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We demonstrate how to evaluate stepwise hedge automata (Shas) with subhedge projection while completely projecting irrelevant subhedges. Since this requires passing finite state information top-down, we introduce the notion of downward stepwise hedge automata. We use them to define in-memory and streaming evaluators with complete subhedge projection for Shas. We then tune the evaluators so that they can decide on membership at the earliest time point. We apply our algorithms to the problem of answering regular XPath queries on Xml streams. Our experiments show that complete subhedge projection of Shas can indeed speed up earliest query answering on Xml streams so that it becomes competitive with the best existing streaming tools for XPath queries. [ABSTRACT FROM AUTHOR]

The Extensible Markup Language (XML) files, widely used for storing and exchanging information on the web require efficient parsingmechanisms to improve the performance of the applications. With the existing Document Object Model (DOM) based parsing, the performance degrades due to sequential processing and large memory requirements, thereby requiring an efficient XML parser to mitigate these issues. In this paper, we propose a Parallel XML Tree Generator (PXTG) algorithm for accelerating the parsing of XML files and a Regression-based XML Parsing Framework (RXPF) that analyzes and predicts performance through profiling, regression, and code generation for efficient parsing. The PXTG algorithm is based on dividing the XML file into n parts and producing n trees in parallel. The profiling phase of the RXPF framework produces a dataset by measuring the performance of various parsing models including StAX, SAX, DOM, JDOM, and PXTG on different cores by using multiple file sizes. The regression phase produces the prediction model, based on which the final code for efficient parsing of XML files is produced through the code generation phase. The RXPF framework has shown a significant improvement in performance varying from 9.54% to 32.34% over other existing models used for parsing XML files. [ABSTRACT FROM AUTHOR]

We demonstrate how to evaluate stepwise hedge automata (Shas) with subhedge projection while completely projecting irrelevant subhedges. Since this requires passing finite state information top-down, we introduce the notion of downward stepwise hedge automata. We use them to define in-memory and streaming evaluators with complete subhedge projection for Shas. We then tune the evaluators so that they can decide on membership at the earliest time point. We apply our algorithms to the problem of answering regular XPath queries on Xml streams. Our experiments show that complete subhedge projection of Shas can indeed speed up earliest query answering on Xml streams so that it becomes competitive with the best existing streaming tools for XPath queries.

The data produced by various services should be stored and managed in an appropriate format for gaining valuable knowledge conveniently. This leads to the emergence of various data models, including relational, semi-structured, and graph models, and so on. Considering the fact that the mature relational databases established on relational data models are still predominant in today’s market, it has fueled interest in storing and processing semi-structured data and graph data in relational databases so that mature and powerful relational databases’ capabilities can all be applied to these various data. In this survey, we review existing methods on mapping semi-structured data and graph data into relational tables, analyze their major features, and give a detailed classification of those methods. We also summarize the merits and demerits of each method, introduce open research challenges, and present future research directions. With this comprehensive investigation of existing methods and open problems, we hope this survey can motivate new mapping approaches through drawing lessons from each model’s mapping strategies, as well as a new research topic - mapping multi-model data into relational tables. [ABSTRACT FROM AUTHOR]

Lithium–ion batteries play a crucial role in clean transportation systems including EVs, aircraft, and electric micromobilities. The design of battery cells and their production process are as important as their characterisation, monitoring, and control techniques for improved energy delivery and sustainability of the industry. In recent decades, the data-driven approaches for addressing all mentioned aspects have developed massively with promising outcomes, especially through artificial intelligence and machine learning. This paper addresses the latest developments in explainable machine learning known as XML and its application to lithium–ion batteries. It includes a critical review of the XML in the manufacturing and production phase, and then later, when the battery is in use, for its state estimation and control. The former focuses on the XML for optimising the battery structure, characteristics, and manufacturing processes, while the latter considers the monitoring aspect related to the states of health, charge, and energy. This paper, through a comprehensive review of theoretical aspects of available techniques and discussing various case studies, is an attempt to inform the stack-holders of the area about the state-of-the-art XML methods and encourage those to move from the ML to XML in transition to a NetZero future. This work has also highlighted the research gaps and potential future research directions for the battery community. [ABSTRACT FROM AUTHOR]

The current scene of Digital Scholarly Editions, particularly those concerning modern manuscripts, registers a trend towards documentary and diplomatic editions. This “bédierian” tendence leads to editions focused mainly on the diplomatic transcription of the source, lacking in consideration about the genetic process. Lying under this situation we could figure out the existence of digital tools and languages more adequate to this documentary approach. In addition to that, this article is aimed at showing the concrete problems in representing the diachronic elaboration of the text in a digital form, through methodological considerations and examples of realisation. Despite the widespread trend and the difficulty in choosing a different way, it seems possible to reflect on genetic digital editions from an optimistic perspective. In fact, these editions are not only necessary, at least for certain textual situations, but also feasible, as shown by some ongoing projects and experiments (Saba 2021, VaSto 2022 and Il Conte di Carmagnola), presented at the end of the contribution.

