A Unifying Framework Design for the Management of Autonomic Network Functions

2018 ◽  
pp. 45-89
Author(s):  
Laurent Ciavaglia ◽  
Pierre Peloso
Keyword(s):  
Dynamic Systems ◽  
Relevant Information ◽  
Full Potential ◽  
Framework Design ◽  
Recent Developments ◽  
Cognitive Techniques ◽  
Network Functions ◽  
Knowledge Function ◽  
And Performance ◽  
Coordination Function

The increased use of software-driven and virtualization techniques enables more versatile network infrastructures. Realizing the full potential of such large and dynamic systems requires advanced automation and adaptation capabilities. In this chapter, the authors review recent development of so-called self-driving networks combining cognitive techniques and autonomic behaviors. In particular, the authors provide insights on a set of core mechanisms for the operation of self-driving networks: (1) a governance function to help operators deploy, pilot, control, and track run-time behaviors and performance of self-driving functions; (2) a coordination function to ensure stability and performance when several self-driving functions are running together; (3) a knowledge function to share relevant information to empowering their actions; and (4) common workflows, lifecycles, and APIs to enable deployment and interoperability of autonomic functions. The analysis connects with reference work in scientific literature and the most recent developments in standards (e.g., IETF/IRTF and ETSI).

Intelligent Processing of Loosely Structured Documents as a Strategy for Organizing Electronic Health Care Records

1993 ◽  
Vol 32 (04) ◽  
pp. 265-268 ◽  
Author(s):  
D. J. Essin
Keyword(s):  
Health Care ◽  
Health Care Organization ◽  
Database Systems ◽  
Relevant Information ◽  
Full Potential ◽  
Care Organization ◽  
Intelligent Processing ◽  
Data Interchange ◽  
Structured Documents ◽  
Health Care Records

AbstractLoosely structured documents can capture more relevant information about medical events than is possible using today’s popular databases. In order to realize the full potential of this increased information content, techniques will be required that go beyond the static mapping of stored data into a single, rigid data model. Through intelligent processing, loosely structured documents can become a rich source of detailed data about actual events that can support the wide variety of applications needed to run a health-care organization, document medical care or conduct research. Abstraction and indirection are the means by which dynamic data models and intelligent processing are introduced into database systems. A system designed around loosely structured documents can evolve gracefully while preserving the integrity of the stored data. The ability to identify and locate the information contained within documents offers new opportunities to exchange data that can replace more rigid standards of data interchange.

scholarly journals Mineral Physicochemistry Underlying Feature-Based Extraction of Mineral Abundance and Composition from Shortwave, Mid and Thermal Infrared Reflectance Spectra

Minerals ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 347
Author(s):  
Carsten Laukamp ◽  
Andrew Rodger ◽  
Monica LeGras ◽  
Heta Lampinen ◽  
Ian C. Lau ◽  
...  
Keyword(s):  
Mineral Exploration ◽  
Relevant Information ◽  
Ore Deposits ◽  
Reflectance Spectra ◽  
Full Potential ◽  
Infrared Reflectance ◽  
Vibrational Modes ◽  
Diagnostic Features ◽  
Wide Range ◽  
Mineral Assemblages

Reflectance spectroscopy allows cost-effective and rapid mineral characterisation, addressing mineral exploration and mining challenges. Shortwave (SWIR), mid (MIR) and thermal (TIR) infrared reflectance spectra are collected in a wide range of environments and scales, with instrumentation ranging from spaceborne, airborne, field and drill core sensors to IR microscopy. However, interpretation of reflectance spectra is, due to the abundance of potential vibrational modes in mineral assemblages, non-trivial and requires a thorough understanding of the potential factors contributing to the reflectance spectra. In order to close the gap between understanding mineral-diagnostic absorption features and efficient interpretation of reflectance spectra, an up-to-date overview of major vibrational modes of rock-forming minerals in the SWIR, MIR and TIR is provided. A series of scripts are proposed that allow the extraction of the relative intensity or wavelength position of single absorption and other mineral-diagnostic features. Binary discrimination diagrams can assist in rapidly evaluating mineral assemblages, and relative abundance and chemical composition of key vector minerals, in hydrothermal ore deposits. The aim of this contribution is to make geologically relevant information more easily extractable from reflectance spectra, enabling the mineral resources and geoscience communities to realise the full potential of hyperspectral sensing technologies.

Relationships between Learning Styles and Performance on Problem-Solving Tasks

1986 ◽  
Vol 59 (3) ◽  
pp. 1135-1138 ◽  
Author(s):  
Penny Armstrong ◽  
Ernest McDaniel
Keyword(s):  
College Students ◽  
Problem Solving ◽  
Learning Styles ◽  
Learning Style ◽  
Choice Point ◽  
Relevant Information ◽  
And Performance ◽  
Problem Solving Task

A computerized problem-solving task was employed to study the relationships among problem-solving behaviors and learning styles. College students made choices to find their way home in a simulated “lost in the woods” task and wrote their. reasons at each choice point. Time to read relevant information and time to make decisions were measured by the computer clock. These variables were correlated with learning style variables from Schmeck's (1977) questionnaire. The findings indicated that subjects who perceived themselves as competent learners take more time on the problem-solving task, use more information and make fewer wrong choices.

