dataesr/scientific-paragraphs-categorization huggingface dataset:
Web APIs (Application Programming Interface) are a common means for Web portals and data producers to enable HTTP-based, machine-processable access to their data. They are a prominent source of information*1 pertaining to topics as diverse as scientific information, social networks, entertainment or finance. The methods of Linked Data (Heath and Bizer 2011) similarly aim to publish machine-readable data on the Web, while connecting related resources within and between datasets, thereby creating a large distributed knowledge graph. Today, the biodiversity community is increasingly adopting the Linked Data principles to publish data such as trait banks, museum collections and taxonomic registers (Parr et al. 2016, Baskauf et al. 2016). However, standard approaches are still missing to combine disparate representations coming from both Linked Data interfaces and the manifold Web APIs that were developed during the last two decades to expose legacy biodiversity databases on the Web.
The SPARQL Micro-Service architecture (Michel et al. 2018)  tackles the goal of reconciling Linked Data interfaces and Web APIs. It proposes a lightweight method to query a Web API using SPARQL (Harris and Seaborne 2013), the Semantic Web standard to query knowledge graphs expressed in the Resource Description Framework (RDF). A SPARQL micro-service provides access to a small RDF graph, typically resource-centric, that it builds at run-time by transforming a fraction of the whole dataset served by the Web API into RDF triples. Furthermore, Web APIs traditionally rely on internal, proprietary resource.
I. INTRODUCTION The appearance of Internet of Things (IoT) concept is shaping and reshaping how future services are going to be define. The main idea behind this concept is to develop different type of communi-cation network based on group of physical objects or simply "things". The IoT objects embedded with electronic chips, software, sensors and internet connectivity to collect and process data from the environment or affecting it by deploying actuators. IoT combines real-world data and computer processing to lower the costs and increase the efficiency and accuracy. Each thing can be recognized separately through its embedded computing system and is able to communicate with other things through Internet infrastructure. Recently the number of connected and embedded smart devices grows rapidly.
As shown, the IoT main characteristics is presented in the core circle of the figure including anywhere, anything and anytime features that indi-Fig. 1 : Internet of Things -main concept and functionalities cate limitless IoT realm. It is noteworthy to know applying this technology with CPS (Cyber Physical Systems) and Cloud Computing creates Industry 4. 0. The middle circle includes the general application domains. Cross-system automation makes tasks to be performed more accurately, coordinately, and conveniently. In the outer circle, we illustrate a general cyber-physical learning process. Each cycle of this process boost systems knowledge and performance. First, monitoring operations carried out by sensors. Then the system will measure and store the data. In the control phase, it will check whether measured data has touched or passed pre-defined minimums, maximums or thresholds.
IoT in combination with Cloud functionalities provide a new phenomena called as Cloud of Things (CoT) where enable many new possibilities such as Big Data processing as well as covering security concerns, resource constraints, and scalability to some extend. CoT creates new revenue streams, improve customer services and inspire product innovations. As Figure  2  shows, CoT can be considered as a composition of at least this five enabling technologies. Among these enablers, middleware play a key role in CoT (similarly to IoT). The main focus of this paper is on middleware technologies and it aims to study their roles and necessities in CoT environment. Middleware is a software layer that sits between applications and the objects. It aims to provide solutions to frequently encountered problems such as heterogeneity, interoperability, security and dependability  [5] ?.
A. Information exchange and storing Systems that benefit from this domain (i. e. transaction systems) should provide the ability for users to pass their request to middleware for ex-changing it with other nodes or saving their information on a database without any problem. For instance, this service allows a group of operators to use a contextaware middleware to manage a smart environment. Internet of things is primarily based on ubiquitous and pervasive computing so communicating and storing data through this distributed environment require appropriate considerations.
B. Data management and analytics This is absolutely a huge and complex domain. Users should be able to manage databases, data security, data quality, reference and master data, Metadata and other topics. By using these services there are no longer any concerns about managing and processing data, especially Big Data. Each user can implement personal policies without any concerns. Also, services like data processing and data acquisition should be presented by IoT middleware. Data analytics is another important service that the middleware provides it by extracting statistics data or visualizing obtained data and sends them to the user application.
D. Communication Actually, this domain is a baseline for many other domains. A communication middleware provides a framework or environment which enables two applications to negotiate and exchange data in a distributed system. Communication middleware provides an abstraction of the network protocol for software application and reduces complexity of designing low-level communication mechanisms. DDSS is an example for communication middleware  [24]. In addition to the above mentioned domains and by considering system services and operations, there are some other special-purpose applications which need various features of middleware domains to perform their task properly. For example WebCrawler  [25]  and GRank  [26]  are two search engines middleware that uses a combination of aforementioned domains capabilities.
