scholarly journals Adding Temporal Characteristics to Geographical Schemata and Instances: A General Framework

2018 ◽  
Vol 1 ◽  
pp. 1-6
Author(s):  
Morishige Ota
Keyword(s):  
Real World ◽  
General Framework ◽  
General Feature ◽  
Geospatial Data ◽  
Feature Model ◽  
Current Application ◽  
New Model ◽  
Meta Model ◽  
Temporal Characteristics ◽  
Modeling Methods

This paper proposes the temporal general feature model (TGFM) as a meta-model for application schemata representing changes of real-world phenomena. It is not very easy to determine history directly from the current application schemata, even if the revision notes are attached to the specification. To solve this problem, the rules for description of the succession between previous and posterior components are added to the general feature model, thus resulting in TGFM. After discussing the concepts associated with the new model, simple examples of application schemata are presented as instances of TGFM. Descriptors for changing properties, the succession of changing properties in moving features, and the succession of features and associations are introduced. The modeling methods proposed in this paper will contribute to the acquisition of consistent and reliable temporal geospatial data.

Abstract 15829: Temporal Characteristics of Intra-thoracic Impedance Before and After Heart Failure Hospitalizations in a Large Real-world Population of Patients With Cardiovascular Implanted Electronic Devices

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Shantanu Sarkar ◽  
Jodi L Koehler ◽  
Eddy Warman
Keyword(s):  
Real World ◽  
Electronic Devices ◽  
Primary Diagnosis ◽  
Implantable Devices ◽  
World Population ◽  
Inclusion Criteria ◽  
Temporal Characteristics ◽  
Before And After ◽  
Intrathoracic Impedance

Introduction: Intrathoracic impedance (IMP), measured in ICD/CRTD implantable devices, is a measure of intravascular blood volume and have been shown to correlate with intracardiac pressures. We investigated the temporal characteristics of IMP before and after HF events (HFE) in a large real-world cohort of patients (pts) with ICD/CRTD devices. Methods: We linked Optum© deidentified EHR dataset during the period from 2007-2017 to the Medtronic CareLink data warehouse. Pts with ICD/CRTD implants with IMP measurements were included. HFE was defined as an inpatient, ED, or observation unit stay with primary diagnosis of HF and IV diuretics administration. Temporal average of IMP measurement across all pts in the 60 days pre and post HFE were compared for HFE with and without readmission for HF within 60 days and in pts with no HFE. Results: A total of 17,886 pts with 1.8±1.2 years of follow-up met inclusion criteria. The average age was 66.6 ±12.3 years, with 72% being males, and 51% with ICD devices. A total of 1174 pts had 1425 HFE with no readmission and 282 pts had 295 HFE which were followed by readmission. A total of 17,839 pts had no HFE over 86,858 follow-up months. The average IMP during HFE, with and without readmission, and in pts with no HFE are shown in Fig. IMP decreases over a period of time prior to HFE and recovers due to treatment during HFE. The average IMP across all patients was lower on all 60 days pre and post HFE with readmission compared to HFE with no readmission (p<0.001) and both were lower compared to follow-up period with no HFE (p<0.001). The IMP recovers less often after HF events which are followed by readmission within 60 days compared to HF events with no readmission. Conclusions: In a large real-world population of pts with ICD/CRTD devices, on an average IMP reduces prior to and recovers during HFE. IMP was lower before and after HFE with readmission compared to HFE with no readmission. Readmission is more likely in pts with smaller impedance recovery after HF events.

Online Machine Learning

2013 ◽  
pp. 27-54 ◽  
Author(s):  
Óscar Fontenla-Romero ◽  
Bertha Guijarro-Berdiñas ◽  
David Martinez-Rego ◽  
Beatriz Pérez-Sánchez ◽  
Diego Peteiro-Barral
Keyword(s):  
Machine Learning ◽  
Online Learning ◽  
Mathematical Models ◽  
General Framework ◽  
Current Application ◽  
Data Set

Machine Learning (ML) addresses the problem of adjusting those mathematical models which can accurately predict a characteristic of interest from a given phenomenon. They achieve this by extracting information from regularities contained in a data set. From its beginnings two visions have always coexisted in ML: batch and online learning. The former assumes full access to all data samples in order to adjust the model whilst the latter overcomes this limiting assumption thus expanding the applicability of ML. In this chapter, we review the general framework and methods of online learning since its inception are reviewed and its applicability in current application areas is explored.

