dataesr/scientific-paragraphs-categorization huggingface dataset:
This way, if the device is tilted, its inclination data is immediately updated in the SCM. Finally, data are injected in the memory in order to be consumed by the Dynamic Hardware Reconfiguration Module (DHRM). This reconfiguration is continuous and carried out in the background. Whenever a sensor value changes, SCM takes it into account. It updates the new value and upgrades it through a shared memory, then DHRM performs optimizations on the screen brightness level. Figure  4  presents the HW and logic sensors, and Fig. 5  presents the SCM process.
The ambient luminosity is also taken into account to adjust luminosity. When the environment is too bright, the screen brightness is increased and vice versa. For brightness management, the power reduction opportunity is about 30 % between the max screen brightness and the min screen brightness. The DHRM relies on the data captured by the SCM and selects four stand device's state. Here is an example for each state: • Hard-to-watch: device shake is too important to watch it correctly. • Mild-motion: from small movement to mild ones like when playing game. • Normal-stand: device left in the same position for a moment. • Abnormal-tilted: set device in ±90 • on x-/y-axis with no motion. Each state is recognized through sensors metrics. DHRM sets the corresponding screen brightness, according to the identified state, as shown in Table  1.
Sound management based on ambient noise Another use case similar to Section 3. 1 was achieved in order to manage the device's speaker level depending on the ambient noise. As in the case of brightness management, we have two main modules. The first one is the Ambient Noise Collector (ANC) and is measuring the ambient noise and shares its to the second module Sound Control Module (SCM) which will adapt the speaker level accordingly. For example, in this scenario when the ambient noise is high , the SCM increases the speaker's sound level. On the other hand, if the ambient noise is at average or low, the module decreases the speaker's sound level. Contrary to the first component, we do not store the values of the ambient noise, and we act dynamically on the speaker's volume. The sound level is modified gradually to avoid any impact on the user satisfaction, on the base of the change blindness  [2].
A sequence is frequent when its occurrence in the database is over a specified threshold. Based on the Apriori algorithm  [6] , GSP starts by collecting the applications whose frequency is higher than a minimal support threshold, to create the length-1 frequent sequence set. Then, it iteratively scans the data to collect the support count in order to select the length-(k+1) frequent sequences from the length-k frequent sequences. The process is repeated until no frequent sequence or no candidate sequence can be found. At the end of the GSP processing, we have the number of applications occurrence from 1 application sequence to k application sequence. Table  4  shows a small example of data provided as input to the GSP process. These data show the running application sequences during four successive Mondays.
The second solution is based on data mining and machine learning techniques. In the paper, we demonstrated that our tools first allow new ILs for collecting data and ALs for controlling hardware elements can be easily added to the framework. At the opposite of the existing methods, where the user needs and behaviors are rarely taken into account, in our second solution, we not only take these elements into account but we also classify possible running applications, in terms of resources needs, and we predict the future applications. In comparison to the advanced energy management provided by windows 8. 1, for some situations the gain offered by our approach reaches 30 %. As perspective, we will consider more possible user patterns and more applications. User satisfaction level will be included as a parameter to control our energy-saving techniques.
The prediction mechanism will be also developed to measure and increase the prediction accuracy. The offline classification phase will be extended to include luminosity, sound level, and GPS needs. We can also improve DHRM by adding the mentioned parameters in  [15] , like battery level and per-user brightness references. For this reason, we will expand the use of IL and AL in order to exploit other sensors, such as compass and GPS and for other mobile platforms such as smartphones and tablets running Android/Linux and iOS. The algorithm used to learn the application sequences, namely GSP, was a first solution, but certainly not the most efficient one that can be found. We intend to select and implement a more recent and efficient algorithm (like in  [20] ), especially to improve the computing time that is dedicated to the maintenance and updating of the discovered sequences.
The purpose of this research is making an ordering food application based on Android with New Order, Order History, Restaurant Profile, Order Status, Tracking Order, and Setting Profile features. The research method used in this research is water model of System Development Life Cycle (SDLC) method with following phases: requirement definition, analyzing and determining the features needed in developing application and making the detail definition of each features, system and software design, designing the flow of developing application by using storyboard design, user experience design, Unified Modeling Language (UML) design, and database structure design, implementation an unit testing, making database and translating the result of designs to programming language code then doing unit testing, integration and System testing, integrating unit program to one unit system then doing system testing, operation and maintenance, operating the result of system testing and if any changes and reparations needed then the previous phases could be back.
