dataesr/scientific-paragraphs-categorization huggingface dataset:
With this information, we will be able to link values with evidence, supporting informed debate or a process for resolving disagreements or ambiguities. Finally, the value of the Databank will be enhanced by developing support for applying different interpretative frameworks to the underlying data, supporting multiple different views of history, perhaps associated with a particular expert, research team, or research methodology. For example, an estimate of the carrying capacity, which feeds into the estimated population, itself depends on many assumptions of the models used to calculate it. The impact of using alternative assumptions in the models or theories, leading to different interpretations, can then be quickly analyzed and evaluated. The next logical step is to implement a catalog of simulation models, which each can generate predictions about the data based on different mechanisms.
As the background data become more accurate and plentiful, simulations could then be re-run by any Seshat user to discover how such improved or alternative data affect model predictions. This novel capability will lead to a more rapid process of theory evaluation, and to quantification of the limits for the application of a given theory. Despite the current excitement associated with the digital humanities, historians and social scientists have not really begun to utilize the full power of what modern Information Technology can deliver. We believe that the new IT capabilities will eventually (and in the not-too-distant future) transform the field of historical social sciences into what the sociologist Randall Collins termed rapiddiscovery science  (Collins 1994 , although it should be noted that Collins himself was sceptical that such transformation is likely).
Acknowledgments This work was supported by a  John Templeton Foundation  grant to the  Evolution Institute , entitled "Axial-Age Religions and the  Z-Curve of Human Egalitarianism ," a  Tricoastal Foundation  grant to the Evolution Institute, entitled " The Deep Roots of the Modern World: The Cultural Evolution of Economic Growth and Political Stability ," an  ESRC   Large Grant  to the  University of Oxford , entitled "Ritual,  Community , and  Conflict"  ( REF RES-060-25-0085 ), and a grant from the  European Union   Horizon 2020 research and innovation programme  (grant agreement No  644055  [ALIGNED, www. aligned-project. eu]). We gratefully acknowledge the.
It is important to note that, although various members of our research network have particular theories that they are interested in testing, our overarching goal is to test rival theories against each other. Thus, data that we gather in Seshat focus on theoretically relevant variables (variables invoked by various theories, see Building the Databank below), but overall, the Databank is theory-neutral, and all effort is made to ensure that no particular theory is privileged. In the long run, theoretical neutrality is enforced by the open-ended nature of this collective enterprise, which allows for alternative conceptualizations and recoding of any variable by proponents of different theoretical frameworks.
Evaluating transition efforts requires agreement about the specific normative objectives to be met. For a given empirical domain, and measured against a specific societal target, the aim of the assessment will be to characterise goal-setting, momentum, depth and scope of systems changes leading to a transition, e. g. what is meant by the momentum of a sustainability transition? How can it be measured and evaluated? A second bridging step is to orient analysis towards specific governance problems, explicitly mobilising different kinds of information in the assessment of transition strategies, to specify the empirical domains of the analysis to be carried out (setting clear boundaries, scales and temporalities), to establish the common metrics and data that will be transferred (and enable the consolidation of operational linkages) between analytical approaches, and to specify the type of assessment sought.
Acknowledgements The research leading to this article has received funding from the  European Union 's  Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement no. 603942  (PATHWAYS). We would like to thank  Holger Berg ,  Marcel Kok ,  Jonathan Köhler ,  Karoline Rogge , and  Henk Westhoek  for valuable conversations, input and feedback on previous versions of this paper.
Semantic reasoning can help solve the problem of regulating the evolving and static measures of knowledge at theoretical and technological levels. The technique has been proven to enhance the capability of process models by making inferences, retaining and applying what they have learned as well as discovery of new processes. The work in this paper propose a semantic rule-based approach directed towards discovering learners interaction patterns within a learning knowledge base, and then respond by making decision based on adaptive rules centred on captured user profiles. The method applies semantic rules and description logic queries to build ontology model capable of automatically computing the various learning activities within a Learning Knowledge-Base, and to check the consistency of learning object/data types.
The various units/activities within a learning process model can be related to exactly one case and assigned a case identifier  [2]  which results in automatic creation of workflow processes  [3]  and can help to maintain the resulting hierarchy correctly. This approach is made possible by using the semantic annotation scheme to represent sets of various entities, properties and classes within the learning knowledge base and create inferences capable of providing new knowledge or a richer set of intelligence within the model. In this paper, we use ontology web rule languages and schema to discover sets of relationships that can be found within a learning process knowledge base. As a result, suitable learning paths are determined by means of semantic reasoning, which is then used to address the problem of extracting useful patterns from captured data source to provision of knowledge.