CAN BAYESIAN METHODS MAKE DATA AND ANALYSES MORE RELEVANT TO DECISION MAKERS?

2001 ◽  
Vol 17 (1) ◽  
pp. 114-122 ◽  
Author(s):  
Steven H. Sheingold
Keyword(s):  
Decision Making ◽  
Bayesian Methods ◽  
Paradigm Shift ◽  
Relevant Information ◽  
Decision Makers ◽  
Evidence Based ◽  
Critical Importance ◽  
Bayesian Approaches ◽  
Bayesian Analyses ◽  
And Performance

Decision making in health care has become increasingly reliant on information technology, evidence-based processes, and performance measurement. It is therefore a time at which it is of critical importance to make data and analyses more relevant to decision makers. Those who support Bayesian approaches contend that their analyses provide more relevant information for decision making than do classical or “frequentist” methods, and that a paradigm shift to the former is long overdue. While formal Bayesian analyses may eventually play an important role in decision making, there are several obstacles to overcome if these methods are to gain acceptance in an environment dominated by frequentist approaches. Supporters of Bayesian statistics must find more accommodating approaches to making their case, especially in finding ways to make these methods more transparent and accessible. Moreover, they must better understand the decision-making environment they hope to influence. This paper discusses these issues and provides some suggestions for overcoming some of these barriers to greater acceptance.

scholarly journals A Comprehensive Survey of Knowledge Graph-Based Recommender Systems: Technologies, Development, and Contributions

Information ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 232
Author(s):  
Janneth Chicaiza ◽  
Priscila Valdiviezo-Diaz
Keyword(s):  
Recommender Systems ◽  
Survey Result ◽  
Knowledge Graph ◽  
Specific Recommendation ◽  
Future Directions ◽  
Recent Developments ◽  
Comprehensive Survey ◽  
System 2 ◽  
And Performance ◽  
Use Of Knowledge

In recent years, the use of recommender systems has become popular on the web. To improve recommendation performance, usage, and scalability, the research has evolved by producing several generations of recommender systems. There is much literature about it, although most proposals focus on traditional methods’ theories and applications. Recently, knowledge graph-based recommendations have attracted attention in academia and the industry because they can alleviate information sparsity and performance problems. We found only two studies that analyze the recommendation system’s role over graphs, but they focus on specific recommendation methods. This survey attempts to cover a broader analysis from a set of selected papers. In summary, the contributions of this paper are as follows: (1) we explore traditional and more recent developments of filtering methods for a recommender system, (2) we identify and analyze proposals related to knowledge graph-based recommender systems, (3) we present the most relevant contributions using an application domain, and (4) we outline future directions of research in the domain of recommender systems. As the main survey result, we found that the use of knowledge graphs for recommendations is an efficient way to leverage and connect a user’s and an item’s knowledge, thus providing more precise results for users.

scholarly journals FDAAA TrialsTracker: A live informatics tool to monitor compliance with FDA requirements to report clinical trial results

2018 ◽  
Author(s):  
Nicholas J. DeVito ◽  
Seb Bacon ◽  
Ben Goldacre
Keyword(s):  
Clinical Trials ◽  
Relevant Information ◽  
Reporting Requirements ◽  
Practical Support ◽  
The Us ◽  
Us Government ◽  
Results Reporting ◽  
And Performance ◽  
Legal Frameworks ◽  
Clinical Trial Results

AbstractIntroductionNon-publication of clinical trials results is an ongoing issue. In 2016 the US government updated the results reporting requirements to ClinicalTrials.gov for trials covered under the FDA Amendments Act 2007. We set out to develop and deliver an online tool which publicly monitors compliance with these reporting requirements, facilitates open public audit, and promotes accountability.MethodsWe conducted a review of the relevant legislation to extract the requirements on reporting results. Specific areas of the statutes were operationalized in code based on the results of our policy review, publicly available data from ClinicalTrials.gov, and communications with ClinicalTrials.gov staff. We developed methods to identify trials required to report results, using publicly available registry data; to incorporate additional relevant information such as key dates and trial sponsors; and to determine when each trial became due. This data was then used to construct a live tracking website.ResultsThere were a number of administrative and technical hurdles to successful operationalization of our tracker. Decisions and assumptions related to overcoming these issues are detailed along with clarifications directly from ClinicalTrials.gov. The FDAAA TrialsTracker was successfully launched in February 2018 and provides users with an overview of results reporting compliance.DiscussionClinical trials continue to go unreported despite numerous guidelines, commitments, and legal frameworks intended to address this issue. In the absence of formal sanctions from the FDA and others, we argue tools such as ours - providing live data on trial reporting - can improve accountability and performance. In addition, our service helps sponsors identify their own individual trials that have not yet reported results: we therefore offer positive practical support for sponsors who wish to ensure that all their completed trials have reported.