This study implemented the measurement results and administrative information obtained from the hole plate into the Digital Calibration Certificate (DCC). The DCC comprises three parts: Norms and Standards, Hierarchical Structure, and XML as Exchange Format. DCCs play a significant role in the field of metrology and statistics by ensuring data interoperability, correctness, and traceability during the conversion and transmission process. The hole plate is a length standard used for two-dimensional geometric error measurements. We evaluated the accuracy of the high-precision coordinate measuring machine (CMM) in measuring a hole plate and compared the measurement error results obtained from the hole plate with those of the laser interferometer, autocollimator, and angle square. The results show that the maximum difference in linear error is −0.30 μm, the maximum difference in angle error is −0.78″, and the maximum difference in squareness error is 4.54″. The XML is designed for machine-readability and is modeled and edited using the XMLSpy 2022 software, which is based on information published by PTB. The administrative management and measurement results tasks are presented in PDF format, which is designed for human-readability and ease of use. Overall, we implemented the measurement results and information obtained from the hole plate into the DCC. [ABSTRACT FROM AUTHOR]

This paper presents how we enhanced the accessibility and utility of historical linguistic data in the project Bullinger Digital. The project involved the transformation of 3,100 letters, primarily available as scanned PDFs, into a dynamic, fully digital format. The expanded digital collection now includes 12,000 letters, 3,100 edited, 5,400 transcribed, and 3,500 represented through detailed metadata and results from handwritten text recognition. Central to our discussion is the innovative workflow developed for this multilingual corpus. This includes strategies for text normalisation, machine translation, and handwritten text recognition, particularly focusing on the challenges of code-switching within historical documents. The resulting digital platform features an advanced search system, offering users various filtering options such as correspondent names, time periods, languages, and locations. It also incorporates fuzzy and exact search capabilities, with the ability to focus searches within specific text parts, like summaries or footnotes. Beyond detailing the technical process, this paper underscores the project’s contribution to historical research and digital humanities. While the Bullinger Digital platform serves as a model for similar projects, the corpus behind it demonstrates the vast potential for data reuse in historical linguistics. The project exemplifies how digital humanities methodologies can revitalise historical text collections, offering researchers access to and interaction with historical data. This paper aims to provide readers with a comprehensive understanding of our project’s scope and broader implications for the field of digital humanities, highlighting the transformative potential of such digital endeavours in historical linguistic research.

Many efforts have been deployed by the IR community to extend freetext query processing toward semi-structured XML search. Most methods rely on the concept of Lowest Comment Ancestor (LCA) between two or multiple structural nodes to identify the most specific XML elements containing query keywords posted by the user. Yet, few of the existing approaches consider XML semantics, and the methods that process semantics generally rely on computationally expensive word sense disambiguation (WSD) techniques, or apply semantic analysis in one stage only: performing query relaxation/refinement over the bag of words retrieval model, to reduce processing time. In this paper, we describe a new approach for XML keyword search aiming to solve the limitations mentioned above. Our solution first transforms the XML document collection (offline) and the keyword query (on-the-fly) into meaningful semantic representations using context-based and global disambiguation methods, specially designed to allow almost linear computation efficiency. We use a semantic-aware inverted index to allow semantic-aware search, result selection, and result ranking functionality. The semantically augmented XML data tree is processed for structural node clustering, based on semantic query concepts (i.e., key-concepts), in order to identify and rank candidate answer sub-trees containing related occurrences of query key-concepts. Dedicated weighting functions and various search algorithms have been developed for that purpose and will be presented here. Experimental results highlight the quality and potential of our approach. [ABSTRACT FROM AUTHOR]