Evaluating Conceptual Coherence in Multi-Modeling Techniques

2011 ◽  
pp. 43-62
Author(s):  
Bart-Jan Hommes
Keyword(s):  
Quantitative Evaluation ◽  
Modeling Language ◽  
Modeling Languages ◽  
Conceptual Coherence ◽  
Meta Model ◽  
Modeling Methods ◽  
Methods And Techniques ◽  
Modeling Techniques ◽  
Meta Modeling ◽  
Evaluation Of Methods

Meta-modeling is a well-known approach for capturing modeling methods and techniques. A meta-model can serve as a basis for quantitative evaluation of methods and techniques. By means of a number of formal metrics based on the meta-model, a quantitative evaluation of methods and techniques becomes possible. Existing meta-modeling languages and measurement schemes do not allow the explicit modeling of so-called multi-modeling techniques. Multi-modeling techniques are techniques that offer a coherent set of aspect modeling techniques to model different aspects of a certain phenomenon. As a consequence, existing approaches lack metrics to quantitatively assess aspects that are particular to multi-modeling techniques. In this chapter, a modeling language for modeling multi-modeling techniques is proposed as well as metrics for evaluating the coherent set of aspect modeling techniques that constitute the multi-modeling technique.

scholarly journals DDoS-Capable IoT Malwares: Comparative Analysis and Mirai Investigation

2018 ◽  
Vol 2018 ◽  
pp. 1-30 ◽  
Author(s):  
Michele De Donno ◽  
Nicola Dragoni ◽  
Alberto Giaretta ◽  
Angelo Spognardi
Keyword(s):  
Comparative Analysis ◽  
Internet Of Things ◽  
Detailed Analysis ◽  
Real World ◽  
General Framework ◽  
Denial Of Service ◽  
The Internet ◽  
Distributed Denial Of Service ◽  
Ddos Attacks ◽  
The Internet Of Things

The Internet of Things (IoT) revolution has not only carried the astonishing promise to interconnect a whole generation of traditionally “dumb” devices, but also brought to the Internet the menace of billions of badly protected and easily hackable objects. Not surprisingly, this sudden flooding of fresh and insecure devices fueled older threats, such as Distributed Denial of Service (DDoS) attacks. In this paper, we first propose an updated and comprehensive taxonomy of DDoS attacks, together with a number of examples on how this classification maps to real-world attacks. Then, we outline the current situation of DDoS-enabled malwares in IoT networks, highlighting how recent data support our concerns about the growing in popularity of these malwares. Finally, we give a detailed analysis of the general framework and the operating principles of Mirai, the most disruptive DDoS-capable IoT malware seen so far.

scholarly journals Real-Valued GCS Classifier System

2007 ◽  
Vol 17 (4) ◽  
pp. 539-547 ◽  
Author(s):  
Łukasz Cielecki ◽  
Olgierd Unold
Keyword(s):  
Evolutionary Computation ◽  
Real World ◽  
Learning Classifier Systems ◽  
Classifier Systems ◽  
Context Free Grammar ◽  
New Model ◽  
Classifier System ◽  
Learning Classifier ◽  
Context Free ◽  
Real World Problems

Real-Valued GCS Classifier SystemLearning Classifier Systems (LCSs) have gained increasing interest in the genetic and evolutionary computation literature. Many real-world problems are not conveniently expressed using the ternary representation typically used by LCSs and for such problems an interval-based representation is preferable. A new model of LCSs is introduced to classify realvalued data. The approach applies the continous-valued context-free grammar-based system GCS. In order to handle data effectively, the terminal rules were replaced by the so-called environment probing rules. The rGCS model was tested on the checkerboard problem.

scholarly journals GEODETIC REFERENCE SYSTEM TRANSFORMATIONS OF 3D ARCHIVAL GEOSPATIAL DATA USING A SINGLE SSC TERRASAR-X IMAGE

2016 ◽  
Vol XLI-B1 ◽  
pp. 1207-1212
Author(s):  
D. I. Vassilaki ◽  
A. A. Stamos
Keyword(s):  
Coordinate System ◽  
Real World ◽  
Reference System ◽  
Absolute Error ◽  
Geospatial Data ◽  
Geodetic Reference System ◽  
Coordinate Systems ◽  
3D Information ◽  
The Relationship ◽  
Geolocation Accuracy

Many older maps were created using reference coordinate systems which are no longer available, either because no information to a datum was taken in the first place or the reference system is forgotten. In other cases the relationship between the map’s coordinate system is not known with precision, meaning that its absolute error is much larger than its relative error. In this paper the georeferencing of medium-scale maps is computed using a single TerraSAR-X image. A single TerraSAR-X image has high geolocation accuracy but it has no 3D information. The map, however, provides the missing 3D information, and thus it is possible to compute the georeferencing of the map using the TerraSAR-X geolocation information, assembling the information of both sources to produce 3D points in the reference system of the TerraSAR-X image. Two methods based on this concept are proposed. The methods are tested with real world examples and the results are promising for further research.

scholarly journals Augmenting Transfer Learning with Semantic Reasoning

2019 ◽  
Author(s):  
Freddy Lécué ◽  
Jiaoyan Chen ◽  
Jeff Z. Pan ◽  
Huajun Chen
Keyword(s):  
Transfer Learning ◽  
Real World ◽  
General Framework ◽  
Prediction Models ◽  
Learning Tasks ◽  
Real World Applications ◽  
Bus Delay ◽  
Robust Prediction ◽  
Air Quality Forecasting ◽  
Web Learning

