A challenge for historical research: Making data FAIR using a collaborative ontology management environment (OntoME)

Semantic Web ◽  
2020 ◽  
pp. 1-16
Author(s):  
Francesco Beretta
Keyword(s):  
Semantic Analysis ◽  
Historical Research ◽  
Data Modelling ◽  
Fine Grained ◽  
Fair Principles ◽  
Active Research ◽  
New Research ◽  
High Level ◽  
Conceptual Data ◽  
Ontology Management

This paper addresses the issue of interoperability of data generated by historical research and heritage institutions in order to make them re-usable for new research agendas according to the FAIR principles. After introducing the symogih.org project’s ontology, it proposes a description of the essential aspects of the process of historical knowledge production. It then develops an epistemological and semantic analysis of conceptual data modelling applied to factual historical information, based on the foundational ontologies Constructive Descriptions and Situations and DOLCE, and discusses the reasons for adopting the CIDOC CRM as a core ontology for the field of historical research, but extending it with some relevant, missing high-level classes. Finally, it shows how collaborative data modelling carried out in the ontology management environment OntoME makes it possible to elaborate a communal fine-grained and adaptive ontology of the domain, provided an active research community engages in this process. With this in mind, the Data for history consortium was founded in 2017 and promotes the adoption of a shared conceptualization in the field of historical research.

scholarly journals Deep Learning for Anomaly Detection

2021 ◽  
Vol 54 (2) ◽  
pp. 1-38
Author(s):  
Guansong Pang ◽  
Chunhua Shen ◽  
Longbing Cao ◽  
Anton Van Den Hengel
Keyword(s):  
Deep Learning ◽  
Anomaly Detection ◽  
Novelty Detection ◽  
Research Area ◽  
Unique Problem ◽  
Fine Grained ◽  
Advantages And Disadvantages ◽  
Active Research ◽  
High Level ◽  
Critical Direction

Anomaly detection, a.k.a. outlier detection or novelty detection, has been a lasting yet active research area in various research communities for several decades. There are still some unique problem complexities and challenges that require advanced approaches. In recent years, deep learning enabled anomaly detection, i.e., deep anomaly detection , has emerged as a critical direction. This article surveys the research of deep anomaly detection with a comprehensive taxonomy, covering advancements in 3 high-level categories and 11 fine-grained categories of the methods. We review their key intuitions, objective functions, underlying assumptions, advantages, and disadvantages and discuss how they address the aforementioned challenges. We further discuss a set of possible future opportunities and new perspectives on addressing the challenges.

Addiction and schizophrenia

2000 ◽  
Vol 15 (2) ◽  
pp. 115-122 ◽  
Author(s):  
P. Batel
Keyword(s):  
Epidemiologic Studies ◽  
Self Medication ◽  
Addictive Disorders ◽  
Co Morbidity ◽  
Dependency Type ◽  
Active Research ◽  
Social Environmental ◽  
High Level ◽  
Environmental Hypothesis ◽  
High Degree

SummaryEpidemiologic studies in the general population and those based on the clinical assessment of schizophrenic populations have revealed a high degree of overlap between schizophrenia and addictive disorders. The abuse of psychoactive substances (including alcohol) throughout life is so frequent (50%) that the possibility of a specific link inevitably arises. Various hypotheses have been suggested to explain the high co-morbidity between schizophrenia and addiction: 1) The social-environmental hypothesis has been developed but studies have provided poor evidence to validate it. 2) The possible shared biological vulnerability between schizophrenia and addictions led researchers to explore common genetic determinants and study the involvement of the dopaminergic and opioid systems in the aetiology of both schizophrenia and the abuse of and dependence on psychoactive drugs. 3) Finally, the theory of self-medication suggests that schizophrenics may be attempting to counter the deficit linked to their disorders by using the substances they take or their dependency-type behaviour to cope with their emotional problems. The clinical profile of schizophrenic addicts does seem to display some distinctive features, such as the high level of depressive co-morbidity, very high nicotine and alcohol dependence, with a very poor prognosis. These patients are difficult to manage; the possibility of pharmacologic interactions between the substances they are taking and neuroleptic medication calls for prudence, and their compliance is also poor. Addictive disorders in schizophrenics are currently a topic of active research intended to lead to identifying specific treatments. The early identification of addictive disorders in schizophrenics should make it possible to limit their development and improve the prognosis.

