dataesr/scientific-paragraphs-categorization huggingface dataset:
MySQL is a true multi-user, multi-threaded SQL database server. MySQL is small and exquisite database server software, which is ideal for small application system  [4]. Apache server is an open source Web server of Apache Software Foundation. Apache has a good cross-platform and can run on almost any computer platform widely used. The system was developed with today's most popular PHP and MySQL softwares. Two functions of the foreground use and background management were considered in this system. The database of this system was built scientifically, and each module function was carefully designed. Furthermore, it was a full-featured office management system, with the nice interface and simple operation, in order to satisfy the demands of all aspects of daily office management. In the era of information, office collaboration in different place and office business in the business trip were often required for the modern enterprise.
The choice of development tools was also considered carefully. At present, there are a lot of development systems based on Web technology, but the three main kinds of dynamic technology are PHP, JSP and ASP. NET, etc. [6] , each technology has its own characteristics. PHP is a completely free, and the source code can be downloaded freely. It is a development tool favored by a lot of programmers. PHP is a simple, efficient and dynamic scripting language, with the advantages of cross-platform, powerful database support  [7]. As the PHP is independent on the platform, it can run in a relatively high safety system platform  [8]. Therefore, it has more advantages for system background construction. PHP (Professional Hypertext Preprocessor) technology and MySQL database have many advantages. Therefore, they are the best combination to develop the dynamic website.
Background function design of the system The background function in the enterprise office management system included all data information management involved in foreground functions, such as documents, employees, salary, and etc. information management. In addition, the section information management and system management were included. The management to the section can include the query, add, delete and modify, etc. to section information. The management work of the system background should be done by the system administrator. The system administrator was conferred the management function of the system, therefore, his access right was bigger than that of normal users. The background management function module in the enterprise office management system was shown in Figure  3. The development and implementation of the enterprise office management system involves the analysis and design to the database and database tables, including the design of the data entities, attributes, entity relation, etc.
Two kinds of operation permissions users were designed in this system: common users and administrators. According to the permission categories, the operating range for different users was limited  [9]. System mainly involves the table structure design, the establishment of the link between tables, the establishment of the records in the table, etc. in the relational model. There were 31 table files involved in the database of the system. The structure of each table file table was carefully designed. On the premise of minimum redundancy guaranteed, links between tables were established scientifically to improve the efficiency of system running time, and take up less storage space. The table structure of employee basic information table (employee), department information table(dept), the announcement information table(notice), staff attendance sheet(sign), users of the system table(sysuser) and file information table (file) were given here, as shown in table  1  to table 6.
The other table files involved in the system will not be illustrated here. The relation among the data tables in the system database was more complex, and more table files were involved, such that the employee information management involves the operation and run of many table. Its link establishment must be accurate to reduce the redundancy of database. Only when created, stored procedure was compiled. Afterwards, recompiling was not needed whenever executing the stored procedure. Therefore, the efficiency of database access can be improved when using the stored procedure  [10]. In the PHP programm, there was a common. inc. php file, in which some constants, configuration files, path of the session, host information, database information, global function, etc. were stored to ensure important information needed for the program at runtime  [11].
In the development system, constants were defined and "$magic_quotes = get_magic_quotes_gpc ()" was used in order to increase the readability of the code and ease use. The automatic escape function was shut down, and self-defining functions escape was used. The database encoding and references to the parameters configuration page were completed by the program, therefore, the modularization of the program was realized, which is easy for maintenance and modification in the future. The development of this system also involves image processing, encoding and decoding, compressed file processing, XML parsing, identity authentication of HTTP, POP3, SNMP, database processing, network interface application, security coding mechanism, and other functions. Using PHP + MySQL technology can solve these above problems well.
API function supplied in MySQL in PHP can be used to operate the database. The database management, maintenance, and data retrieval and other various operations can be completed through the MySQL function library. Much more module designs were involved in the system development, of which each module had personalized design in the design process. Such as: the main function of the background page design in the employee information management was to achieve the management and maintenance to the enterprise staff information, therefore, ordinary users can query, but can't modify. Only admin can modify operations. Next example: file transfer management page design was to save files uploaded and downloaded to the server. The path of transferring files in the server was saved to the database for the receiver to download again.