Ontological Description of Process Model Ontological description of process model is based on computer logic programming  [4] , and has been related to the natural process of human thinking. Lumpe  [5]  mentions that Inductive intelligent is the process of reasoning from the particular to the general which involves the observation of particular events or data. The approach associates new content with prior knowledge; which can lead to unrelated data being discovered, examined and further grouped and labelled in order to draw conclusions or make predictions based on the analysis of the data. Following the works in  [6]  and  [7] , the ability to analyse information and create concepts is fundamental to ontological reasoning and can be applied towards automation of learning processes, as we define below; Step 1: Examine the Learning Knowledge base to define unrelated entities.
Association rule is now recently being used in application domains such as the web mining and scientific data analysis. The association patterns reveals interesting connection among domain entities, the individual classes and object/data types to provide a better understanding of how the different elements within the Learning Process Knowledge base relate and interact with each other. In the next section, we describe and implement the concept of ontological reasoning of learning activities capable of deducing inferences based on such design rule-base semantic approach to automated learning. The focus is on implementing the learning objects/property restrictions to define the classes and relationships of the entities within the Learning Knowledge-base.
In this paper we propose a novel method for human action recognition based on string edit distance. A two layer representation is introduced in order to exploit the temporal sequence of the events: a first representation layer is obtained by using a feature vector obtained from depth images. Then, each action is represented as a sequence of symbols, where each symbol corresponding to an elementary action (aclet) is obtained according to a dictionary previously defined during the learning phase. The similarity between two actions is finally computed in terms of string edit distance, which allows the system to deal with actions showing different length as well as different temporal scales. The experimentation has been carried out on two widely adopted datasets, namely the MIVIA and the MHAD datasets, and the obtained results, compared with state of the art approaches, confirm the effectiveness of the proposed method.
In order to overcome the above mentioned problems, in this paper we propose a system for Human Action Recognition based on a string Edit Distance (HARED); each action is represented as a sequence of symbols (a string) according to a dictionary acquired during the learning step. The similarity between two strings is computed by a string edit distance, measuring the cost of the minimal sequence of edit operations needed to transform one string into the other; the string edit distance is robust with respect to local modifications (such as the insertion or deletion of symbols) even when they change the length of the string, thus dealing in a natural way with speed changes and with spurious elementary actions. The experimentation, conducted over two standard datasets, confirms the robustness of the proposed approach, both in absolute terms and in comparison with other state of the art methodologies.
EXPERIMENTAL RESULTS The experimentation has been carried out over two widely adopted datasets, namely the Berkeley Multimodal Human Action Detection (hereinafter MHAD) Dataset  (Ofli et al. , 2013)  and the MIVIA Dataset  (Foggia et al. , 2013) , both providing RGB-D images and background for each sequence. Some examples are reported in Figure  3 , while the actions included in each dataset are listed in Table  1. In particular, the MHAD dataset is based on actions with movement in both upper and lower extremities, actions with high dynamics in upper extremities or actions with high dynamics in lower extremities. It contains 11 actions performed by 7 male and 5 female subjects. All the subjects performed 5 repetitions of each action, yielding about 660 action sequences which correspond to approximatively 82 minutes of total recording time.
This protocol simulates, for instance, the home monitoring of an elderly person, where it is possible to adapt the system during the configuration step to the person under test. • Protocol T2: the test person is not known to the system. It implies that the test person is not included in the training set, which is formed by all the repetitions of all the other persons in the dataset. This protocol simulates those applications for surveilling public places, where the system has to recognize actions of unknown persons. For each protocol, the tests have been carried out by a leave-one-out cross-validation strategy; in particular, each test has been repeated for all the repetitions of all the persons of each dataset and finally the average performance have been reported. The results obtained by varying the |C| parameter are reported in Figures  4a  and 4b  for the MIVIA and MHAD datasets, respectively.
We can note that in  both the datasets the performance achieved are very stable with respect to the number of the clusters. It makes the proposed approach very suited for real applications, since the set up of the system can be performed in a very simple way also for an unexperienced operators. Finally, in order to further confirm the effectiveness of the proposed approach, a proper comparison has been carried out with several state of the art approaches, as shown in Table  2. It is worth pointing out that among the methods in Table  2a  only Bow, BM1 and Deep are based on depth information; in fact, the other methods are based on the skeleton acquired by a Mocap system, able to capture 3D position of active LED markers. Even if improving the performance of the action recognition system, this kind of marker can not be easily applied in real environments since it requires ad hoc hardware mounted on the persons under test.
