 10. Additionally, the JVM was configured to initialise its heap to its maximum size to keep it constant during the experiments, and the calls to programmatically request a MaGC were disabled. Metrics.
Based on this behaviour, our analysis centred on understanding the causes of these troughs. To assess if the troughs were caused by the variability of the program behaviours (in terms of memory usage), we analysed the MiGC CV of the programs. This analysis showed us that there is a relationship between the FWS troughs and the changes in the number of MiGCs that occur between the MaGCs (which is the 8 http://ncalculators. com/statistics/coefficient-of-variance-calculator. htm key input used by our MaGC forecast algorithm). Whenever those changes are "too drastic" (reflected in a high value of MiGC CV ), using more historical data is not useful because that history does not properly capture the drastic (several orders of magnitude) changes in memory behaviour. On the contrary, if only the most recent history is used in this scenario (implicitly meaning the usage of a smaller FWS), the forecast accuracy is drastically improved.
Finally, the forecast logic uses Java Management Extension (JMX)  11. This technology was chosen because it is a standard Java component which can retrieve all the required information (e. g. , GC and memory snapshots). Experimental Setup. This experiment used a distributed environment composed of seven VMs: Five application nodes, one load balancer and one load tester (using Apache JMeter 2. 9  12  ). All VMs had the characteristics described in Section IV. The application nodes ran an Apache Tomcat 6. 0. 35. All the 23 previously tested programs were used in conjunction with the two biggest heap sizes (800 and 1600MB) and the two GC strategies used in Experiment #1 (Section IV). Regarding the MaGC forecast algorithm, its configuration was also similar to the one used in Experiment #1 except the FWS, as our program families took the place of this configuration.
Moreover, two types of runs were performed: The first type used the normal round-robin algorithm and was considered the  To be able to call a program from within a JMeter HTTP test script (so that multiple concurrent calls can be invoked), a wrapper JSP was developed and installed in the Tomcat instance of each application node. For each program, a JMeter test script was created, adding some controlled diversity to the workload. For the DaCapo programs, it involved varying the workload size among the calls; while for the SPECJVM programs, it involved varying the execution time. Each JMeter test run lasted 60 minutes and used 50 concurrent users. Also, each individual program call was considered a transaction. Finally, as we were interested in performance, the throughput (tps) and the response time (ms) were our main metrics and were collected with JMeter.
Under these conditions, the only transactions affected by the MaGC event were those in the pipeline to be processed by the node which suffered the MaGC, transactions which eventually led to the triggering of the MaGC event. As a minor contribution of this work, the results of this experiment also allowed us to classify the 23 tested programs according to their GC characteristics (the MaGC D and the MiGC CV ). This program classification is shown in Table  II. Finally, we also compared the resource usages in the load balancer node to understand the costs of our solution (compared against the normal round-robin): The average CPU usage (CP U AV G ) increased in the range of 3% and 7%, while the average memory usage (MEM AV G ) increased in the range of 0. 11 and 0. 35GB. This memory increase was caused by the historical data that the forecast logic maintained.
Study population and comparator The population was representative of the French population, according to demographics and known risk factors of developing CRTI (age, passive or active smoking, living in a community setting). The evaluated strategy was a generalized (100%) use of probiotics among the French population aged 3 to 79 years-old while the reference strategy was placebo i. e. the absence of probiotics use. The maximal probiotic effect was therefore estimated, in the absence of published data on the current probiotics consumption.
Most probably, the effect of having concomitant risk factors on the CRTI risk is less than the multiplication of the relative risks. Further limitations include the extrapolation of the results to other countries and seasons: the model was applied to France. In terms of public health impact it is likely that a pro rata to the population size of other countries might give a rough estimate, especially for the neighboring countries because of free cross-border circulation in the European Union. Similar ILI incidence databases are however necessary for properly adapting the model inputs to another country. As said above, the 2011-2012 season used in the analysis was characterized by a low ILI incidence compared to average rate over the last five winter ILI epidemics. Finally, we limited the effect of probiotics to environmental risk factors to mimic a consumer behavioral approach.
The fine structure is not confined to the OV states. Indeed, a similar mode splitting is also found for EV configurations (see Supplementary Figs  1  and 2 ; Supplementary Note 1). Micromagnetic simulations. To interpret the results, we performed detailed micromagnetic studies with our finite-element code TetraMag  26. The frequencies of spin-torque-induced dynamics in a nanopillar with a similar structure and geometry to that used in the experiment were determined numerically for different combinations of vorticities and core polarities. The simulations include the local Slonczewski spin-transfer torque  27  , which depends on the current polarization P. They also account for the magnetostatic interaction between the magnetic layers and include the Oersted field contribution. Inhomogeneities of the current distribution and the resulting Oersted field have been shown to potentially influence vortex dynamics  28.