This new generation of KRR systems includes graph-based knowledge representation formalisms such as Constraint Networks (CNs), Bayesian Networks (BNs), Semantic Networks (SNs), Conceptual Graphs (CGs), Formal Concept Analysis (FCA), CP-nets, GAI-nets, and Argumentation Frameworks, all of which have been successfully used in a number of applications. The goal of the workshop series on Graph Structures for Knowledge Representation and Reasoning (GKR) is to bring together researchers involved in the development and application of graph-based knowledge representation formalisms and reasoning techniques. This volume contains extended and revised selected papers of the sixth edition of GKR, under the auspices of ScaDS. AI -Center for Scalable Data Analytics and Artificial Intelligence Dresden/Leipzig, which took place jointly with ECAI 2020, the 24th European Conference on Artificial Intelligence, which was supposed to be held in Santiago de Compostela, Spain.
The Metamodel Diagram To illustrate the contribution of CG and FCA, Fig. 1  acts as our starting point. This figure represents the metamodel of a warehouse pick pack process of a UK manufacturer, based on the LEAD Enterprise Ontology referred to earlier (i. e. LEAD ID#-ES20001ALL)  [13]. The metamodel was created using the Enterprise Plus (E+) software (www. enterpriseplus. tools) from LEADing Practice, a notfor-profit body of LEAD industry practitioners (www. leadingpractice. com). E+ is a comprehensive repository of LEAD reference content, including its artefacts, metaobjects, and semantic relations. The semantic relations in Fig. 1  go in two directions between each metaobject. This duality is intended in many EA metamodels, including LEAD. That is because it reveals how each metaobject views itself in relation to each other directly, and indirectly through intermediate metaobjects; hence LEAD metamodels are two-way directed graphs  [9].
e. the presently pseudo-algorithm (Fig. 2 ) into one that can be computerimplemented. With help from software libraries or web services that for example allow us to identify and rephrase passive and active relationships-e. g. Grammarly (www. grammarly. com) or DeepL (www. deepl. com)-the pseudoalgorithm could be automated as real executable code. Our formal analysis of the metamodel has two main objectives. First, optimising the hands-on nature of the metamodel as a management tool: by separating the active from the passive semantics it is easier to find causes of a management issue and the levers that act upon this problem (that needs to be addressed) using the active semantics. Additionally, the passive semantics allow for identifying the effects of this management issue (and building the business case for the change).
Response Generation Architecture The proposed architecture of the ABLG system relies on three principle processes (Fig. 1 ): Preparing input for Therapeutic Expert, the Therapeutic Expert System, and the Surface Realization System. Based on the input sources, heuristic tests set the values of key variables controlling the behaviour of the Therapeutic Expert, such as patient type, clarity of the patient's chief complaint, the patient's readiness to change, their current emotional state, and their rapport with the therapist. At each conversational turn, the expert system recommends the best pragmatic move to the Surface Realisation System. This in turn chooses a feature structure template based on the pragmatic move recommended by the expert system. The template slot filler will fill in the template with relevant content, drawn from CG representation of the patient's recent utterances, or looked up from the background database.
A simple database currently provides background knowledge for our experiments. Each entry in the knowledgebase is a history list of zero or more CGs, indexed by both a patient identifier and one of the 15 background topics (Sect. 2. 3) such as suicide_attempts, willingness_to_change and chief_complaint. Entries may be added, deleted or modified during processing, so the database can be used as a working memory to update and maintain therapeutic reasoning over sessions. Initially these entries are provided manually to represent information from the pre-existing admitting interview. Psychiatric expertise is represented by a clinical Expert System Therapist, based on TMYCIN  [20]. Consultation of the system is performed at each conversational turn, informed by the current state of variables from the inputs.
KG are networks of entities, their semantic types, properties, and relationships between entities. KG are networks of all kind entities which are relevant to a specific domain or to an organization. They are not limited to abstract concepts and relations but can also contain instances of things like documents and datasets. Can be associated to Knowledge Representation in Logic such as RDF, Ontologies or Argumentation. Contribution of this work is to define the Creative Composition Problem (CCP) for Human Well-being Optimization by Construction of Knowledge Graph using Knowledge Representation and logic-based Artificial Intelligence reasoning-planning where the computation of the Optimal Solution is achieved by Dynamic Programming or Logic Programming. The Creative Composition Problem is embedded within Cecilia: an architecture of a digital companion artificial intelligence agent system composer of dialogue scripts for Well-being and Mental Health.
I. Abstract Script Dialogue Session (ASDS) is generated by MAIC in this process (Fig. 2  ). ASDS is a composition of slave agents tasks sequence to be performed by Cecilia as a single dialogue session with the student. MAIC basically consists of two modules of KB-reasoning represented and specified via ASP, the lowest one consists of a logical theory that generates -Diagnoses a set of recommendations (resources/assets)  An optimal solution for DCP instance is the intended ASDS to be proposed by Cecilia. Figure  3  is example of an abstract dialogue session built by MAIC.
