Decentralized and self-organizing mechanisms present promising approaches to deal with the massive amount of data generated by different components in the smart grid. In this work we present a self-organized approach that is able to take decisions locally and in a distributed way. Our approach distinguishes between an infrastructure level and a decision level. The middleware processes running on the infrastructure level, which provides services such as routing, data filtering and aggregation. The decision level defines an explicitly and declaratively represented dynamic meta model that provides the semantics for the infrastructure level processes, which support interoperability. Additionally, this level runs processes that design, supervise and control agents on the infrastructure level. The levels coupled together through feedback loop that ensures that relevant changes on the infrastructure level are reflected in the decision level and vice versa. The corresponding reflection principles provide the basis for the implementation of the self-organizing mechanisms that govern the overall system. We distinguish two complementary categories of quality metrics for evaluating self-organizing systems. The first category explores the performance of the algorithm with respect to the target application such as delay, path length and success rate. In the context of self-organizing systems, it is important to evaluate additional kind of metrics that do not depend necessarily on the application rather than on self-organizing characteristics of the algorithm. In this paper we focus on the second type, and particularly on self-organizing properties of the algorithms.

Self-Organizing Smart Grid Services

Grid and Cloud computing technologies are being applied as an affordable method to cluster computational power together. These structures aim to support service applications by grouping devices and shared resources in one large computational unit. However, the management complexity grows proportionally to the number of resources being integrated. The paper claims to address the problems of management, considering the routing problem in a particular context. An experimental assessment of routing for grid and cloud is presented. In addition, it introduces a proof-of- concept implementation and case study scenarios.

/pdf/experimental-assessment-of-routing-for-grid-and-cloud-2exny3e2hn.pdf

Experimental Assessment of Routing for Grid and Cloud

Automation of emerging smart distribution grids is required to operate the grid efficiently and swiftly. This study draws a vision on grid automation with agent-based cyber-physical system integration to provide a truly distributed architecture. Furthermore, this study introduces a notion of self-organising smart distribution grid that promotes the grid capability to heal and organise itself in the best-suited topology without the intervention of a central operator. The proposed architecture comprises the system of bus agents (BAs) that emulate the given grid. This emulation is used by BAs to comprehend the grid conditions, switch location and compute their representative bus voltage and partial loss and to estimate the best organisation of the BAs as well as the grid. This study details the behavioural designing of BA that incorporates the functioning above. The proposed architecture also uses an event trigger approach to initiate grid organisation, which is showcased by case studies on IEEE 33 bus system. The results showcase the efficiency of the concept regarding solution accuracy with distributed computations; computational efficiency during contingencies; architecture performance under communication latency; and fault-tolerant characteristics of the proposed architecture.

Distributed architecture for self-organising smart distribution systems

The electronic healthcare in the modern society has the possibility of converting the practice of delivery of health care. Currently, chaos of information is characterizing the public health care, which leads to inferior decision-making, increasing expenses and even loss of lives. Technological progress in the sensors, integrated circuits, and the wireless communications have allowed designing low cost, microscopic, light, and smart sensors. These smart sensors are able to feel, transport one or more vital signals, and they can be incorporated in wireless personal or body networks for remote health monitoring. Sensor networks promise to drive innovation in health care allowing cheap, continuous, mobile and personalized health management of electronic health records with the Internet. The e-health applications imply an exciting set of requirements for Grid middleware and provide a rigorous testing ground for Grid. In the chapter, the authors present an overview of the current technological achievements in the electronic healthcare world combined with an outline of the quality dimensions in healthcare. Anastasia N. Kastania Biomedical Research Foundation of the Academy of Athens, Greece & Athens University of Economics and Business, Greece Sophia Kossida Biomedical Research Foundation of the Academy of Athens, Greece Quality Issues in Personalized E-Health, Mobile Health and E-Health Grids DOI: 10.4018/978-1-60960-561-2.ch108

Quality Issues in Personalized E-Health, Mobile Health and E-Health Grids

While analyzing scanned documents, handwritten text can overlap with printed text. This overlap causes difficulties during the optical character recognition (OCR) and digitization process of documents, and subsequently, hurts downstream NLP tasks. Prior research either focuses solely on the binary classification of handwritten text or performs a three-class segmentation of the document, i.e., recognition of handwritten, printed, and background pixels. This approach results in the assignment of overlapping handwritten and printed pixels to only one of the classes, and thus, they are not accounted for in the other class. Thus, in this research, we develop novel approaches to address the challenges of handwritten and printed text segmentation. Our objective is to recover text from different classes in their entirety, especially enhancing the segmentation performance on overlapping sections. To support this task, we introduce a new dataset, SignaTR6K, collected from real legal documents, as well as a new model architecture for the handwritten and printed text segmentation task. Our best configuration outperforms prior work on two different datasets by 17.9% and 7.3% on IoU scores. The SignaTR6K dataset is accessible for download via the following link: https://forms.office.com/r/2a5RDg7cAY. 