The measurements were performed at room temperature. Micromagnetic simulations. The simulations are performed by numerically solving the Landau-Lifshitz-Gilbert equation with our finite-element code Tetra-Mag  26. The spin-transfer torque is taken into account as an additional term added to the Gilbert formulation of the equation of motion, as described in ref. 31. The magnetization dynamics of the coupled vortex oscillators is thus described by the following explicit equation: 1 g dm dt ¼ À 1 1 þ a 2 mÂH eff ð ÞÀ a 1 þ a 2 mÂ mÂH eff ð Þ þ s 1 þ a 2 Á w m Á m p ; P À Á mÂ mÂm p À Á À a mÂm p À Á Â Ã ; ð3Þ where g is the gyromagnetic ratio, m ¼ M/M s is the normalized magnetization, H eff the total effective field comprising the exchange, magnetostatic, anisotropy, external and Oersted fields, a is the Gilbert damping parameter and m p is the normalized magnetization direction of the polarizer.
Acknowledgements We thank  R. Schreiber ,  H. Pfeiffer ,  J. Lauer ,  T. Jansen ,  N. Schnitzler  and  F. -J. Ko  ¨hne for technical support. A. M. D. acknowledges financial support from the  EU  project  STraDy  ( MOIF-CT-2006-039772 ) and  Swiss National Science Foundation Ambizione  grant  PZ00P2 131808.
Mapping involved the use of the CIDOC-CRM system (International Committee for Documentation (CIDOC)-Conceptual Reference Model)-This is a system that has been previously utilised to in the heritage sector and is largely available to the established scientific community. The proposed theoretical representation is based on procedural attachment; moreover, a strong link is maintained between the ontological description of the modelled concepts and the Java programming language which permitted 3D structure estimation and modelling based on a set of oriented images. A very recently discovered shipwreck acted as a testing ground for this project; the Xelendi Phoenician shipwreck, found off the Maltese coast, is probably the oldest known shipwreck in the western Mediterranean. The approach presented in this paper was developed in the scope of the GROPLAN project (Généralisation du Relevé, avec Ontologies et Photogrammétrie, pour l'Archéologie Navale et Sous-marine).
Introduction At the convergence of computer science and the humanities, the Ontology and Photogrammetry; Generalizing Surveys in Underwater and Nautical Archaeology (GROPLAN) project, brings together researchers from academia and industry. The main fields of activity of the GROPLAN project are in underwater and nautical archaeology. Our central objective is to build an information system based on ontologies. In turn, such an information system will provide a formal framework as well as tools to express and manage digital content and expert knowledge in a homogenous manner. Our second objective is the development of methods for collecting data from sites and the integration of such data into the information system during the acquisition phase. In this paper these two aspects are addressed in the context of the exploration of an exceptional archaeological site-The Phoenician shipwreck off Gozo Xlendi.
This was done whilst taking into account the constraints related to the set of three fixed and calibrated cameras. Two software programs were then used to interface with our system: Agisoft's PhotoScan and Bingo. The use of both programs permitted the control the final adjustments and the precision of the resulting model. When comparing the results of the two models, the one obtained using odometry and traditional bundle adjustment which take into account all the possible observations of the 3D points, reveals the presence of residues of approximately 5 mm on the (X,Y) plane (which is lateral to the motion), and within 1cm range depth-wise (cameras pointing direction) on sequences with more than 1000 images. In fact, these data vary in function of the quality of the surveyed terrain. When the seafloor is sandy and low textured, the matched points are less and of lower quality.
This was done whilst taking into account the constraints related to the set of three fixed and calibrated cameras. Two software programs were then used to interface with our system: Agisoft's PhotoScan and Bingo. The use of both programs permitted the control the final adjustments and the precision of the resulting model. When comparing the results of the two models, the one obtained using odometry and traditional bundle adjustment which take into account all the possible observations of the 3D points, reveals the presence of residues of approximately 5 mm on the (X,Y) plane (which is lateral to the motion), and within 1 cm range depth-wise (cameras pointing direction) on sequences with more than 1000 images. In fact, these data vary in function of the quality of the surveyed terrain. When the seafloor is sandy and low textured, the matched points are less and of lower quality.
16 heterogeneous information. In fact, this information can be in the form of data sheets, schematics or drawings, spatial data, textual information or literature, bibliographies, geographical information or even classifications. Figure  11  shows one type of data related to the description of amphora: their profile. In the specific case of amphora, the following information is available: • metrological data (height, maximum diameter, volume,. ); • spatial data (position, convex envelope, 3D representation); • physio-chemical data (type of pottery, colour, container analysis); • archaeological documentation (chronologies, bibliographies, studies). The media holding this information varies from data sheets filled in by archaeologists, to digital 3D models, as well as electronic field databases.
Notion of a Measurable Item The conceptual representation of objects from different fields allows us to give expression to objects, or at least a portion of our knowledge of the object, from one field to another. However, it is impossible to develop measurement techniques and adapted tools if each set of concepts from a particular field is not intelligible to experts of another field. In the scope of this work, beyond our study of a cargo of amphorae, the implicated fields of study are varied and often independent or only possess slight common knowledge. For example, the study of the ship's structure and its cargo can be carried out by different specialists each having their own specific knowledge. The construction of a representation that can be used by experts from different fields then depends on the establishment of a minimal body of knowledge that is coherent and shared by all experts and which also guarantees The media holding this information varies from data sheets filled in by archaeologists, to digital 3D models, as well as electronic field databases.