Using a discrete scale between 1 and 7, where 1 means the worst behaviour, and 7 means the best behavior. The results were in average a value of 6 for each considered aspect. Seven examples, one for each student, were pre-evaluated. The Table  1  exposes the pre-evaluation results obtained from the students. "Very interesting I love it!" Figure  6  shows an example of the Cecilia GUI application. The used language in the application is Spanish, however it will be translated to an English language version. Cecilia is designed to be independent of the knowledge scripts domain, for example, the use of Enriching Talks. Also Cecilia is independent of the used human language to dialogue with the Agent User (in our case the student). Another major issue of this paper was to justify the use of ASP besides the one present in the last subsection.
It is worth to mention that Prolog-type solvers of such as XSB and CIAO  [1, 32]  can also be considered. -ASP can naturally interact with Inductive Logic Programming (ILP)  [12]. Recall that ILP  [39, 54]  is a machine learning paradigm based on logic that allows learning from cases in order to generate rules needed to reason about future similar cases. It should be clear that learning is fundamental in Cecilia with the purpose to understand better the student. For instance, it could learn that the student become sad on Sundays. -There are ASP approaches  [3, 73]  that can used to help in understanding Natural Language (NLP). Clearly NLP is major issue for Cecilia. Being able to understand written text by the students also allows the system to know her better, and hence have more enriching conversations.
-Updates and believe revision are also fundamental concepts required in our application. There are many well known proposed solutions based on ASP such as  [17, 65, 92 ]. -There is promising work related with ethical chatbots  [21]  that could allow Cecilia to become more respectful to improve its ethical interaction with the student. -ASP parallelism  [19, 74]. -Planning  [23, 44]  for example the use of Coala in CLASP  [25]. -Rapid Prototyping: Note that our generate and test code for DCP  [63] , and the solver to obtain the Optimal Solution of DCP KG described in this work is simply and directly written in ASP, that is one of the reasons why we are using ASP. Furthermore, for example, the use in sentiment analysis computed by solving the set covering [77] and minimal cut  [72]  problems.
Approach In solving this problem we used several graph theory algorithms to structure the data in a way that ensures the validity of our assumptions and assures that the data was not corrupted in a prior step. The proposed solution will analyse the subscriber's activity during a given time period, classify the subscriber into a known usage pattern and it will provide a password sharing probability. Moving forward, we will describe the capabilities and functions of the proposed solution on an example from the TV industry. The raw data, that will be inserted in the algorithm, observes the activity of real users during a month. This data was collected and provided by a client from the industry and contains the following fields: user id (as defined in the clients database), the coordinates at which an event took place, device type, device id and the time at which an event occured.
Introduction In many organizations, a vast amount of complex information is used in operations daily. This data is often stored in various databases or file systems while information can be retrieved using query languages and information retrieval techniques. During the past decade, several companies have started taking up knowledge graphs (KG)  [10] , as a way to represent heterogeneous data and make it useful for a large variety of applications  [14]. To make said data accessible, various querying languages like SPARQL and Cypher have been developed. Such querying languages allow for accessing nodes in the graph, traversing them via specific relations, or retrieve nodes that match a specific pattern. At the core of these languages lie graph patterns. These patterns can be thought of as graph shaped structures where some nodes and edges can correspond to nodes existing in the graph, while others correspond to variables (with specific variable names).
Given a query embedding q, a score for every entity in the graph can be obtained via cosine similarity: score(q, e v ) = q e v q e v. The entity and relation type embeddings, as well as any free parameters in the embedding function φ, are optimized via stochastic gradient descent on a specific loss function. Usually the loss is defined so that for a given embedding of a query, the cosine similarity is maximized with embeddings of entities that answer the query, and minimized for embeddings of entities sampled at random. The dataset used for training consists of query-answer pairs mined from the graph. Once the procedure terminates, the function φ can be used to embed a query. The entities in the graph can then be ranked as potential answers, by computing the cosine similarity of all the entity embeddings and the embedding of the query.
As mentioned before, we could create such a set by embedding the query as box, thus getting a hard boundary for separating entities in and not in the target set. Datasets. While previous work  [4, 9]  incorporated multiple datasets, our implementation has yet solely been tested on the AIFB dataset. This dataset is a knowledge graph of academic institution in which persons, organizations, projects, publications, and topics are the entities. Table  1  give some statistics of this dataset and also for two more datasets often used for the evaluation of approximate query answering. Query Generation. To train our model we have to sample for query graphs from our dataset. This is done by initially sampling anchor nodes and relations which are later used to form graphs based on specific query patterns (Fig.
