dataesr/scientific-paragraphs-categorization huggingface dataset:
XEOL-XAS acquisition The data were acquired on beam line BM 28 (XMaS) at the ESRF. X-rays were incident at 60°to the sample surface in a footprint around 1 × 2 mm 2 containing 10 11 -10 12 photons s -1. At 9 keV this corresponds to an input power density <7. 2 × 10 -3 W mm -2. Image stacks containing 51 to 112 images were acquired at uniform intervals across x-ray energy range 8. 96 -9. 01 keV, thus spanning the Cu-K absorption edge. Image acquisition times vary between 20 and 300 seconds in this work, but are constant for a given stack acquisition. In a typical experiment on a new material system, image stacks or total XEOL-XANES data (substituting a photomultiplier tube for the camera) are taken through a range of narrow band filters across the bandwidth of the device. Along with similar measurements on reference materials, this allows us to decide which filters to use in future analyses of similar samples.
Acknowledgements The XEOM 1 project is greatly indebted to numerous organizations and individuals viz: The  Research Foundation-Flanders (FWO)  and the  Paul Instrument Fund of the Royal Society  for the funding which the construction;  EVA Surface Analysis (UK)  which supplies the XEOM instrument engineering and the data processing software (esaProject); the beam-line team on BM28 (XMAS) at the  ESRF , especially  Simon Brown  and  Laurence Bouchenoire ;  Adrian Lovejoy ,  David Greenshields , and  Bob Day  of the Electronics Workshop at the  University of Warwick  who built the electronics; The Computing Group at the  ESRF  for advice on interfacing to ESRF systems;  Nigel Poolton ,  Manolis Pantos  and  Rob Pettifer  for their early help and guidance. MH and P-JS are grateful to the  UK Engineering and Physical Sciences Research Council (EPSRC)  and  Ghent University respectively  for their PhD studentships.
The semantic web is an evolution of Web, through ontologies; it aims to allow software agents to understand that, the web pages content is not just a sequence of characters and pixels but intelligible information in the same way as humans. The issue of this paper is to present our sociocultural knowledge ontology (OntoSOC) modeling approach. Which ontology will be used by our platform. OntoSOC modeling approach is based on Engeström"s Human Activity Theory (HAT). HAT is a conceptual framework which the foundational concept is "activity", which is understood as purposeful, transformative, and developing interaction between actors ("subjects") and the world ("objects"). That Sociocultural ontology will allow us to identify which type of data could be shared on our platform. This paper continues with background in which we present the related works to our ontology.
OntoSOC alignment For semantic web, alignment is solution to the interoperability problem between heterogeneous ontologies developed and help us not to recreate that exists but only improve it. OntoSOC is an inter-domain vocabulary. It reuses some concepts of related ontologies. Figure  5  presents the mapping, done between related vocabularies and some OntoSOC concepts. For editing, we used the ontology development tool "Protégé 4. 3". Protégé is a platform of ontologies implementation and management, using tools for modeling different areas and knowledgebased applications with specific ontologies  [9]. Its Populating was done with three use cases data according to "collaborative persona" approach. The cultural community called Naakonda engaged in organizing a cultural event in Mokolo locality, The CDE-SAARE  9  community was building a rural library in Kolara and sport community called Club 2-0 organizing a soccer tournament holidays in « College L"Espoir » of Maroua.
Conclusion and perspectives In this paper, we presented a modeling approach of sociocultural knowledge ontology named OntoSOC. To get there, we simulated the Engeström Human Activity Theory to identify upperlevel ontology concepts and relationships between them. The developed ontology is considered as an improved version of ontology developed by  [5] , But in the Cameroonian context. To fix the interoperability problem we established mapping between OntoSOC concepts and those related. We populated our ontology with three use cases and applied some SPARQL tests. Certainly, the three cases are far to be representative, but, their data helped us to eliminate or explain some inconsistencies. It should be noted that the elimination of redundant triplets was done empirically. We have no guarantee of reaching the minimal coverage.