Collaborative spectrum access requires wireless devices to perform spectrum-related tasks (such as sensing) on request from other nodes. Thus, while joining the network, they need to inform neighboring devices and/or the central coordinator of their capabilities. During the operational phase, nodes may request other permissions from the the controller, like the opportunity to transmit according to the current policies and spectrum availability. To achieve such coordinated behavior, all associated devices within the network need a language for describing radio capabilities, requests, scenarios, policies, and spectrum availability. In this paper, we present a thorough comparison of the use of two candidate languages—Web Ontology Language (OWL) and eXtensible Markup Language (XML)—for such purposes. Towards this goal, we propose an evaluation method for automating quantitative comparisons with metrics such as precision, recall, device registration, and the query response time. The requests are expressed in both SPARQL Protocol and RDF Query Language (SPARQL) and XML Query Language (XQuery), whereas the device capabilities are expressed in both OWL and XML. The evaluation results demonstrate the advantages of using OWL semantics to improve the quality of matching results over XML. We also discuss how the evaluation method can be applicable to other scenarios where knowledge, datasets, and queries require richer expressiveness and semantics. [ABSTRACT FROM AUTHOR]

Lithium–ion batteries play a crucial role in clean transportation systems including EVs, aircraft, and electric micromobilities. The design of battery cells and their production process are as important as their characterisation, monitoring, and control techniques for improved energy delivery and sustainability of the industry. In recent decades, the data-driven approaches for addressing all mentioned aspects have developed massively with promising outcomes, especially through artificial intelligence and machine learning. This paper addresses the latest developments in explainable machine learning known as XML and its application to lithium–ion batteries. It includes a critical review of the XML in the manufacturing and production phase, and then later, when the battery is in use, for its state estimation and control. The former focuses on the XML for optimising the battery structure, characteristics, and manufacturing processes, while the latter considers the monitoring aspect related to the states of health, charge, and energy. This paper, through a comprehensive review of theoretical aspects of available techniques and discussing various case studies, is an attempt to inform the stack-holders of the area about the state-of-the-art XML methods and encourage those to move from the ML to XML in transition to a NetZero future. This work has also highlighted the research gaps and potential future research directions for the battery community.

With the advent of artificial intelligence and big data era, the application scenarios of smart transportation are becoming more and more extensive, and the transportation industry is also undergoing intelligent upgrades. According to the flexible customization requirements of Intelligent Traffic Cloud Platform business function, this paper presents a flexible customization of User Identity, which has been applied to a specific business system. Intelligent Traffic Cloud Platform as a new information processing infrastructure model and business model has been widely recognized. The model-driven strategy based on user identity uses the C# delegation mechanism. The storage of business function customization designs the driving strategy of flexible customization model and realizes the flexible customization of platform. This paper uses Load Runner v8.1, an industry-standard load-testing tool that predicts system behavior and performance, to predict system behavior and optimize system performance. Finally, the feasibility and effectiveness of the proposed scheme is verified by a specific service function on the Intelligent Traffic Cloud Platform.

Although multi-temporal XML databases supporting schema versioning are used in several domains, like e-commerce, e-health, and e-government, existing database management systems and XML tools do not provide any support for managing (inserting, updating, and deleting) temporal XML data or temporal XML schema versioning. Besides, whereas much research work has focused in the last decade on schema versioning in temporal XML databases, any attention has been devoted to manipulating data in such databases. To fill this theoretical and practical gap, we propose in this paper a generic approach, named TempoX (Temporal XML), for data manipulation in multi-temporal and multi-schema-version XML databases. Indeed, we (i) define a new multi-temporal XML data model supporting temporal schema versioning, named TempoXDM (Temporal XML Data Model), (ii) introduce the principles on which our approach is based, and (iii) provide the specifications of the basic data manipulation operations: “insert”, “replace”, “evolve”, and “delete”. Moreover, to show the feasibility of TempoX, we use it to propose a temporal XML update language, named TempoXUF (Temporal XQuery Update Facility), as an extension of the W3C XQuery Update Facility language to temporal and versioning aspects. Furthermore, to validate our language proposal, we develop a system prototype, named TempoXUF-Manager, that supports TempoXUF.

To improve XML query processing, it is necessary to label XML documents efficiently for the indexing process because it allows the structural relationships between the XML nodes to be preserved without having to access the original document. However, XML data on the Web is updated as time passes, which means that the dynamic updating of XML data is an issue that may need to be handled by a XML labeling scheme specifically designed for dynamic updates. Previous XML labeling schemes have limitations when updates take place. For example, a lot of node labels need to be relabeled, a lot of duplicate labels occur during this relabeling process, and the size and time costs of the updated labels are high. Therefore, this paper proposes an efficient prefix-based labeling scheme that uses a hexagonal pattern. The proposed labeling scheme has three main advantages: (i) it avoids the need for node relabeling when XML documents are updated at random locations, (ii) it avoids duplicated labels by creating a new label for every inserted node, and (iii) it reduces the size and time costs of the updated labels. The proposed scheme is evaluated against the three most recent prefix-based labeling schemes in terms of the size and time costs of the updated labels. In addition, the ability of the proposed labeling scheme to handle several updates (such as insertions) in XML documents is also evaluated. The evaluations show that the proposed labeling scheme outperforms previously developed prefix-based labeling schemes in terms of both size and time costs, particularly for large-scale XML datasets, resulting in improved query processing performance. Moreover, the proposed scheme efficiently supports frequent updates at arbitrary positions. The paper concludes with several suggestions for further research.