Acknowledgments The authors gratefully acknowledge the financial support provided for this study by  GIS LIRGeC and France's Loire Valley Regional Council. The authors acknowledge the cooperation of  Mrs. Annabelle Defrance  in performing experimental tests in partial fulfilment of the requirements for the engineering programme of the Ecole Polytech Montpellier. Special thanks are due to  Elke Gruyaert ,  Anass Cherki El Idrissi ,  Jeroen Dils  and  Mieke De Schepper  for their help performing calorimetric and laser diffraction measurements as well as  Yannick Benoit  for his technical assistance during SEM/EDX measurements. D. P. Bentz  and  E. J. Garboczi  are acknowledged for CEMHYD3D hydration model and  V. Šmilauer  for CemPy.
the discussion in  [7]  and the recent work in  [8]  ). Although they provide solutions for a wide set of SW methods, there are still many metrics, quality measures and problems left uncovered. Moreover, there is nothing there that allows an organization of the tree banks in a structural way with respect to OKE and SW tasks. We argue the urgency and opportunity to define OKE tasks and their associated benchmarks, which would provide the SW community with a native platform to assess research advancement. We think that the right direction is creating RDF graph banks , which would radically change OKE research similarly to how syntactic tree-banks  [9]  did in computational linguistics. A tree-bank is a text corpus annotated with a syntactic sentence structure, providing large-scale empirical data for NLP tasks evaluation.
Background NLP and SW. The integration between Natural Language Processing (NLP) and SW, under the hat of "semantic technologies", is progressing fast. Most work has been opportunistic: on the one hand exploiting NLP algorithms and applications (typically namedentity recognizers and sense taggers) to populate SW data-sets or ontologies, or for creating NL query interfaces; on the other hand exploiting large SW data-sets and ontologies (e. g. , DBpedia,  3 YAGO,  4  Freebase, 5  etc. ) to improve NLP algorithms. For example, large text analytics and NLP projects such as Open Information Extraction (OIE,  [14]  ), Alchemy API,  6  and Never Ending Language Learning (NELL,  [15]  ), perform grounding of extracted named entities in publicly available identities such as Wikipedia, DBpedia and Freebase.
Some work from NLP followed a similar direction  [21]  , e. g. , NELL relation properties and ontology  [22]  , and "formal semantics" applied to NL (e. g. ,  [23]  ,  [24]  ). These works assume some axiomatic forms, and make the extraction process converge to that form. This is good in principle, but the current state of the art does not really help with establishing clear cut criteria on how to convert NL extractions to RDF or OWL. From the perspective of NLP, there are a (few) approaches from natural language formal semantics which output formal data structures, but they are not easily interpretable into SW languages. For example, Discourse Representation Structure (DRS), as shown in the output of Boxer  [23]  , is a first-order logic data structure that heavily uses discourse referents as variables to anchor the predicates into extensional interpretations, and a boxing representation that contextualises the scope of logical (boolean, modal, inferential) operators.
Both issues need non-trivial decisions on the side of RDF and OWL design, such as (i) what variables should be accommodated in a SW representation, or ignored? (ii) What logical operators can be safely represented in the formal semantics supported by SW languages? (iii) What predicates should be represented, and in which form, in RDF or OWL? From the perspective of LD, even porting the original NLP tools data structures into RDF can be beneficial (cf. e. g. , the LODifier method  [25]  ), but the reuse of those data will require some intelligence to be integrated. Our stance is that LD are better served if NLP results are reused with a shared semantics that is ready to be integrated with existing RDF data. For example, if a NLP tool outputs data about Barack Obama (i. e. , its roles, types, relations to other entities), we should be ready to integrate those data to e.
FRED FRED  [19]  is a machine reader for the SW that coherently integrates and represents multiple semantic parsing results such as frame detection, semantic role labeling, entity linking, taxonomy induction, etc. It has been successfully applied in semantic sentiment analysis, relation augmentation and discovery  [7, 12, 13, 19, 27, 28]. FRED generates RDF graph representations out of the knowledge extracted by components dedicated to basic NLP tasks. An example of a FRED graph is depicted in Fig. 1  , whose caption includes an explanation on how to read the graph. FRED formally represents, integrates, improves, and links the output of several NLP tools, which can be "plugged in", and are partly offered as options in the FRED API. FRED is also accessible by means of a Python API, fredlib,  14  which exposes features for retrieving FRED graphs from user-specified sentences, and managing them.
The Python API hides details related to the communication with the FRED service, and returns a FRED graph object that is easily manageable. FRED graph objects expose methods for retrieving useful information, including the set of individual and class nodes, equivalences and type information, categories of FRED nodes (events, situations, qualities, general concepts) and categories of edges (roles and non-roles). It also includes a function to retrieve motifs as defined below. fredlib uses curl to send FRED each sentence, and to extract the semantic triples from the graph of each sentence. Moreover, fredlib reuses rdflib (for managing RDF graphs)  15  and networkx  16  (for managing complex networks) libraries. Following an approach common in linguistic treebanks, FRED has also been used for bootstrapping an RDF graph bank annotated with explicit motifs.