Nevertheless, the reduction rate is considerable, about 60%. In addition, the notion of sociocultural knowledge is not easy to be well-mastered: it can be specific to a problem, generally to a domain, deep; surface, accurate, uncertain, imprecise or incomplete. However, OntoSOC informs us about data type to be shared and co-constructed into our platform. In following the work, to design the best architecture of our platform we focus on domain ontology modeling. That will allow us to point out if all poles of TAH may still exist depending on community or activity type.
Several Hybrid Transactional Memory (HyTM) schemes have recently been proposed to complement the fast, but best-effort nature of Hardware Transactional Memory (HTM) with a slow, reliable software backup. However, the costs of providing concurrency between hardware and software transactions in HyTM are still not well understood. In this paper, we propose a general model for HyTM implementations, which captures the ability of hardware transactions to buffer memory accesses. The model allows us to formally quantify and analyze the amount of overhead (instrumentation) caused by the potential presence of software transactions. We prove that (1) it is impossible to build a strictly serializable HyTM implementation that has both uninstrumented reads and writes, even for very weak progress guarantees, and (2) the instrumentation cost incurred by a hardware transaction in any progressive opaque HyTM may get linear in the transaction's data set.
Moreover, all current proposals are best-effort, as they may abort under imprecisely specified conditions. In brief, the programmer should not solely rely on HTMs. Several Hybrid Transactional Memory (HyTM) schemes  [9, 11, 26, 28]  have been proposed to complement the fast, but best-effort nature of HTM with a slow, reliable software transactional memory (STM) backup. These proposals have explored a wide range of trade-offs between the overhead on hardware transactions, concurrent execution of hardware and software, and the provided progress guarantees. Early proposals for HyTM implementations  [11, 26]  shared some interesting features. First, transactions that do not conflict are expected to run concurrently, regardless of their types (software or hardware). This property is referred to as progressiveness  [19]  and is believed to allow for increased parallelism.
Second, in addition to exchanging the values of transactional objects, hardware transactions usually employ code instrumentation techniques. Intuitively, instrumentation is used by hardware transactions to detect concurrency scenarios and abort in the case of contention. The number of instrumentation steps performed by these implementations within a hardware transaction is usually proportional to the size of the transaction's data set. Recent work by  Riegel et al. [33]  surveyed the various HyTM algorithms to date, focusing on techniques to reduce instrumentation overheads in the frequently executed hardware fast-path. However, it is not clear whether there are fundamental limitations when building a HyTM with non-trivial concurrency between hardware and software transactions. In particular, what are the inherent instrumentation costs of building a HyTM, and what are the trade-offs between these costs and the provided concurrency, i.
e. , the ability of the HyTM system to run software and hardware transactions in parallel? Modelling HyTM. To address these questions, we propose the first model for hybrid TM systems which formally captures the notion of cached accesses provided by hardware transactions, and precisely defines instrumentation costs in a quantifiable way. We model a hardware transaction as a series of memory accesses that operate on locally cached copies of the variables, followed by a cache-commit operation. In case a concurrent transaction performs a (read-write or write-write) conflicting access to a cached object, the cached copy is invalidated and the hardware transaction aborts. Our model for instrumentation is motivated by recent experimental evidence which suggests that the overhead on hardware transactions imposed by code which detects concurrent software transactions is a significant performance bottleneck  [29].
Software transactions in this implementation remain progressive, while hardware transactions are guaranteed to commit only if they do not run concurrently with an updating software transaction (or exceed capacity). Therefore, the cost of avoiding the linear lower bound for progressive implementations is that hardware transactions may be aborted by non-conflicting software ones. In sum, this paper captures for the first time an inherent trade-off between the degree of concurrency between hardware and software transactions provided a HyTM implementation and the incurred amount of instrumentation overhead. Roadmap. The rest of the paper is organized as follows. Section 2 introduces the basic TM model and definitions. Section 3 presents our model of HyTM implementations, and Section 4 formally defines instrumentation.