Handwritten and Printed Text Segmentation: A Signature Case Study

Historical archival records present many challenges for OCR systems to correctly encode their content, due to visual complexity, e.g. mixed printed text and handwritten annotations, paper degradation and faded ink. This paper addresses the problem of automatic identification and separation of handwritten and printed text in historical archival documents, including the creation of an artificial pixel-level annotated dataset and the presentation of a new FCN-based model trained on historical data. Initial test results indicate 18% IoU performance improvement on recognition of printed pixels and 10% IoU performance improvement on recognition of handwritten pixels in synthesised data when compared to the state-of-the-art trained on modern documents. Furthermore, an extrinsic OCR-based evaluation on the printed layer extracted from real historical documents shows 26% performance increase.

/pdf/handwritten-and-printed-text-identification-in-historical-n59wa76g.pdf

Handwritten and Printed Text Identification in Historical Archival Documents

Handling highly dynamic scenarios as they arise in emergency situations requires lots of semantic information about the situation and an extremely flexible, selforganizing IT infrastructure that provides services that can be used to manage the situation. We show that a distributed meta level architecture is particularly suited for the implementation of such a self-organizing grid of services. This architecture (SOGOS) distinguishes between an object level and a meta level. The middleware processes of the grid are running on the object level. The meta level defines an explicitly and declaratively represented dynamic meta model that provides the semantics for the object level processes. Additionally, this level runs processes that plan, supervise and control mobile agents on the object level. The levels are linked together by reflection processes that ensure that relevant changes on the object level are reflected in the meta model and vice versa. The corresponding reflection principles provide the basis for the implementation of the selforganizing mechanisms that govern the overall system.

/pdf/sogos-a-distributed-meta-level-architecture-for-the-self-3502bxbcbp.pdf

SOGOS - A Distributed Meta Level Architecture for the Self-Organizing Grid of Services

. This paper describes our experience in combining a large corpus of material that was classified manually over decades of Art Historical and Book History research using the ICONCLASS 4 subject classification system with heuristics based on a current state-of-the-art neural network (CLIP from OpenAI), leveraging visual similarity to provide suggestions for automated classification of cultural heritage content. The effectiveness of the approach is demonstrated through an evaluation of the underlying extreme multi-label classification problem.

The Art Historian’s Bicycle Becomes an E-Bike

Terminology sources, such as controlled vocabularies, thesauri and classification systems, play a key role in digitizing cultural heritage. However, Information Retrieval (IR) systems that allow to query and explore these lexical resources often lack an adequate representation of the semantics behind the user's search, which can be conveyed through multiple expression modalities (e.g., images, keywords or textual descriptions). This paper presents the implementation of a new search engine for one of the most widely used iconography classification system, Iconclass. The novelty of this system is the use of a pre-trained vision-language model, namely CLIP, to retrieve and explore Iconclass concepts using visual or textual queries.

https://export.arxiv.org/pdf/2306.16529v1.pdf

Multimodal Search on Iconclass using Vision-Language Pre-Trained Models

Knowledge Extraction (KE) techniques are used to convert unstructured information present in texts to Knowledge Graphs (KGs) which can be queried and explored. Despite their potential for cultural heritage domains, such as Art History, these techniques often encounter limitations if applied to domain-specific data. In this paper we present the main challenges that KE has to face on art-historical texts, by using as case study Giorgio Vasari’s The Lives of The Artists . This paper discusses the following NLP tasks for art-historical texts, namely entity recognition and linking, coreference resolution, time extraction, motif extraction and artwork extraction. Several strategies to annotate art-historical data for these tasks and evaluate NLP models are also proposed.

Knowledge Extraction for Art History: the Case of Vasari's The Lives of The Artists

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