The main trends in the Russian tourism and hospitality market from the point of view of Russian travel agencies

2018 ◽  
Vol 10 (4) ◽  
pp. 412-420 ◽  
Author(s):  
Aleksandr Gudkov ◽  
Elena Dedkova ◽  
Kristina Dudina
Keyword(s):  
Relevant Information ◽  
Tourism Industry ◽  
Point Of View ◽  
Content Type ◽  
Qualitative Research Methodology ◽  
Recent Developments ◽  
Survey Results ◽  
The Subject ◽  
Tourism And Hospitality ◽  
The Impact

PurposeThis paper aims to discuss recent developments in the Russian tourism industry and the main reasons for new initiatives in local destination development.Design/methodology/approachThe study is based on qualitative research methodology. A summary of key literature is presented alongside the analysis of the survey results.FindingsThis paper sheds light on the challenges and changes that took place in the Russian tourism business between 2014 and 2017. The subject is poorly covered in academic literature. The basic data for analysis presented in official statistics are scarce. Therefore a more effective way of obtaining relevant information was to conduct a survey using a semi-structured questionnaire, with tourism business actors as respondents.Research limitations/implicationsThis paper provides mostly conceptual analysis based on limited empirical data; directions for further empirical research are proposed in the conclusion.Originality/valueThe paper reveals something of the impact of economic and geopolitical factors, both negative and positive ones, on the restructuring of the Russian tourism market and the emergence of promising opportunities for the development of new domestic destinations. As a result, tourism market actors are able to become more diverse.

Percussion Theatre: a body in between

2018 ◽  
Author(s):  
◽  
Jennifer Torrence
Keyword(s):  
Contemporary Music ◽  
Music Performance ◽  
The Body ◽  
John Cage ◽  
Artistic Practice ◽  
Research Project ◽  
Art Forms ◽  
Recent Developments ◽  
And Performance

What does the musician become when sound and instrumental thinking are no longer privileged as the foundation of a musician's practice? In what ways does an emphasis on the musician's body cause music to approach art forms such as theatre and performance? After a generation of pioneering work from Mauricio Kagel, Dieter Schnebel, John Cage and many others, where is the theatrical and the performative in music today? How do its recent developments shape, alter, constitute a musician's artistic practice? Through her research, Jennifer Torrence argues that this type of music demands the musician assume a different understanding and relation to their instrument and therefore a different relation to their body. This relation calls for new ways of making and doing (new artistic practices) that foreground the body as a fundamental performance material. Through an emphasis on the body, the musician emerges as a performer. This exposition is a reflection on the research project Percussion Theatre: a body in between. This project is comprised of a collection of new evening-length works that approach the theatrical and performative in contemporary music performance. These works are created with and by composers Wojtek Blecharz, Carolyn Chen, Neo Hülcker, Johan Jutterström, Trond Reinholdtsen, François Sarhan, and Peter Swendsen. The exposition contains reflections on recent developments in contemporary music that mark a mutation of the executing musician into a co-creating performer, as well as images, artefacts, videos, and texts that unfold the process of creating and performing the work that constitutes this project. The ambition of this exposition is that through the exposure of a personal artistic practice an image of a larger field may come into focus.

Evaluating the learning and performance characteristics of self-organizing systems with different task features

2021 ◽  
pp. 1-19
Author(s):  
Hao Ji ◽  
Yan Jin
Keyword(s):  
Systematic Evaluation ◽  
Full Potential ◽  
Learning Capability ◽  
Reward Function ◽  
Reward Functions ◽  
Good Learning ◽  
And Performance ◽  
Generation Problem ◽  
Self Organizing

Abstract Self-organizing systems (SOS) are developed to perform complex tasks in unforeseen situations with adaptability. Predefining rules for self-organizing agents can be challenging, especially in tasks with high complexity and changing environments. Our previous work has introduced a multiagent reinforcement learning (RL) model as a design approach to solving the rule generation problem of SOS. A deep multiagent RL algorithm was devised to train agents to acquire the task and self-organizing knowledge. However, the simulation was based on one specific task environment. Sensitivity of SOS to reward functions and systematic evaluation of SOS designed with multiagent RL remain an issue. In this paper, we introduced a rotation reward function to regulate agent behaviors during training and tested different weights of such reward on SOS performance in two case studies: box-pushing and T-shape assembly. Additionally, we proposed three metrics to evaluate the SOS: learning stability, quality of learned knowledge, and scalability. Results show that depending on the type of tasks; designers may choose appropriate weights of rotation reward to obtain the full potential of agents’ learning capability. Good learning stability and quality of knowledge can be achieved with an optimal range of team sizes. Scaling up to larger team sizes has better performance than scaling downwards.

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