For over 50 years, the International HLA and Immunogenetics Workshops (IHIW) have advanced the fields of histocompatibility and immunogenetics (H&I) via community sharing of technology, experience and reagents, and the establishment of ongoing collaborative projects. Held in the fall of 2017, the 17th IHIW focused on the application of next generation sequencing (NGS) technologies for clinical and research goals in the H&I fields. NGS technologies have the potential to allow dramatic insights and advances in these fields, but the scope and sheer quantity of data associated with NGS raise challenges for their analysis, collection, exchange and storage. The 17th IHIW adopted a centralized approach to these issues, and we developed the tools, services and systems to create an effective system for capturing and managing these NGS data. We worked with NGS platform and software developers to define a set of distinct but equivalent NGS typing reports that record NGS data in a uniform fashion. The 17th IHIW database applied our standards, tools and services to collect, validate and store those structured, multi-platform data in an automated fashion. We have created community resources to enable exploration of the vast store of curated sequence and allele-name data in the IPD-IMGT/HLA Database, with the goal of creating a long-term community resource that integrates these curated data with new NGS sequence and polymorphism data, for advanced analyses and applications.

Despite the flexibility that a TEI-based workflow offers for preparing editions based on collating several manuscripts, textual scholars working with XML-TEI edition files face a lack of domain-specific auxiliary tools. The TEI-CAT partially fills this gap by providing interfaces for visualizing the transcription of different manuscripts and for exploring the encoded variations and detecting common mistakes. While not properly supporting several types of textual variation and editorial intervention, these tools satisfy most of the needs usually arising during transcription and collation. The image annotation tool complements them by allowing users to generate TEI-compliant code. This tool very flexibly describes or comments on facsimile layout and content and thus is particularly helpful in preparing digital editions. Finally, the PDF-via-LaTeX exporting facility extends the scope of the toolbox towards traditional paper-based publishing, which often remains an editor’s ultimate goal. However, this tool requires adopting specific encoding practices, which are not made explicit and may not match the user’s choices; its output needs manual modifications in some cases. Together, these tools partially illustrate how the XML-TEI format can be used to prepare critical editions. They constitute an important step towards creating a TEI-based digital environment in this field.

The paper summarises many years of discussions and experience of biodiversity publishers, organisations, research projects and individual researchers, and proposes recommendations for implementation of persistent identifiers for article metadata, structural elements (sections, subsections, figures, tables, references, supplementary materials and others) and data specific to biodiversity (taxonomic treatments, treatment citations, taxon names, material citations, gene sequences, specimens, scientific collections) in taxonomy and biodiversity publishing. The paper proposes best practices on how identifiers should be used in the different cases and on how they can be minted, cited, and expressed in the backend article XML to facilitate conversion to and further re-use of the article content as FAIR data. The paper also discusses several specific routes for post-publication re-use of semantically enhanced content through large biodiversity data aggregators such as the Global Biodiversity Information Facility (GBIF), the International Nucleotide Sequence Database Collaboration (INSDC) and others, and proposes specifications of both identifiers and XML tags to be used for that purpose. A summary table provides an account and overview of the recommendations. The guidelines are supported with examples from the existing publishing practices. [ABSTRACT FROM AUTHOR]

The reachability problem on directed graphs, asking whether two vertices are connected via a directed path, is an elementary problem that has been well-studied. In this paper, we study a variation of the elementary reachability problem, called the sink-reachability problem, which can be found in many applications such as static program analysis, social network analysis, large scale web graph analysis, XML document link path analysis, and the study of gene regulation relationships. To scale sink-reachablity analysis to large graphs, we develop a highly scalable sink-reachability preserving graph reduction strategy for input sink graphs, by using a composition framework. That is, individual sink-reachability preserving condensation operators, each running in linear time, are pipelined together to produce graph reduction algorithms that result in close to maximum reduction, while keeping the computation efficient. Experiments on large real-world sink graphs demonstrate the efficiency and effectiveness of our compositional approach to sink-reachability preserving graph reduction with a reduction rate of up to 99.74 percent for vertices and a rate of up to 99.46 percent for edges. [ABSTRACT FROM AUTHOR]