OKE motifs Evaluating against a SW graph bank. There is not much clarity about what should be evaluated as a good NLP-based knowledge extraction tool for the SW. Recently, we have presented the results of a first landscape analysis of existing tools  [7]  , which proposes some mappings between traditional NLP tasks/data structures, and SW tasks/logical patterns. That was a landscape analysis, but proved useful to identify the main areas of vagueness in the interface. In this paper, we try to make that proposal more precise, in a programmatical way by providing the community with methods for creating OKE graph banks. We have started building a OKE graph bank containing RDF named graphs extracted from individual sentences. These named graphs can be modularized into motif occurrences that match customizable motifs.
This evolutionary graph bank was produced from a corpus of 151 sentences 26 (10 from blogs, 22 from Wikipedia, 18 general sentences, 5 from legal documents, 41 from news, 4 from reviews, 3 from advertisement, 51 test sentences). The average sentence length is 16. 7 words per sentence. The same process seen for the first gold standard has been applied. The total number of generated triples is 23,305, 9801 of which are purely semantic triples, 10,650 are annotation triples, 2552 are metadata triples that annotate text segments with parts of speech, 302 are metadata triples annotating named graphs with their identifiers, related sentences, and sentence topics. 26 Publicly downloadable from http://wit. istc. cnr. it/motifs/goldfrombenchmark. tsv. To give an idea of the computational time required by a user to build the first graph bank, the process of extracting triples from FRED and building the named graphs for 100 sentences took 156 seconds (this time is the average of 5 different measurements in order to minimise differences in Internet latency for accessing FRED's RESTful service).
Similarly as with linguistic treebanks, we have used a broad-coverage tool (FRED) to identify motifs, and to generate draft named graphs that can be used to evaluate specific SW tasks after a crowd-sourced correction. We have described that motifs ease the detection of rules and the development of algorithms for sentiment analysis feature detection. In the domain of sentiment analysis, we are currently investigating whether language patterns exist for sarcasm and irony that can be matched to some of the motifs we have introduced in this paper. This is another step toward a new way of thinking about the formal merging of results from OKE, and NLP tools in general. As started in  [7]  , an open-minded attitude towards the class of algorithms and tools to be considered should be accompanied by a serious reflection on which formal representation can work in practice.
The third difficulty consists in taking into account source (document) reliability when using these data in calculations. Belief theory provides elegant solutions to handle this point  [8]. The DSS architecture proposed in this paper aims at coupling generic methods and reusable software modules to meet these challenges, while being instantiated to meet the specific needs of a particular application. More precisely, this DSS relies on the development of a system: (i) able to annotate, store and maintain potentially incomplete or imprecise data extracted from the scientific literature in dedicated databases, (ii) allowing the computation of indicators taking into account data imprecision, (iii) evaluating document reliability. Our approach is compared to the state of the art in Section 2, and the software architecture is detailed in Section 3.
III. ARCHITECTURE OF THE DECISION SUPPORT SYSTEM Fig. 1  details the three steps of the data treatment pipeline, which combines ontologies and soft computing tools. In the first step, experimental data published in scientific papers are annotated thanks to an ontology and assessed in terms of their source reliability. Annotated data (that may be imprecise) are stored in a RDF database and available in open access via permalinks, a SPARQL 1  end-point and a dedicated querying system guided by the ontology. The second step consists in extracting annotated data from the RDF database to compute indicators and data reliability scores. Indicators and data reliability scores can be visualized in the third step as graphical maps.
Heterogeneous experimental data integration (step 1) To facilitate integration of scientific data coming from heterogeneous sources, one of the relevant solutions is to use ontologies  [20, 10, 9]. @Web implements the first step of Fig. 1  as a complete workflow (see Fig. 2 ) to manage experimental data: extraction and semantic annotation of data from scientific documents, data source reliability assessment and bipolar flexible querying of the collected imprecise data stored in a database opened on the Web. @Web relies on an Ontological and Terminological Resource (OTR) which guides the scientific data semantic annotation and the querying. OTR is composed of two layers: a generic one and a specific one dedicated to a given application domain. Since the OTR is at the heart of the scientific data capitalization workflow, @Web can therefore be reused for different application domains: only the specific part of the OTR must be redefined to re-use @Web for a new domain (see  [29]  for a reuse in food packaging domain).
A non-decreasing score function on is then defined, in our case. Second, groups of meta-information that will be used to assess reliability are defined, a group taking values ,. Various types of meta-information have been considered for the data sources: 1. meta-information on the data source itself: for instance the source type (e. g. scientific publication, technical report), the source reputation, citation data; 2. meta-information related to means used to produce data. Such information is typically included in a section called Material and method in papers based on experiments in Life Science, which thoroughly describes the experimental protocol and material. Some methods may be known to be less accurate than others, but are still chosen for practical considerations; 3. meta-information related to statistical procedures: presence of repetitions, uncertainty quantification (i.