Wset E (T k )), is the set of t-objects that T k attempts to read (and resp. write) by issuing a t-read (and resp. t-write) invocation in E (for brevity, we sometimes omit the subscript E from the notation). The data set of T k is Dset(T k ) = Rset(T k ) ∪ Wset(T k ). T k is called read-only if Wset(T k ) = ∅; write-only if Rset(T k ) = ∅ and updating if Wset(T k ) = ∅. Note that we consider the conventional dynamic TM model: the data set of a transaction is not known apriori (i. e. , at the start of the transaction) and it is identifiable only by the set of t-objects the transaction has invoked a read or write in the given execution. For any finite execution E and execution fragment E , E • E denotes the concatenation of E and E and we say that E • E is an extension of E. For every transaction identifier k, E|k denotes the subsequence of E restricted to events of transaction T k.
Finally, τ i is set to ∅ and the primitive returns commit. Note that HTM may also abort spuriously, or because of unsupported operations  [31]. The first cause can be modelled probabilistically in the above framework, which would not however significantly affect our claims and proofs, except for a more cumbersome presentation. Also, our lower bounds are based exclusively on executions containing t-reads and t-writes. Therefore, in the following, we only consider contention and capacity aborts. Slow-path and fast-path transactions. In the following, we partition HyTM transactions into fast-path transactions and slow-path transactions. Practically, two separate algorithms (fast-path one and slow-path one) are provided for each t-operation. A slow-path transaction models a regular software transaction.
Instrumentation Now we define the notion of code instrumentation in fast-path transactions. An execution E of a HyTM M appears t-sequential to a transaction T k ∈ txns(E) if there exists an execution E of M such that: • txns(E ) ⊆ txns(E) \ {T k } and the configuration after E is t-quiescent, • every transaction T m ∈ txns(E) that precedes T k in real-time order is included in E such that E|m = E |m, • for every transaction T m ∈ txns(E ), Rset E (T m ) ⊆ Rset E (T m ) and Wset E (T m ) ⊆ Wset E (T m ), and • E • E|k is an execution of M. Definition 1 (Data and metadata base objects). Let X be the set of t-objects operated by a HyTM implementation M. Now we partition the set of base objects used by M into a set D of data objects and a set M of metadata objects (D ∩ M = ∅). We further partition D into sets D X associated with each t-object X ∈ X : D = X∈X D X , for all X = Y in X , D X ∩ D Y = ∅, such that: 1.
Throughout this proof, we will assume that any two transactions (and resp. execution fragments) with distinct subscripts represent distinct identifiers. For all i ∈ {0,. , |Sm| 2 -1}, let X 2i+1 , X 2i+2 ∈ |Sm|-1 i=0 {X i,1 ,. , X i,m } be distinct t-objects and let v be the value of X 2i+1 and X 2i+2 after E m. Let T s i denote a slow-path transaction which writes nv = v to X 2i+1 and X 2i+2. Let E s i be the t-complete step contention-free execution fragment of T s i running immediately after E m. Let E s i be the longest prefix of the execution E s i such that E m • E s i can be extended neither with the complete step contention-free execution fragment of transaction T f 2i+1 that performs its m t-reads of X 2i+1,1 ,. , X 2i+1,m and then performs read f 2i+1 (X 2i+1 ) and returns nv, nor with the complete step contention-free execution fragment of some transaction T f 2i+2 that performs t-reads of X 2i+2 1 ,.
Intuitively, fa allows fast-path transactions to detect the existence of concurrent updating slow-path transactions. Each time an updating slow-path updating transaction tries to commit, it increments fa and once all writes to data base objects are completed (this part of the algorithm is identical to Algorithm 1) or the transaction is aborted, it decrements fa. Therefore, fa = 0 means that at least one slow-path updating transaction is incomplete. A fast-path transaction simply checks if fa = 0 in the beginning and aborts if so, otherwise, its code is identical to that in Algorithm 1. Note that this way, any update of fa automatically causes a tracking set abort of any incomplete fast-path transaction. Theorem 14. There exists an opaque HyTM implementation that provides uninstrumented writes, invisible reads, progressiveness for slow-path transactions, sequential TM-progress for fast-path transactions and wait-free TM-liveness such that in every its execution E, every fastpath transaction accesses at most one metadata base object.