Epilepsy is a brain disorder that results in seizures; in general seizure is a suddenly occurring uncontrollable electrical disturbance in the brain. These disturbances in the brain can lead to changes in behaviour, feelings, movements, etc. It is highly essential for the patients suffering with epilepsy to be diagnosed and treated. The normal detection of epilepsy is done using EEG signals which are time consuming. This paper aims at proposing a methodology to diagnose epilepsy by the use of EEG signals by establishing a correlation between statistical calculations and EEG signals. A various set of features are applied to the epilepsy and non-epilepsy dataset. Features such as time domain frequency which include mean, skewness, variance, kurtosis, standard deviation, approximate entropy, zero crossings, power spectrum and frequency domain features that include signal energy and total signal area, average DWT coefficient, signal relation features and human brain graph features. Further, considering these features, feature fusion and optimization aka FFO is carried out which helps in analysing the features in an optimal way for further classification. Moreover, feature fusion and its optimization helps in exploring the new features that helps in enhancing the distinguish between classes. These features help diagnosis of the brain disorder in a very time efficient manner with higher accuracy. In this paper, we propose a feature fusion methodology for the most efficient working of the system. [ABSTRACT FROM AUTHOR]

EnzymeML is an XML‐based data exchange format that supports the comprehensive documentation of enzymatic data by describing reaction conditions, time courses of substrate and product concentrations, the kinetic model, and the estimated kinetic constants. EnzymeML is based on the Systems Biology Markup Language, which was extended by implementing the STRENDA Guidelines. An EnzymeML document serves as a container to transfer data between experimental platforms, modeling tools, and databases. EnzymeML supports the scientific community by introducing a standardized data exchange format to make enzymatic data findable, accessible, interoperable, and reusable according to the FAIR data principles. An application programming interface in Python supports the integration of software tools for data acquisition, data analysis, and publication. The feasibility of a seamless data flow using EnzymeML is demonstrated by creating an EnzymeML document from a structured spreadsheet or from a STRENDA DB database entry, by kinetic modeling using the modeling platform COPASI, and by uploading to the enzymatic reaction kinetics database SABIO‐RK. [ABSTRACT FROM AUTHOR]

The Industry 4.0 (I4.0) is a paradigm settled down by the introduction of the Internet of things (IoT) into the production and manufacturing environment. I4.0 promotes the connection of physical items such as sensors, devices, and enterprise assets, to each other and to the Internet. The information that flows through these items is vital because it serves to make relevant decisions. One of the main features of I4.0 is its adaptability to the human needs, this means that the items included in the I4.0 network are heterogeneous and they are large in number. The majority of I4.0 papers, which are focused on testing, describe a specific system or part of the I4.0 network. We have not found any paper that undertakes the testing of multiple connected IoT devices that will receive, process, and make decisions according to the complex and real data that travel through the network. In this article, we present IoT-TEG (Test Event Generator) 4.0, which is based on the test event generator system IoT-TEG. IoT-TEG 4.0 provides two new main contributions: the generation of test cases, which can include all the different types of data that the connected I4.0 devices under study can manage, and real-time testing. Additionally, its validation using real IoT programs is included and the results show that IoT-TEG 4.0 allows us to conduct tests that mimic real IoT system behaviors. [ABSTRACT FROM AUTHOR]

The article presents the basic principles of web services. The comparison with other types of web services is carried out. Analyzed the advantages and disadvantages of different solutions. One of the options for software implementation that allows data transfer between the SAP ERP system and the local systems of the main production units using web services is considered. The result of this work was the implementation and adaptation of a new method of data exchange between systems, the possibility of using received classes and functional modules for the quick creation of following-up web services.

Replicated tree data structures are a fundamental building block of distributed filesystems, such as Google Drive and Dropbox, and collaborative applications with a JSON or XML data model. These systems need to support a move operation that allows a subtree to be moved to a new location within the tree. However, such a move operation is difficult to implement correctly if different replicas can concurrently perform arbitrary move operations, and we demonstrate bugs in Google Drive and Dropbox that arise with concurrent moves. In this article we present a CRDT algorithm that handles arbitrary concurrent modifications on trees, while ensuring that the tree structure remains valid (in particular, no cycles are introduced), and guaranteeing that all replicas converge towards the same consistent state. Our algorithm requires no synchronous coordination between replicas, making it highly available in the face of network partitions. We formally prove the correctness of our algorithm using the Isabelle/HOL proof assistant, and evaluate the performance of our formally verified implementation in a geo-replicated setting. [ABSTRACT FROM AUTHOR]