Software workflow In this section, due to the lack of space, we only present the step 1 of the workflow which implements the data treatment pipeline presented in Fig. 1. @Web relies on the generic part of the OTR model (see the core ontology in Fig. 4 ) and allows the management of the domain ontology (by example Biorefinery OTR). As @Web relies on the generic part of the OTR model, several OTR dedicated to different application domains can be managed simultaneously in @Web. For instance, in our current implementation, an OTR dedicated to gas transfer in packaging materials has also been defined and is available at http://www6. inra. fr/cati-icat-atweb. Current version of the OTR of units of measure is also available at http://www6. inra. fr/cati-icat-atweb (section @Web platform, thumbnail Ontology, option Unit Ontology).
V. CONCLUSION AND PROSPECT In this paper we have proposed a decision support system based on the integration of multisource scientific data and on the calculation of overall indicators. The DSS combines an ontology-based semantic approach and soft computing tools (fuzzy logic and belief theory) to handle data imprecision and reliability. The ontology is used to guide the annotation of potentially incomplete or imprecise experimental data retrieved from the bibliography in order to store them in a structured database. Moreover a model has been used to assess the reliability of data source, and a ranking of results is done taking into account data imprecision and reliability. The potential of the approach has been illustrated with a study of environmental impact factors of biomass conversion processes.
We present the 2016 ChaLearn Looking at People and Faces of the World Challenge and Workshop, which ran three competitions on the common theme of face analysis from still images. The first one, Looking at People, addressed age estimation, while the second and third competitions, Faces of the World, addressed accessory classification and smile and gender classification, respectively. We present two crowd-sourcing methodologies used to collect manual annotations. A custom-build application was used to collect and label data about the apparent age of people (as opposed to the real age). For the Faces of the World data, the citizen-science Zooniverse platform was used. This paper summarizes the three challenges and the data used, as well as the results achieved by the participants of the competitions.
However, given the current challenges and the trend of data-driven solutions such as Deep Learning, a major obstacle to advancing the state of the art in this area is a lack of large annotated datasets of in the wild data. The 2016 Chalearn Looking at People and Faces of the world challenge and workshop aim to address this issue by organising three competitions on two novel large-scale datasets. Following previous series on Looking at People (LAP) competitions  [14, 13, 9, 12, 4, 10, 42, 15] , in 2016, ChaLearn and Faces of the World organized new competitions and workshops on automatic face analysis in still images. Computational methods for face analysis are genuinely important in many applications in their own right, but also often serve as excellent benchmarks for general computer vision and machine learning algorithms.
Age estimation challenge Age estimation is a difficult task which requires the automatic detection and interpretation of facial features. We have designed an application using the Facebook API for the collaborative harvesting and labelling by the community in a gamified fashion. Age estimation has historically been one of the most challenging problems within the field of facial analysis  [37, 17, 22]. It can be very useful for several applications, such as advanced video surveillance, demographic statistics collection, business intelligence and customer profiling, and search optimization in large databases of facial images. Different application scenarios can benefit from learning systems that predict the apparent age, such as medical diagnosis (premature ageing due to environment, sickness, depression, stress, fatigue, etc.
ChaLearn-AgeGuess Dataset Due to the nature of the age estimation problem, there is a limited number of publicly available databases providing a substantial number of face images labelled with accurate age information. Table  1  shows a summary of existing databases with their main reference, number of samples, number of subjects, age range, type of age and additional information. This field has experienced a renewed interest from 2006 on, since the availability of large databases such as MORPH-Album 2  [38] , which increased by 55× the amount of real age-annotated data with respect to traditional age databases. Therefore, this database has extensively been used in recent works by applying to it different descriptors and classification schemes. However, all existing datasets are based on real age estimation.
In the present challenge, we are running a second round of our previous ICCV 2015 apparent age estimation challenge. For the first stage of the challenge we collected around 2,500 labelled images and got a total of 20,000 votes by many users. For CVPR 2016, we increased the amount of data by adding data from our AgeGuess application. Joining both databases we gathered a total of 4,691 images and nearly 145,000 votes. The challenge received a record participation of about 100 registered teams. One can notice that for this second round of the challenge we augmented the initial data set with 3000 images, allowing participants to develop better feature-learning methods, and at the same time allowing us to perform additional analysis on the results. Since recognizing the apparent age of a person is highly subjective, for collecting the data we relied on the opinions of many subjects using a new crowd-sourcing data collection and labelling application and the data from AgeGuess platform  1.