It includes three main modules of context-aware, activity-aware and location-aware that work with GSN (Global Sensor Network) as cloud-based companion platform in an integrated system. Context-aware data streaming engine called mobile sensor data engine is based on previous C-MOSDEN also have context-aware functionality and supports push/pull data streaming. Activity aware module can recognize 6 activities of I) moving in a vehicle II) cycling III) walking IV) running V) still (not moving) VI) tilting (falling) and a combination of them. Location aware module recognizes when a device enter or exit from an area which is defined by longitude, latitude, and radius. GSN aims at providing flexible middleware to address sensor data integration and distributed query processing. It is based on four basics principles: A) simplicity B) adaptability C) scalability D) lightweight implementation.
Thing watcher automatically observes and learn normal state and alert the end user if it detects any anomaly. By using things predictor system can predict relevant outcome. Things optimizer can improve future performance and results with automated prescription and simulation. The unique frame work allows being integrated with technologies like industrial connectivity (Kepware) for IIoT (Industrial IoT). Kepware's communications platform is a complete solution for device-to-cloud interoperability. KEPServerEX provides a single source of industrial automation data to multiple applications, allowing users to connect, manage, monitor, and control diverse automation devices and software applications through one intuitive user interface. KEPServerEX provides two options for interoperability with the ThingWorx IoT Platform.
D. Carriots Carriot 6  is a cloud-based IoT platform (as a PaaS) mainly designed for M2M projects like smart city and smart energy. This platform provides some modules for common M2M projects like date collection and storage, security, and device management. Seven layer architecture of the platform can provide all requirements of diverse projects and M2M applications. Carriots projects can run it in five steps of connecting the devices, collecting data, management of devices and data, building the app and running the project. It collects and stores raw streams of data from any devices by sensors through a web connectivity (gateway or embedded 3G or GPRS modems) by using MQTT protocols and send to a HTTP/ HTTPS RESTful API in XML or JSON format. It can also integrate with other systems and pull or push data from CRM and ERP or any other APIs like Zoho and Dropbox.
The platform put forward Apikeys (define privileges and visibility), HTTPS, HMAC hash solutions for security issues. Carriots include a NoSQL big database to store data in two replication sites with high transactions. The data can be accessible through PUSH and PULL strategies of API. The powerful feature in Carriot is the capability of writing and executing code by an arbitrary code. By writing Groovy script and combining with SDK, it can execute any action in Apps and run in the cloud. Carriots device management module allows the user check status and manages configuration and firmware remotely. Carriots gives customers freedom and flexibility in matching with the wide range of hardware and some platforms such Microsoft Azure that is suitable for public cloud services. The main benefits of Carriots platform is saving development time, lower costs (in development and operation), reliability and scale up from tiny prototypes to thousands of devices (free account for up to 10 devices).
E. CHOReOS This middleware is set of software components that implement and execute large-scale web service compositions. CHOReOS tried to converge Internet of Things, Cloud Computing and the Internet of services. By using higher-level abstraction and services, this middleware tries to make scalable, complex and adaptive service structures. CHOReOS is the mixture of four main modules. I) eXecutable Service Composition (XSC) which is responsible for coordinating the proper and required services for things. II) eXtensible Service Access (XSA) that is used for accessing and selecting services and things. III) eXtensible Service Discovery (XSD) which is a management framework for protocols and processes to find requested or suitable services and things. IV) Cloud and Grid Middleware that is responsible for computational components and control implementation of choreographies.
XSC have two main components 1) Composition and Estimation (C&E) 2) Reconfiguration Management for Service Substitution. The first one is responsible for the composition of thing-based services and the second one is a composition of functional abstraction services and impact analyzers. XSA is the next module which includes A) XSB that backs seamless integration on heterogeneous environment B) EasyESB presents a scalable and proper environment for business services to get into large choreographies C) LSB provide services related to things and addresses IoT challenges. XSD includes I) AoSBM Discovery for resource discovery process by using service abstraction II) Things Discovery for the probabilistic method in order to implement it III) Plug-in Manager which is a flexible framework for supporting all discovery solutions.
Cloud and Grid Middleware has three components 1) The Storage Service that deploy a database server on cloud and define access control policies 2) The Grid Service which provides Grid Computing as a service; this means offering highperformance computing applications enough needed resources 3) The Enactment Engine is the main section of Cloud Grid and Middleware and is a composition of the CHOReOS middleware, business services, and coordination delegates. Cloud and Grid components export RESTful services  [28].