This article analyzes ways of calculating characteristics of data and most common data structure types that allow comparison between them or on a time axis. To achieve this, it studies the key aspects of relational databases, XML, JSON and RDF structure types. These data structure types are compared to multiple isolated approaches to data quality and other data characteristics measurements. The goals of the article are the calculation method itself and a storage structure for calculated values. The article presents a method of characterization of data and data structure types based on the calculation of three metrics: the amount of structuredness, the amount of hierarchicallity and the amount of information. This triad of metrics allows comparison between various data sets (objects), for example evaluating the complexity of the transformation of data from one data object to another, as well as with data structure types (as mentioned above). Based on the vector of three metrics, the calculation method of the compatibility between data and data structure type is proposed. This method can help select the most compatible data format for existing data. The calculated values of metrics can also detect non-optimal storage design and classify data transformations. The method was evaluated on an example case study, which showed its usability on an example demonstration data set. It can be used in the process of data modelling to help select optimal data structure type, to design a data transformation process and to optimize existing data storages.

In this article, we show that there exists a graph G with O(n) nodes such that any forest of n nodes is an induced subgraph of G. Furthermore, for constant arboricity k, the result implies the existence of a graph with O(nk) nodes that contains all n-node graphs of arboricity k as node-induced subgraphs, matching a Ω(nk) lower bound of Alstrup and Rauhe. Our upper bounds are obtained through a log2 n + O(1) labeling scheme for adjacency queries in forests. We hereby solve an open problem being raised repeatedly over decades by authors such as Kannan et al., Chung, and Fraigniaud and Korman. [ABSTRACT FROM AUTHOR]

This study implemented the measurement results and administrative information obtained from the hole plate into the Digital Calibration Certificate (DCC). The DCC comprises three parts: Norms and Standards, Hierarchical Structure, and XML as Exchange Format. DCCs play a significant role in the field of metrology and statistics by ensuring data interoperability, correctness, and traceability during the conversion and transmission process. The hole plate is a length standard used for two-dimensional geometric error measurements. We evaluated the accuracy of the high-precision coordinate measuring machine (CMM) in measuring a hole plate and compared the measurement error results obtained from the hole plate with those of the laser interferometer, autocollimator, and angle square. The results show that the maximum difference in linear error is −0.30 μm, the maximum difference in angle error is −0.78″, and the maximum difference in squareness error is 4.54″. The XML is designed for machine-readability and is modeled and edited using the XMLSpy 2022 software, which is based on information published by PTB. The administrative management and measurement results tasks are presented in PDF format, which is designed for human-readability and ease of use. Overall, we implemented the measurement results and information obtained from the hole plate into the DCC.

Due to the myriad of loads that are collected into commercial grid-interactive efficient buildings (GEBs) focused on the industry 4.0 paradigm, it is important to ensure their proper electrical operation. The power quality (PQ) here requires a granular monitoring approach, reaching a point where each device connected to the microgrid can diagnose whether its power supply is optimal. Otherwise, it can participate cooperatively in decision-making to avoid anomalies or faults in the microgrid. In this work, we present cloud-based extended functionality to make smart appliances responsive to the grid, either autonomously or managed under the open automated demand response standard. Further to acting as a switch, the main strength lies in its PQ monitoring via the FIWARE Internet of Things (IoT) platform with data-driven analytics capabilities. It identifies and even predicts a broad spectrum of electrical disturbances, far exceeding the capabilities of previous solutions such as the grid-friendly appliance controller, so it is possible to customize a battery of alarms at will (e.g., according to IEEE 1547 standard). Moreover, although it can act autonomously, its main mission will be to act in a coordinated manner, either cooperatively or under the supervision of the GEB Energy Management System. Finally, different case studies are presented to show their capabilities. With the integration of these distributed submetering systems, under standards IoT wireless communication protocols, a further step will be taken in the advent of the digital utility paradigm. [ABSTRACT FROM AUTHOR]

With the development of high-speed wireless LAN, the use of multimedia-type digital contents is increasing significantly even in the smart home environment. Therefore, it is very important to efficiently process and manage the use of digital content in the form of multimedia. In order to effectively process multimedia digital contents in the wireless LAN environment of a smart home, a server higher than the PC level is required. There is also a need for a support system that can exchange multimedia digital contents between clients of home appliances. Therefore, in this paper, we propose a Smart Home Digital Contents Management and Operation System (SDCMOS) that effectively processes and supports multimedia digital contents. This system is designed to effectively process real-time multimedia processing in home service in a wireless LAN environment. In addition, it was made possible to efficiently search multimedia information directly from the home server. SDCMOS is designed to be usable in fields such as biomedical image search, history museums, art exhibition halls, tourism information, geographic information, and e-commerce. [ABSTRACT FROM AUTHOR]