Related work The notions of opacity and progressiveness for STMs, adopted in this paper for HyTMs, were introduced in  [17]  and  [18] , respectively. Uninstrumented HTMs may be viewed as being inherently disjoint-access parallel, a notion formalized in  [7, 25]. As such, some of the techniques used in Theorems 4 and 9 resemble those used in  [6, 7, 16, 20]. The software component of the HyTM algorithms presented in this paper is inspired by progressive STM implementations like  [10, 13, 27]  and is subject to the lower bounds for progressive STMs established in  [6, 18, 20, 27]. Circa 2005, several papers introduced HyTM implementations  [4, 11, 26 ] that integrated HTMs with variants of DSTM  [23]. These implementations provide nontrivial concurrency between hardware and software transactions, by instrumenting a hardware transaction's toperations with accesses to metadata to detect conflicting software transactions.
Thus, they impose per-access instrumentation overhead on hardware transactions, which as we prove is inherent to such HyTM designs (Theorem 9). While these HyTM implementations satisfy progressiveness, they do not provide uninstrumented writes. However, the HyTM implementation described in Algorithm 1 is provably opaque, satisfies progressiveness and provides invisible reads. Additionally, it uses uninstrumented writes and is optimal with respect to hardware code instrumentation. Experiments suggest that the cost of concurrency detection is a significant bottleneck for many HyTM implementations  [29] , which serves as a major motivation for our definition of instrumentation. Implementations like PhTM  [28]  and HybridNOrec  [9]  overcome the per-access instrumentation cost of  [11, 26]  by realizing that if one is prepared to sacrifice progress, hardware transactions need instrumentation only at the boundaries of transactions to detect pending software transactions.
Inspired by this observation, our HyTM implementation described in Algorithm 2 overcomes the lower bound of Theorem 9 by allowing hardware readers to abort due to a concurrent software writer, but maintains progressiveness for software transactions, unlike  [9, 28, 29]. Recent work has investigated alternatives to STM fallback, such as sandboxing  [2, 8] , and fallback to reduced hardware transactions  [29]. These proposals are not currently covered by our framework, although we believe that our model can be extended to incorporate such techniques. Detailed coverage on HyTM implementations and integration with HTM proposals can be found in  [21]. An overview of popular HyTM designs and a comparison of the TM properties and instrumentation overhead they incur may be found in  [32].
Concluding remarks We have introduced an analytical model for hybrid transactional memory that captures the notion of cached accesses as performed by hardware transactions. We then derived lower and upper bounds in this model to capture the inherent tradeoff between the degree of concurrency allowed between hardware and software transactions and the instrumentation overhead introduced on the hardware. In a nutshell, our results say that it is impossible to completely forgo instrumentation in a sequentially consistent HyTM, and that any opaque HyTM implementation providing non-trivial progress either has to pay a linear number of metadata accesses, or will have to allow slow-path transactions to abort fast-path operations. Several papers have recently proposed the use of both direct and cached accesses within the same transaction to reduce the instrumentation overhead  [26, 32, 33] , although, to the best of our knowledge, no industrial HTM currently supports this functionality.
Collaborative Design The collaborative work carried out by teachers, researchers, and designers attempted to achieve two objectives:  To conceive the Tactileo Map with the aim of providing educators a tool dedicated to the design of fieldwork learning experience. As a result, educators are then able to design their own games adapted to their needs and contexts.  Secondly, those involved strived to design and test a first location-based game as a proof of concept, in order to demonstrate the feasibility of such an approach before it is fully implemented. The design process required the constant communication and collaboration of educators, software developers, and researchers. This communication fostered the methodology of Participatory Design  (NAMIOKA & SCHULER 1993). Participatory Design means that input from users is as important as technical specifications, since users are the most qualified to improve their work environment  (CARMEL et al.