6 ). After acquiring the anchor nodes and the relations connecting them, we can obtain the target set. Although this may appear straightforward, there are some caveats. The biggest one is that some queries contain considerable sets of potential target entities (over 100,000 answers). Because we sample for edges first these particular graphs actually appear often. Luckily, for most query structures this was not the case, but specifically the 2-chain and 3-chain query structures occasionally suffer from it. This is likely explained by the fact that knowledge graphs contain "hub nodes", nodes with a very high degree, to which a plethora of other nodes connect via a certain relation. Table  2  shows the average size of the target sets of sampled queries for the aforementioned datasets. One interesting thing to note is that for the AM dataset the 3-chain-inter structure actually had the largest average target set.
This could indicate that this problem is indeed very graph-dependent. Since this is a problem with the AIFB dataset, we limit the query target sets to a maximum of 100 answers. We also sample for entities not in the target set to be used as negative samples during training. For the query structures that contain an intersection we incorporate hard negative samples by finding entities that would have been in the target set if the conjunctive intersections were to be relaxed to disjunctions. Evaluation. In order to test whether the model is actually able to find answers to queries that involve edges which are not in the graph, careful preparation of our data splits was necessary. We started by our original graph and marked 10% of the edges to be removed (they are still there at this stage). Then, we sample the graph for the query patterns.
For the MLP aggregation function we applied a two layer fullyconnected MLP. As for the non-linearities in our model, we used the ReLU function. To update the parameters of the model we used Adam optimizer with a learning rate of 0. 01. Our code base is based on PyTorch. In particular, we made use of the library PyTorch Geometric  [7] , which is a PyTorch extension specialised for graph-based models. While there are potential baselines to consider  [4, 9] , they are not suitable for our work. This happens because we perform a binary classification as opposed to ranking-based methods. To our knowledge there have not been any related work that performed binary classification in the context of approximate graph querying. In the area of link prediction, we do find some work, like the early work on Neural Tensor Networks  [19]  and a more recent one which looks at triple classification  [6].
In theory, if a method can produce a good ranking, it should also be able to produce a good classification, given that the optimal thresholds for these rankings could be found. Since we do not have direct result in a manner we would have liked, we will instead analyse the trained models to see if there are relevant insights to be found. For this we looked at models using different aggregation functions, trained on the AIFB dataset. While we have no intersections between query boxes and target boxes, we could still look whether the target boxes (from T ) appear relatively close to the entity boxes, when compared to the box representations of entities in V -T. This effectively provides some measure as to whether the produced rankings are good. Table  3  shows these results. While these scores may not indicate state-of-the-art results, they do seem to suggest that the model did at least produce decent nontrivial rankings using the SUM and TM aggregators.
Acknowledgements. I would like to thank  Dr. Christian Sȃcȃrea  for his kindness, help and time invested in making this article possible. The authors were supported by  EPSRC  grant  EP/L021226/1. Martin Cooper  was supported by the grants  ANR-18-CE40-0011  and  ANR-19-PI3A-000. Stanislav Živný  was supported by a  Royal Society University Research Fellowship. This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No  714532 ). The paper reflects only the authors' views and not the views of the ERC or the  European Commission. The European Union is not liable for any use that may be made of the information contained therein.
The GLM with fixed study effect performed well for low inter-study variation and small numbers of studies, but was outperformed by the meta-analysis methods for moderate to large inter-study variability and larger numbers of studies. Conclusions: The p-value combination techniques illustrated here are a valuable tool to perform differential meta-analyses of RNA-seq data by appropriately accounting for biological and technical variability within studies as well as additional study-specific effects. An R package metaRNASeq is available on the R Forge.
In particular, such methods must be able to appropriately account for the biological and technical variability among samples within a given study as well as for the additional variability due to study-specific effects. Such inter-study variability may arise due to technical differences among studies (e. g. , sample preparation, library protocols, batch effects) as well as additional biological variability. In recent years, several methods have been proposed to analyze microarray data arising from multiple independent but related studies; these meta-analysis techniques have the advantage of increasing the available sample size by integrating related datasets, subsequently increasing the power to detect differential expression. Such meta-analyses include, for example, methods to combine pvalues  [14] , estimate and combine effect sizes  [6] , and rank genes within each study  [4] ; see  [10]  and  [9]  for a review and comparison of such methods.