One-IPC high-level simulation of microthreaded many-core architectures

2016 ◽  
Vol 31 (2) ◽  
pp. 152-162 ◽  
Author(s):  
Irfan Uddin
Keyword(s):  
Design Space Exploration ◽  
Instruction Set ◽  
Efficient Design ◽  
Simulation Framework ◽  
Fine Grained ◽  
Detailed Simulation ◽  
High Level ◽  
Many Core ◽  
The Cost ◽  
Multiple Clusters

The microthreaded many-core architecture is comprised of multiple clusters of fine-grained multi-threaded cores. The management of concurrency is supported in the instruction set architecture of the cores and the computational work in application is asynchronously delegated to different clusters of cores, where the cluster is allocated dynamically. Computer architects are always interested in analyzing the complex interaction amongst the dynamically allocated resources. Generally a detailed simulation with a cycle-accurate simulation of the execution time is used. However, the cycle-accurate simulator for the microthreaded architecture executes at the rate of 100,000 instructions per second, divided over the number of simulated cores. This means that the evaluation of a complex application executing on a contemporary multi-core machine can be very slow. To perform efficient design space exploration we present a co-simulation environment, where the detailed execution of instructions in the pipeline of microthreaded cores and the interactions amongst the hardware components are abstracted. We present the evaluation of the high-level simulation framework against the cycle-accurate simulation framework. The results show that the high-level simulator is faster and less complicated than the cycle-accurate simulator but with the cost of losing accuracy.

Constructing the Limits of State Violence in Central America: Towards a New Research Agenda

1996 ◽  
Vol 28 (2) ◽  
pp. 435-459 ◽  
Author(s):  
Robert H. Holden
Keyword(s):  
Political Culture ◽  
Central America ◽  
State Violence ◽  
Military Power ◽  
Independent Research ◽  
Civil Military Relations ◽  
Us Military ◽  
New Research ◽  
Military Relations ◽  
High Level

AbstractThis analysis of the historically high level of state-sponsored violence in Central America, typically explained in terms of ‘authoritarianism’ or ‘civil-military relations’, argues for according it a more independent research status. Three historic dimensions of state-sponsored violence – the mechanisms by which caudillo violence was displaced upward in the late 19th century, the level of subaltern collaboration with the agents of state violence as a function of clientelist politics, and the intrusion of US military power after 1940 – are proposed. The implications for the utility of political culture theory and for a reevaluation of the literature on civil-military relations are developed.

Stories as Technology: Past, Present, and Future

2021 ◽  
Author(s):  
Roger’s Bacon ◽  
Sergey Samsonau ◽  
Dario Krpan ◽  
◽  
Keyword(s):  
Social Cognitive ◽  
Fine Grained ◽  
Good Story ◽  
High Level ◽  
Future Technologies

What is it about a good story that causes it to have life-changing effects on one person and not another? I wonder if future technologies will enable us to develop the type of truly deep and fine-grained understanding of stories as social, cognitive, and emotional technologies that might allow us to answer this question with a high-level of precision.

scholarly journals Entity Type Recognition for Heterogeneous Semantic Graphs

AI Magazine ◽  
2015 ◽  
Vol 36 (1) ◽  
pp. 75-86 ◽  
Author(s):  
Jennifer Sleeman ◽  
Tim Finin ◽  
Anupam Joshi
Keyword(s):  
Machine Learning ◽  
Background Knowledge ◽  
Knowledge Bases ◽  
Heterogeneous Data ◽  
Unstructured Data ◽  
Supervised Machine Learning ◽  
Coreference Resolution ◽  
Multiple Sources ◽  
Fine Grained ◽  
High Level

We describe an approach for identifying fine-grained entity types in heterogeneous data graphs that is effective for unstructured data or when the underlying ontologies or semantic schemas are unknown. Identifying fine-grained entity types, rather than a few high-level types, supports coreference resolution in heterogeneous graphs by reducing the number of possible coreference relations that must be considered. Big data problems that involve integrating data from multiple sources can benefit from our approach when the datas ontologies are unknown, inaccessible or semantically trivial. For such cases, we use supervised machine learning to map entity attributes and relations to a known set of attributes and relations from appropriate background knowledge bases to predict instance entity types. We evaluated this approach in experiments on data from DBpedia, Freebase, and Arnetminer using DBpedia as the background knowledge base.