Transfer learning aims at building robust prediction models by transferring knowledge gained from one problem to another. In the semantic Web, learning tasks are enhanced with semantic representations. We exploit their semantics to augment transfer learning by dealing with when to transfer with semantic measurements and what to transfer with semantic embeddings. We further present a general framework that integrates the above measurements and embeddings with existing transfer learning algorithms for higher performance. It has demonstrated to be robust in two real-world applications: bus delay forecasting and air quality forecasting.

scholarly journals Some Considerations to Improve the General Feature Model and General Portrayal Model in Gittok

2019 ◽  
Vol 2 ◽  
pp. 1-7
Author(s):  
Morishige Ota
Keyword(s):  
Information Technology ◽  
General Feature ◽  
Feature Model ◽  
Geospatial Information ◽  
One Dimensional ◽  
Technology Learning ◽  
Learning Assistance ◽  
Different Types ◽  
Screening Guideline ◽  
Feature Association

<p><strong>Abstract.</strong> The general feature model (GFM) and the general portrayal model (GPM) are defined in the geospatial information technology learning assistance tool called gittok. This paper introduces five proposals to integrate different types of features and associations in the GFM and GPM: 1) the extensional-schematization procedure enables to formulate application schemata by specifying its extension, that is, every object that falls under the screening guideline; 2) nongeographic feature type may be included in the application schema; 3) feature association type can be geographic or nongeographic in a similar way as feature type; 4) nongeographic features and/or associations can be represented as a one-dimensional map or list; 5) representation by the copy of the portrayal declaration associating with a super feature type to avoid duplication should be possible. These proposals will expand the discipline of geospatial information technology (GIT).</p>

scholarly journals Preference-Aware Task Assignment in On-Demand Taxi Dispatching: An Online Stable Matching Approach

2019 ◽  
Vol 33 ◽  
pp. 2245-2252 ◽  
Author(s):  
Boming Zhao ◽  
Pan Xu ◽  
Yexuan Shi ◽  
Yongxin Tong ◽  
Zimu Zhou ◽  
...  
Keyword(s):  
Theoretical Analysis ◽  
User Experience ◽  
Real World ◽  
Online Algorithm ◽  
Task Assignment ◽  
Stable Matching ◽  
Central Issue ◽  
New Model ◽  
On Demand ◽  
Long Run

A central issue in on-demand taxi dispatching platforms is task assignment, which designs matching policies among dynamically arrived drivers (workers) and passengers (tasks). Previous matching policies maximize the profit of the platform without considering the preferences of workers and tasks (e.g., workers may prefer high-rewarding tasks while tasks may prefer nearby workers). Such ignorance of preferences impairs user experience and will decrease the profit of the platform in the long run. To address this problem, we propose preference-aware task assignment using online stable matching. Specifically, we define a new model, Online Stable Matching under Known Identical Independent Distributions (OSM-KIID). It not only maximizes the expected total profits (OBJ-1), but also tries to satisfy the preferences among workers and tasks by minimizing the expected total number of blocking pairs (OBJ-2). The model also features a practical arrival assumption validated on real-world dataset. Furthermore, we present a linear program based online algorithm LP-ALG, which achieves an online ratio of at least 1−1/e on OBJ-1 and has at most 0.6·|E| blocking pairs expectedly, where |E| is the total number of edges in the compatible graph. We also show that a natural Greedy can have an arbitrarily bad performance on OBJ-1 while maintaining around 0.5·|E| blocking pairs. Evaluations on both synthetic and real datasets confirm our theoretical analysis and demonstrate that LP-ALG strictly dominates all the baselines on both objectives when tasks notably outnumber workers.

scholarly journals An unsupervised framework for comparing graph embeddings

2019 ◽  
Author(s):  
Bogumił Kamiński ◽  
Paweł Prałat ◽  
François Théberge
Keyword(s):  
Real World ◽  
General Framework ◽  
Graph Embedding ◽  
Relevant Information ◽  
Graph Embeddings ◽  
Graph Topology ◽  
Domain Experts ◽  
Main Challenge ◽  
Synthetic Networks

Abstract Graph embedding is the transformation of vertices of a graph into set of vectors. A good embedding should capture the graph topology, vertex-to-vertex relationship and other relevant information about the graph, its subgraphs and vertices. If these objectives are achieved, an embedding is a meaningful, understandable and compressed representations of a network. Finally, vector operations are simpler and faster than comparable operations on graphs. The main challenge is that one needs to make sure that embeddings well describe the properties of the graphs. In particular, a decision has to be made on the embedding dimensionality which highly impacts the quality of an embedding. As a result, selecting the best embedding is a challenging task and very often requires domain experts. In this article, we propose a ‘divergence score’ that can be assigned to embeddings to help distinguish good ones from bad ones. This general framework provides a tool for an unsupervised graph embedding comparison. In order to achieve it, we needed to generalize the well-known Chung-Lu model to incorporate geometry which is an interesting result in its own right. In order to test our framework, we did a number of experiments with synthetic networks as well as real-world networks, and various embedding algorithms.

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