In this study, we examined machine extraction of polymer data from tables in scientific articles. The extraction system consists of five processes: table extraction, data formatting, polymer name recognition, property specifier identification, and data extraction. Tables were first extracted in plain text. XML versions of scientific articles were used, and the tabular forms were accurately extracted, even for complicated tables, such as multi-column, multi-row, and merged tables. Polymer name recognition was performed using a named entity recognizer created by deep neural network learning of polymer names. The preparation cost of the training data was reduced using a rule-based algorithm. The target polymer properties in this study were glass transition temperature (Tg), melting temperature (Tm), and decomposition temperature (Td), and the specifiers were identified using partial string matching. Through these five processes, 2,181 data points for Tg, 1,526 for Tm, and 2,316 for Td were extracted from approximately 18,000 scientific articles published by Elsevier. Nearly half of them were extracted from complicated tables. The F-scores for the extraction were 0.871, 0.870, and 0.841, respectively. These results indicate that the extraction system created in this study can rapidly and accurately collect large amounts of polymer data from tables in scientific literature.

The purpose of this paper is to analyze various dimensions for measurement of human behavior. Human behaviour is complex. Behaviors, emotions, cognitions, and attitudes can rarely be described in terms of one or two variables. It is multimodal in nature. Furthermore, the traits, modalities and dimensions cannot be measured directly, but must be inferred from constructs which in turn are measured by multiple factors or variables. I have emphasized on the use of baseline data for each subject as the degree of expressiveness for same situation which varies for each subject and needs to be measured based on the individual trait of the subject. This can be done by making baseline data for subjects being researched. Subsequently, discussion has been done on data analysis. Finally, framework for the same has been proposed. Basically, the researcher asks two questions, “Do I have anything important?” (Which is based upon the researcher’s observations of some aspect of human behavior adequately addresses the observation) “If so, what do I have?” (What is the best explanation of the relationship between the variables?)

Objetive. To carry out the sustainability analysis of the Latin American initiative Redalyc.org, based on two fundamental aspects, the economic and the need for its existence. Method. the measurement of the man-hours required to publish a scientific article is carried out, using Marcalyc. 40 articles published in scientific journals from different areas of knowledge, belonging to Brazil, Chile, Colombia, Mexico and Venezuela, are processed using this tool. Results. it is obtained that the markup takes on average 86.3% (SD = 4.2%) of the total processing time, distributed in almost equal parts between the content of the article (body) (average = 48.3%, SD = 9.5%) and references, notes, acknowledgement, appendix and glossary (back) (mean = 43.2%, SD = 9.3%). Conclusions. The sustainability of Redalyc's publication model is more economically guaranteed for two reasons; 1) the processing time to generate XML-JATS is much shorter than SciELO, and 2) it uses a markup tool (Marcalyc), which can be used directly by the editorial team of a journal, without the need for specialized technical knowledge. This favors the sustainability of the scientific publication model in Latin American open access and returns the control of the publication process to the editors.

The topic of this paper is to show the process of the encoding of fol. 25v of Codex Runicus, AM 28 8vo. More specifically, this manuscript has been chosen as a pilot project since it has been written entirely in runes and it is thus eminently suited for testing the XML encoding of a runic manuscript. This has to the best of my knowledge not been done before. To this aim I used the Oxygen XML Editor to produce the encoding of the text in compliance with the Menota (Medieval Nordic Text Archive) Guidelines of Handbook v. 3.0, a detailed manual of how to encode medieval manuscripts in XML, and the last version P5 of the TEI standard. The paper aims at presenting the Codex Runicus and its runes, and then showing the process of encoding the runes, the punctuation marks and of the linguistic analysis (lexical and grammatical) of the selected text. This pilot project is the basis of my current PhD research project, which aims at encoding the whole manuscript on the basis of the procedure shown in this paper.

In common with many technical aspects of digital humanities, the TEI has a reputation for being difficult to teach and difficult to learn, with potential practitioners put off by the large and (at first sight) intimidating set of guidelines, the seemingly complex hierarchical structure and the profusion of angle brackets. One-to-one or small group teaching in the context of a specific project is often the preferred method, where the short but steep learning curve required to engage with the TEI can be addressed in a way which is relevant to the aims and experience of the learner. This, however, is not a particularly efficient way of teaching. In this article, the authors discuss their experience of teaching (and learning) the TEI, and how lessons learned in contexts relating to specific projects might feed into the teaching of TEI in a more general setting – the Digital Humanities at Oxford Summer School being the prime example.