CONCLUSIONS In this paper we proposed HARED, a novel method able to recognize human actions by string edit distance. The main advantages of the proposed approach are the following: first, the string based representation allows the system to explicitly take into account the order of sub-actions within an action; furthermore, the introduction of the edit distance for evaluating the similarity between two strings enables the system to deal with actions of different lengths as well as with changes in the speed of execution; finally, the edit distance, being robust to the presence of spurious symbols, makes the approach suitable to work in noisy environments. The results obtained by testing the proposed methods over two standard datasets confirm its effectiveness: in fact, the accuracy achieved on the MIVIA dataset (85.
2%) is higher with respect to any other considered state of the art method; the one obtained on the MHAD dataset (87. 1%) is still higher with respect to all the methods exploiting depth images instead of skeleton information. Furthermore, the stability observed when varying the number of clusters confirms its usability in real applications, where unexperienced human operator may easily set the system up. Future work will investigate the possibility of further improving the algorithm performance by suitably tuning the edit operation costs during the training phase.
White Spaces (TVWS) technology allows wireless devices to opportunistically use locally-available TV channels enabled by a geolocation database. The UK regulator Ofcom has initiated a pilot of TVWS technology in the UK. This paper concerns a large-scale series of trials under that pilot. The purposes are to test aspects of white space technology, including the white space device and geolocation database interactions, the validity of the channel availability/powers calculations by the database and associated interference effects on primary services, and the performances of the white space devices, among others. An additional key purpose is to perform research investigations such as on aggregation of TVWS resources with conventional resources and also aggregation solely within TVWS, secondary coexistence issues and means to mitigate such issues, and primary coexistence issues under challenging deployment geometries, among others.
• Verification before commercial TVWS operations start. • Involvement of the regulator, industry, and end users in the process, such that the interactions between the relevant stakeholders can be verified. The Ofcom Pilot also aims to test several aspects, such as: • Device operations. • Geolocation Database (GDB) contract qualification. • GDB operation and calculations. • Ofcom's provision of the qualifying GDB listing. • Ofcom's DTT calculation results and provision of Programme Making and Special Events (PMSE) data. • Interference management. • Coexistence. In practice, this further includes verification of other aspects, such as the testing methodology for WSD RF performances, testing methodology for WSDs interactions with Ofcom's "database of GDBs" and selection of the appropriate GDB to use, testing methodology for WSD interactions with the GDB (including aspects such as security), the testing methodology for correct operation of WSDs (e.
g. , RF channel/power settings based on information from the GDB, ceasing to transmit when communication with the Ofcom "database of GDBs" or the GDB itself is not successfully carried out, changing of RF channels and powers if necessary, based on changed information from the GDB, etc. ), the methodology for monitoring interference and the correctness of interference levels around deployments of WSDs under the Ofcom Pilot, the assessment of any possible effects on primary services, and verification of security precautions, among other aspects. The correct performance of all of these elements is essential to the assurance of the viability of the wider picture of TVWS technology, and the confidence that the regulator is able to authorize such a technology for commercial use. Our trials within the Ofcom Pilot are the subject of this paper, noting that reference  [7]  provides more information on our objectives.
III. TV WHITE SPACE DEVICES AND DEPLOYMENT LOCATIONS Our trials have amassed a wide range of devices for use at various times: • Three different forms of WSDs created by collaborators at NICT, Japan, namely: IEEE 802. 11af high-power and lowpower variant WSDs  [8] , and FD/TD-LTE base station and terminal WSDs  [9]. These devices obtained white spaces information from NICT's geolocation database, which is included in the list of qualified databases by Ofcom. The database-device interface is compliant with PAWS. • University of Surrey (Guildford). • Strathclyde University (Glasgow). • Cambridge University. • University of Bath. These locations range from some of the most challenging that it is possible to envisage for operation of WSDs in the UK, such as at the Strand, close to perhaps the most extensive licensed PMSE usage in the world through West-End theatres, concert halls, television studios, etc.
B. WSD Parameter Values and Parameter Acquisition Another key observation of our trials relates to the procedures for WSDs obtaining parameters, and the values of those parameters that are obtained. The Ofcom/ETSI framework specifies the concepts of master and slave devices, and specific and generic WSD operational parameters. The slave devices must obtain parameters via a master device, first forwarding their characteristics to the master device such that the master device can query the database on their behalf. The master device must transmit initial allowed parameters that any slave device can use anywhere within the coverage area of the master, such that the slave is able to transmit its characteristics to the master over those parameters. Parameters that allow this initial, "inspecific" transmission by slave devices are termed "generic" slave parameters, and parameters that are based on the later-obtained precise information from slave devices are termed "specific" slave parameters.