The Tactileo Map, a Geomedia The Tactileo Map is a mobile platform, offering a ludicized fieldwork experience by combining multimedia information, such as images (maps, pictures and schemes), video, and audio components. It is possible to organize these audiovisual components around an interactive story that consists of information displayed by different points of interest located on a digital map. As a media source that carries geo-referenced information, the Tactileo Map is a form of geomedia  (JEKEL et al. 2014 ). The Tactileo Map encompasses two modules: the Tactileo Map client, dedicated to students and accessible with a digital tablet, and the Tactileo Map Editor, dedicated to teachers who want to design their own location-based game. Location-based games designed by teachers can be shared with other teachers and reused.
Through the Editor, educators can start the game and/or activate teammate positions tracking, as well as instant messaging. The editor is powered by a cloud-based data storage system, including a database and storage space. By creating a Tactileo Map scenario, all the information is stored in an online database. Teachers can upload any kind of multimedia content files using the Editor, which then stores them to an Azure Technology cloud server. After creating and saving a new scenario, the Tactileo Map Editor generates a QR code. When this QR code is scanned with a digital tablet, all the information required for playing the game (including the related multimedia content) is downloaded, and usable with the Tactileo Map client. This single one way synchronization between servers and client applications allows the Tactileo Map client to function offline, displaying all useful information to players during the fieldwork course.
The production of virtual characters, depicted in Figure2A, can be summarized in three stages: description, design, and crafting. In the description stage, a writer describes the personality of the character, lists its habits, and gives him a storyline. In the design stage, a designer provides sketches of the character: the body proportions, close ups of the face, details of the body, hair cut, and habitual postures. The outcome of the design stage is used in the crafting stage, where a highly specialised 3D author, the modeler, uses complex software (such as: 3D Studio, Maya, and Blender) to create the skin, the clothes, and the skeletal structure of the character. In the last years, the industry has provided a number of software to ease the crafting stage. Some video games (e. g. : Oblivion 1  ) and stand-alone tools (e.
The votes of the crowd are used to compute a mapping between the traits and the physical attributes, so that an end-user can search for a character by providing the desired personality. Interactive genetic algorithms help to improve the quality of the mapping as long as votes are collected. The virtual characters are the individuals of a generation; their physical attributes are their genetic code. The votes of the crowd contribute to calculate the fitness function. The individuals with highest fitness value are selected to migrate to the next, better, generation, while other individuals are generated by mutation, cross-over, or randomly. The data collected from the crowd are also suitable to infer correlations between physical attributes and the perception of personality traits: a practice known from psychology as Reverse Correlation (see related work in Section II).
H. Implementation and usability The method has been implemented as online web platform using the Django web framework 5 and is running on a Linux virtual machine equipped with an Apache web server  6  and a MySQL database  7. The creation of the virtual characters is based on the open source application MakeHuman (v1. 1) which has been patched to allow an unattended batch processing of the character generation. This version of MakeHuman exposes 217 physical attributes to model the virtual character. Users can access the platform anonymously or register via e-mail authentication. The administrator decides whether an experiment can accept votes from anonymous users or only from authenticated users. The administration interface also allows for the configuration of new experiments and the management of the generations.
IV. EXPERIMENTS This section reports the results of a data analysis conducted on two experiments. The first experiment was originally conducted by Oosterhof et al. [15]  and investigates the correlation between Trustworthiness and Dominance with respect to Age and Gender. The data analysis shows that the fitness function is able to select an elite group of individuals whose correlation increases. Experiment 2, similar to the one conducted by Toscano et al. [14] , aims at finding a set of predictors for the Dominance dimension. The analysis shows how to extract the smallest set of physical attributes which maximize the correlation. Data were collected in one week of testing with a crowd formed by 27 volunteers: 7 female and 20 males, minimum age 18 and maximum 56 (avg=31. 37, sd=10. 06), various educational levels (12 primary school, 1 diploma, 5 master, 9 PhD), and nationalities (18 DE, 1 FI, 3 FR, 4 IT, 1 US).