G. DropLock T. L. Vinh el al. [30]  present a middleware architecture to integrate mobile devices, sensors and cloud computing. This middleware by using cloud services provides scalability, sufficient data storage, data processing capabilities and energy saving management. The specific goal here is the convergence of Mobile Cloud Computing (MCC) and IoT. Authors presented a basic requirements framework for designing CoT middleware which includes Network management services, Data management services, System management services and Security and authentication services domains. For security concerns, before communicating with other devices, it is essential to perform the authentication process. For resource constraint devices, authentication could be performed by lightweight cryptography algorithms.
This layer is a three-tier model which includes of Cloud tier, Web server tier, and Mobile app tier. In the cloud tier, web applications arranging data into an open-source software framework like Hadoop for storing them and running applications on distributed hardware. In web server tier it uses OSGi (a java framework) to dynamically publish, discover and bind services to Bundles. Mobile app tier makes mobile devices to access web applications to gain best services. In cloud tier, multiple servers may send recommendations about best available parking lots for users therefore, a cloud-based middleware developed with three clusters, A) Kafka a messaging platform for load balancing, B) Storm that consumes topics produced by Message Queue and process data (i. e. filtering, mining, clustering, etc), C) HDFS that contains useful dataset which web applications can access to them in real-time by put, scan, add, get operations  [31].
I. OpenIoT OpenIoT (open source Internet of Things)  [32]  is an open source IoT platform with semantic interoperability in the Cloud. This platform as a standardbased model for physical and virtual sensors applies Semantic Sensor Networks (SSN) ontology for semantic unification of IoT systems. Discovering and collecting data from mobile sensors is achievable through a publish/ subscribe middleware called Cloud-base Publish/ Subscribe (CUPUS). The CU-PUS interact to cloud database via X-GSN (eXtended Global Sensor Network). The architecture of OpenIoT includes seven elements including, a sensor middleware, cloud data storage, Scheduler, Service Delivery and Utility manager (SD&UM), Request definition, Request presentation and Configuration and monitoring. For security and privacy issues, the platform adopts a flexible and generic approach for authentication, authorization and User management through its specific privacy & security module and CAS (Central Authentication Service) service.
K. Capnet Capnet is mainly developed for mobile multimedia applications. This middleware is contextaware and with a common interface for context sensors, it makes it easier to embed a new sensor in the system. Capnet tries to address requirements such as mobility of the software components, multimedia applications, and adaptation. Context-aware pervasive networking focus on adapting service application according to user's environmental position and his personal preference. Service discovery and component management also provide the required level of transparency for applications making them able to perform operations in the mobile environment. This middleware is capable of asynchronous messaging, context management, service discovery, resource management and etc. Capnet can perform a proper task according to component mobility; it can decide whether to run a component on the client side (mobile device) or server side (fixed PC) depending on the resource requirements of the starting application or component  [34].
H. Standardization needs Based on the conducted literature studies, we found out that one of the main and obvious problems is the lack of standardization in designing approaches. As a suggestion for future work, a proposal of layered-based model can be helpful and provides significant stability. although, by considering heterogeneity nature of Internet of Things it may seem quite challenging and add more complexity to the already complex scheme. In a nutshell and as concluding remarks of this study, we present a head to head table (Table  IV ) which compares different features of CoT-based and non CoT-based Middleware. As we found, CoTbased Middleware are suitable in several usecases including social, smart home, smart city, smart agriculture, smart animal farming, smart grids and smart retail applications which produce massive data and don?t need hard real-time processing.
The nonpurifying calculi detect unsatisfiability slightly more frequently, whereas the applicative encoding tends to find more saturations. It seems that, due to the large amount of higher-order reasoning necessary to solve TPTP problems, the advantage of our calculi on the first-order parts of the derivation is not a decisive factor on these benchmarks. 1:33 Concerning question (3), the nonpurifying calculi outperform their purifying relatives across all benchmarks. The raw data show that on most benchmark sets, the problems solved by the nonpurifying calculi are almost a superset of the problems solved by the purifying calculi. Only on the SH256 benchmarks, the purifying calculi can solve a fair number of problems that the nonpurifying calculi cannot solve (11 problems for the intensional calculi with LPO and 9 problems for the extensional calculi with LPO).