Improvement of Superplasticity in Fine-Grained Oxide Ceramics Based on the Concept of Grain Boundary Plasticity

2016 ◽  
Vol 838-839 ◽  
pp. 34-40
Author(s):  
Hidehiro Yoshida ◽  
Koji Morita ◽  
Byung Nam Kim ◽  
Koji Matsui ◽  
Yuichi Ikuhara ◽  
...  
Keyword(s):  
Grain Boundary ◽  
Atomic Structure ◽  
Nucleation And Growth ◽  
Phenomenological Analysis ◽  
Research Field ◽  
Oxide Ceramics ◽  
Cavity Nucleation ◽  
Fine Grained ◽  
Experimental Strain ◽  
New Research

Superplasticity in fine-grained oxide ceramics has been generally elucidated on the basis of their experimental strain rate-flow stress relationship and phenomenological analysis of cavity nucleation and growth. It has been widely accepted that the high temperature superplastic flow and failure in ceramics is significantly influenced by the atomic structure and chemistry of grain boundaries. Such phenomenon cannot be explained based on the classical phenomenological analysis. Our research group has therefore proposed to establish a new research field, grain boundary plasticity, to describe the superplastic deformation related to the grain boundary atomic structure. This paper aims to point out the importance of the atomistic analysis of grain boundary to develop new superplastic ceramics.

A user‐oriented interface for generalised informetric analysis based on applying advanced data modelling techniques

2000 ◽  
Vol 56 (3) ◽  
pp. 250-278 ◽  
Author(s):  
Kalervo Järvelin ◽  
Peter Ingwersen ◽  
Timo Niemi
Keyword(s):  
Ad Hoc ◽  
Impact Factors ◽  
Data Modelling ◽  
Complex Objects ◽  
Bibliographic Data ◽  
Modelling Techniques ◽  
General Data ◽  
Advanced Analysis ◽  
Data Restructuring ◽  
High Level

This article presents a novel user‐oriented interface for generalised informetric analysis and demonstrates how informetric calculations can easily and declaratively be specified through advanced data modelling techniques. The interface is declarative and at a high level. Therefore it is easy to use, flexible and extensible. It enables end users to perform basic informetric ad hoc calculations easily and often with much less effort than in contemporary online retrieval systems. It also provides several fruitful generalisations of typical informetric measurements like impact factors. These are based on substituting traditional foci of analysis, for instance journals, by other object types, such as authors, organisations or countries. In the interface, bibliographic data are modelled as complex objects (non‐first normal form relations) and terminological and citation networks involving transitive relationships are modelled as binary relations for deductive processing. The interface is flexible, because it makes it easy to switch focus between various object types for informetric calculations, e.g. from authors to institutions. Moreover, it is demonstrated that all informetric data can easily be broken down by criteria that foster advanced analysis, e.g. by years or content‐bearing attributes. Such modelling allows flexible data aggregation along many dimensions. These salient features emerge from the query interface‘s general data restructuring and aggregation capabilities combined with transitive processing capabilities. The features are illustrated by means of sample queries and results in the article.

Different Approaches to Economic and Social Changes: New Research Issues, Sources and Results

2022 ◽  
Keyword(s):  
Social History ◽  
Working Group ◽  
Hungarian Academy ◽  
Research Network ◽  
Research Issues ◽  
History Of ◽  
Regional Committee ◽  
New Research ◽  
High Level ◽  
Academy Of Sciences

This series was launched in 2021 by the Working Group of Economic and Social History of the Pécs Regional Committee of the Hungarian Academy of Sciences to present research conducted within its framework. The foreign language edition is meant to be a contribution to the internationalization of research made in Hungary. The Working Group has made every effort since the publication of the first two volumes to allow its members, and also their Ph.D. students, to publish their findings more easily and in larger volume, providing at the same time an opportunity for other professionals in the region of South Transdanubia to publish their researches. The majority of the studies in this book, similarly to the first volume of the series, are about the history of the region, but some of the papers go beyond this theme. The diversity of the papers created an inspiring environment for the authors, which in turn has greatly stimulated the already existing professional cooperation among them. Both the editors and the authors find it very important to popularise the economic and social history of the region as broadly as possible, in line with the ambitions of the Pécs Regional Committee of the Hungarian Academy of Sciences. In addition, this book also promotes the cooperation among generations of researchers; it is not only the young that enjoy the support of their senior colleagues but the ideas and momentum of the younger generation also keep the activity of the Working Group at a high level. It is due to the well-functioning generational discussions, among other things, that several young researchers earned their Ph.D. degree in 2021. The framework of the studies in the broader sense is the economic and social history of Hungary and Europe in the 18th – 20th centuries. The papers in this volume also provide information about the development and current phases of the different pieces of research. Several papers are sequels to publications released in 2021 from a chronological or thematic aspect, however the book contains brand new topics as well. Great significance is attributed to the fact that several renowned international members of the research network of the Working Group were also persuaded to publish. The results of some ongoing Ph.D. research are also presented. The high number of young authors is a proof that the professional interest in economic and social history is not decreasing at all. We do hope that this book will contribute to the maintenance of this trend.

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