BackgroundThe genes that produce antibodies and the immune receptors expressed on lymphocytes are not germline encoded; rather, they are somatically generated in each developing lymphocyte by a process called V(D)J recombination, which assembles specific, independent gene segments into mature composite genes. The full set of composite genes in an individual at a single point in time is referred to as the immune repertoire. V(D)J recombination is the distinguishing feature of adaptive immunity and enables effective immune responses against an essentially infinite array of antigens. Characterization of immune repertoires is critical in both basic research and clinical contexts. Recent technological advances in repertoire profiling via high-throughput sequencing have resulted in an explosion of research activity in the field. This has been accompanied by a proliferation of software tools for analysis of repertoire sequencing data. Despite the widespread use of immune repertoire profiling and analysis software, there is currently no standardized format for output files from V(D)J analysis. Researchers utilize software such as IgBLAST and IMGT/High V-QUEST to perform V(D)J analysis and infer the structure of germline rearrangements. However, each of these software tools produces results in a different file format, and can annotate the same result using different labels. These differences make it challenging for users to perform additional downstream analyses.ResultsTo help address this problem, we propose a standardized file format for representing V(D)J analysis results. The proposed format, VDJML, provides a common standardized format for different V(D)J analysis applications to facilitate downstream processing of the results in an application-agnostic manner. The VDJML file format specification is accompanied by a support library, written in C++ and Python, for reading and writing the VDJML file format.ConclusionsThe VDJML suite will allow users to streamline their V(D)J analysis and facilitate the sharing of scientific knowledge within the community. The VDJML suite and documentation are available from https://vdjserver.org/vdjml/ . We welcome participation from the community in developing the file format standard, as well as code contributions.

Although multi-temporal XML databases supporting schema versioning are used in several domains, like e-commerce, e-health, and e-government, existing database management systems and XML tools do not provide any support for managing (inserting, updating, and deleting) temporal XML data or temporal XML schema versioning. Besides, whereas much research work has focused in the last decade on schema versioning in temporal XML databases, any attention has been devoted to manipulating data in such databases. To fill this theoretical and practical gap, we propose in this paper a generic approach, named TempoX (Temporal XML), for data manipulation in multi-temporal and multi-schema-version XML databases. Indeed, we (i) define a new multi-temporal XML data model supporting temporal schema versioning, named TempoXDM (Temporal XML Data Model), (ii) introduce the principles on which our approach is based, and (iii) provide the specifications of the basic data manipulation operations: "insert", "replace", "evolve", and "delete". Moreover, to show the feasibility of TempoX, we use it to propose a temporal XML update language, named TempoXUF (Temporal XQuery Update Facility), as an extension of the W3C XQuery Update Facility language to temporal and versioning aspects. Furthermore, to validate our language proposal, we develop a system prototype, named TempoXUF-Manager, that supports TempoXUF. [ABSTRACT FROM AUTHOR]

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In common with many technical aspects of digital humanities, the TEI has a reputation for being difficult to teach and difficult to learn, with potential practitioners put off by the large and (at first sight) intimidating set of guidelines, the seemingly complex hierarchical structure and the profusion of angle brackets. One-to-one or small group teaching in the context of a specific project is often the preferred method, where the short but steep learning curve required to engage with the TEI can be addressed in a way which is relevant to the aims and experience of the learner. This, however, is not a particularly efficient way of teaching. In this article, the authors discuss their experience of teaching (and learning) the TEI, and how lessons learned in contexts relating to specific projects might feed into the teaching of TEI in a more general setting – the Digital Humanities at Oxford Summer School being the prime example. [ABSTRACT FROM AUTHOR]

In this study, we examined machine extraction of polymer data from tables in scientific articles. The extraction system consists of five processes: table extraction, data formatting, polymer name recognition, property specifier identification, and data extraction. Tables were first extracted in plain text. XML versions of scientific articles were used, and the tabular forms were accurately extracted, even for complicated tables, such as multi-column, multi-row, and merged tables. Polymer name recognition was performed using a named entity recognizer created by deep neural network learning of polymer names. The preparation cost of the training data was reduced using a rule-based algorithm. The target polymer properties in this study were glass transition temperature (Tg), melting temperature (Tm), and decomposition temperature (Td), and the specifiers were identified using partial string matching. Through these five processes, 2,181 data points for Tg, 1,526 for Tm, and 2,316 for Td were extracted from approximately 18,000 scientific articles published by Elsevier. Nearly half of them were extracted from complicated tables. The F-scores for the extraction were 0.871, 0.870, and 0.841, respectively. These results indicate that the extraction system created in this study can rapidly and accurately collect large amounts of polymer data from tables in scientific literature. [ABSTRACT FROM AUTHOR]