Acknowledgment. We are grateful to the maintainers of StarExec for letting us use their service. We want to thank  Petar Vukmirović  for his work on Zipperposition and for his advice. We thank  Christoph Benzmüller ,  Sander Dahmen ,  Johannes Hölzl ,  Anders Schlichtkrull , Vol. 17:2 SUPERPOSITION FOR LAMBDA-FREE HIGHER-ORDER LOGIC 1:35  Stephan Schulz ,  Alexander Steen ,  Geoff Sutcliffe ,  Andrei Voronkov ,  Daniel Wand ,  Christoph Weidenbach , and the participants in the 2017 Dagstuhl Seminar on Deduction beyond  First-Order Logic  for stimulating discussions. We also want to thank  Christoph Benzmüller ,  Ahmed Bhayat ,  Wan Fokkink ,  Alexander Steen ,  Mark Summerfield ,  Sophie Tourret , and the anonymous reviewers for suggesting several textual improvements. Bentkamp and Blanchette's research has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreement No.
The analysis of tumor growth curves is standard practice in experimental oncology including tumor immunology. In experimental oncology, cancer cells are inoculated into rodents (mostly mice) and their growth is monitored by measuring tumor diameter, surface or volume over time as a function of distinct treatments. Then, different groups of tumors/treatments are compared among each other for their evolution and possible responses to treatment. The R package TumGrowth has been created as a software tool allowing to carry out a series of statistical comparisons across or between groups of tumor growth curves obtained in a standard laboratory, for experimenters with limited knowledge in statistics. TumGrowth is freely available online at https://kroemerlab. shinyapps. io/TumGrowth/ and can be downloaded into any computer.
Driven by these premises, we decided to create an annotated software package that can be used by experimenters without programming knowledge to take advantage of the functionalities of the R environment coupled to the Shiny framework. The software entitled TumGrowth (see Fig. 1  for an overview) allows the user to upload data generated in conventional spreadsheet software, to verify the overall experimental design and data integrity, to visualize the entire experiments or selected groups alone or en masse, and then to perform three alternative, yet complementary statistical analyses, depending on three types of questions: 1. Does a treatment affect the trajectory of tumor growth? For this, longitudinal analysis can be performed either on the entire tumor growth curve or on segments thereof, and the effects of the overall experimental intervention (over all group) can be analyzed statistically in the same way as groups can be subjected to pairwise comparisons.
2. Does a treatment affect tumor size at a given time point chosen by the experimenter? For this, the visualization program facilitates the choice of the time point, and the software package yields statistical comparisons across and between groups. 3. Does a variation in experimental conditions impact on the appearance (or disappearance) of tumors, the overall survival of animals or their survival up to a critical maximum tumor size? TumGrowth application not only allows for exporting new generated graphs, for the inclusion in laboratory notebooks and scientific articles, but also it generates a full set of Materials and Methods (by automatically creating statistical reports that comprise experimental design, modeling settings, description of the statistical tests used for the calculation of p-value as well as diagnostic plots) to explain how each graph and each statistical comparison has been calculated.
TumGrowth is freely available online at https://kroemerlab. shinyapps. io/TumGrowth/ (utilization limited to 25 hours per month) and can be downloaded into any computer to be used offline as well (without limitations to its use). As a free interactive web tool, TumGrowth should profit to technicians, students, post-docs and established investigators working in the area of experimental oncology. Examples of its recent use include articles in Cancer Cell, 2 Immunity, 3 OncoImmunology 4,5 and Science. 6, 7 e anticipate that its use will improve the statistical analyses of experimental tumor growth curves in an ever-expanding number of laboratories. In principle, this statistical package might also be used to analyze growth curves of mice subjected to obesogenic diets.
YARP is an open-source robotics middleware, specifically designed to be modular, non-invasive, and flexible. It promotes software re-usability by means of abstract interfaces and modular software paradigms, and it allows to distribute computational tasks across a system by offering multi-platform network communication primitives  (Fitzpatrick et al. , 2014). YARP development is historically correlated to the iCub robot  (Metta et al. , 2010; Natale et al. , 2016) , a child-sized humanoid platform for the study of cognitive robotics. In these years, the iCub community focused its attention on topics such as human-robot interaction, visual attention, machine learning, object manipulation, and grasping. Balancing a bipedal walking robot like iCub is a problem that has been addressed only recently by some research groups  (Hu et al.
, 2016; Nava et al. , 2016). This is the reason why a standard navigation interface was missing in YARP so far. On the other side, ROS, an Ubuntu-based middleware developed around the PR2 wheeled robot, addressed the problem of making a mobile platform to navigate into a 2D environment from the very beginning  (Quigley et al. , 2009; Cousins, 2010). Over the past years, the ROS navigation stack has grown in comprehensiveness, wrapping or including bindings to basically all state-of-the-art algorithms and third-party libraries  (Marder-Eppstein et al. , 2010). This paper has two goals. First, to provide the YARP community a way to re-use the massive amount of code that has been developed within the ROS community. Second, Yarp is a multiplatform framework which can run on Windows, Linux and MacOs, while ROS is currently limited to Ubuntu-based systems.