With the growing amount of social media contents, the user needs more accurate information that reflects his interests. We focus on deriving user's profile and especially user's interests, which are key elements to improve adaptive mechanisms in information systems (e. g. recommendation, customization). In this paper, we are interested in studying two approaches of user's profile derivation from egocentric networks: individual-based approach and community-based approach. As these approaches have been previously applied in a co-author network and have shown their efficiency, we are interested in comparing them in the context of social annotations or tags. The motivation to use tagging information is that tags are proved relevant by many researches to describe user's interests. The evaluation in Delicious social databases shows that the individual-based approach performs well when the semantic weight of user's interests is taken more in consideration and the community-based approach performs better in the opposite case.
COMPARATIVE ANALYSIS In this section, we first present the experimentation setup, which allows us to explain the main steps of building users' profiles and also to explain the evaluation process. Then, we present the results of the comparison through two main criteria: time-agnostic (ignoring the time constraint) and time-awareness (taking into account the time constraint). We consider in this comparative analysis the notion of user dimension and social dimension to distinguish between the user's explicit interests (user dimension) and the interests extracted from his egocentric network (social dimension). The goal of evaluation is to compare the effectiveness of the two algorithms mentioned in the section 2. 2, in a social tagging context. We compare these algorithms on the Delicious 4 social database.
CONCLUSION AND PERSPECTIVES In this work, we are interested in user's profile deriving process using user's social tagging network. We have compared two egocentric-based user's profile derivation approaches: the individual-based approach and the community-based approach. This comparative study aims to find out the most effective approach in a social tagging context. In order to take into account the dynamic characteristic of tagging information, we further integrate a time-weight strategy into the two studied approaches. Our experiment in Delicious database shows that the individual-based approach performs well when the semantic weight of user's interests is taken more in consideration and the community-based approach performs better in the opposite case (more importance to structural weight).
Experimental design As reported by  Dhanapal et al. (2015a,b) , 385 soybean [Glycine max (L. ) Merr. ] MG IV accessions were sown in a randomized complete block design with three replications in all four environments (two locations and 2 yr; C09, C10, S09, S10). The accessions evaluated were obtained from the USDA-ARS Germplasm collection based on GRIN (Germplasm Resources Information Network, www. arsgrin. gov) data and with the assistance of the collection curator, Dr. Randall Nelson. The selected accessions were all MG IV genotypes with seed yield. 1. 7 Mg ha 21 and good agronomic traits  (height, lodging, shattering, etc. ). Because SNP data on the germplasm collection were not yet available at the time when entries were selected, genetic diversity was estimated by considering country and province of origin in proportion to the number of entries from that source in the germplasm collection.
Analyses In each environment (C09, C10, S09, and S10), the experimental design was a randomized complete block. Ureide values were log-transformed to equalize variance among environments. ANOVA was conducted on the log-transformed values within and across environments using a general linear mixed model with environments and accessions treated as random effects. Additionally, accession mean ureide concentrations were obtained using BLUP (best linear unbiased prediction) predictors by Proc GLIMMIX of SAS (ver. 9. 3; SAS Institute, Cary, NC). Ureide phenotypic means are shown in Supporting Information, File S1. SNP data on the 374 accessions evaluated in this study were obtained from the SoySNP50K iSelect SNP BeadChip  (Song et al. 2013 ) now curated on SoyBase (www. soybase. org). Brief instructions for obtaining the SNP data are shown in File S2.
For the 374 accessions, 33,957 SNPs had a minor allele frequency (MAF) of $3%, which was the threshold for inclusion in the analysis reported herein. This threshold was chosen to facilitate the identification of rare genotypes. BLUP predictors of ureide accession means derived for each individual environment, and also across all environments, were used for genome-wide association analysis. Linkage disequilibrium (LD) was calculated using all SNPs with a MAF $3% among the 374 soybean accessions and distributed over the 20 soybean chromosomes. Calculation of pairwise LD (r 2 ) among SNPs was based on SNPs within a 1-Mb window using PLINK  (Purcell et al. 2007 ) software. Separate LD calculations were performed for euchromatic and heterochromatic chromosomal regions. JMP Genomics 7. 1 (SAS Institute, Cary, NC) was used to perform the genome-wide association analysis and to generate covariate matrices to account for population structure (Q-matrix) and genetic relatedness (K-matrix).