Efficiently Managing Context Information for Large-Scale Scenarios

2005 ◽  
Author(s):  
M. Grossmann ◽  
M. Bauer ◽  
N. Honle ◽  
U.-P. Kappeler ◽  
D. Nicklas ◽  
...  
Keyword(s):  
Large Scale ◽  
Context Information

scholarly journals Context-awareness for heterogeneous access management

2010 ◽  
Vol 8 ◽  
pp. 257-262 ◽  
Author(s):  
C. Mannweiler ◽  
A. Klein ◽  
J. Schneider ◽  
H. D. Schotten
Keyword(s):  
Communication Systems ◽  
Large Scale ◽  
Context Information ◽  
Access Management ◽  
Access Technology ◽  
Performance Improvements ◽  
Radio Access Technology ◽  
Radio Access ◽  
Use Context ◽  
Conventional Systems

Abstract. The increasing availability of both static and dynamic context information has steadily been driving the development of context-aware communication systems. Adapting system behavior according to current context of the network, the user, and the terminal can yield significant end-to-end performance improvements. In this paper, we present a concept for how to use context information, in particular location information and movement prediction, for Heterogeneous Access Management (HAM). In a first step, we outline the functional architecture of a distributed and extensible context management system (CMS) that defines the roles, tasks, and interfaces of all modules within such a system for large-scale context acquisition and dissemination. In a second step, we depict how the available context information can be exploited for optimizing terminal handover decisions to be made in a multi-RAT (radio access technology) environment. In addition, the utilized method for predicting terminal location as well as the objective functions used for evaluating and comparing system performance are described. Finally, we present preliminary simulation results demonstrating that HAM systems that include current and future terminal context information in the handover decision process clearly outperform conventional systems.

scholarly journals Context-Enabled Extraction of Large-Scale Urban Functional Zones from Very-High-Resolution Images: A Multiscale Segmentation Approach

2019 ◽  
Vol 11 (16) ◽  
pp. 1902 ◽  
Author(s):  
Shouji Du ◽  
Shihong Du ◽  
Bo Liu ◽  
Xiuyuan Zhang
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
Satellite Images ◽  
Nearest Neighbor ◽  
Context Information ◽  
Functional Zone ◽  
High Resolution Satellite Images ◽  
Segmentation Approach ◽  
Functional Zones ◽  
Very High

Urban functional-zone (UFZ) analysis has been widely used in many applications, including urban environment evaluation, and urban planning and management. How to extract UFZs’ spatial units which delineates UFZs’ boundaries is fundamental to urban applications, but it is still unresolved. In this study, an automatic, context-enabled multiscale image segmentation method is proposed for extracting spatial units of UFZs from very-high-resolution satellite images. First, a window independent context feature is calculated to measure context information in the form of geographic nearest-neighbor distance from a pixel to different image classes. Second, a scale-adaptive approach is proposed to determine appropriate scales for each UFZ in terms of its context information and generate the initial UFZs. Finally, the graph cuts algorithm is improved to optimize the initial UFZs. Two datasets including WorldView-2 image in Beijing and GaoFen-2 image in Nanchang are used to evaluate the proposed method. The results indicate that the proposed method can generate better results from very-high-resolution satellite images than widely used approaches like image tiles and road blocks in representing UFZs. In addition, the proposed method outperforms existing methods in both segmentation quality and running time. Therefore, the proposed method appears to be promising and practical for segmenting large-scale UFZs.

scholarly journals The 67P/Churyumov–Gerasimenko observation campaign in support of the Rosetta mission

2017 ◽  
Vol 375 (2097) ◽  
pp. 20160249 ◽  
Author(s):  
C. Snodgrass ◽  
M. F. A'Hearn ◽  
F. Aceituno ◽  
V. Afanasiev ◽  
S. Bagnulo ◽  
...  
Keyword(s):  
Large Scale ◽  
Activity Patterns ◽  
Mission Planning ◽  
Context Information ◽  
Large Collection ◽  
Future Studies ◽  
Early Results ◽  
Rosetta Mission ◽  
Comet 67P

We present a summary of the campaign of remote observations that supported the European Space Agency's Rosetta mission. Telescopes across the globe (and in space) followed comet 67P/Churyumov–Gerasimenko from before Rosetta's arrival until nearly the end of the mission in September 2016. These provided essential data for mission planning, large-scale context information for the coma and tails beyond the spacecraft and a way to directly compare 67P with other comets. The observations revealed 67P to be a relatively ‘well-behaved’ comet, typical of Jupiter family comets and with activity patterns that repeat from orbit to orbit. Comparison between this large collection of telescopic observations and the in situ results from Rosetta will allow us to better understand comet coma chemistry and structure. This work is just beginning as the mission ends—in this paper, we present a summary of the ground-based observations and early results, and point to many questions that will be addressed in future studies. This article is part of the themed issue ‘Cometary science after Rosetta’.

scholarly journals False Intel Detection In Crowd Source Knowledge Base

2021 ◽  
Vol 10 (3) ◽  
pp. 2158-2164
Keyword(s):  
Machine Learning ◽  
Information Systems ◽  
Knowledge Base ◽  
Large Scale ◽  
Learning Task ◽  
The Internet ◽  
Context Information ◽  
New Machine

Wikidata is widely considered as the biggest Encyclopaedia on the internet and it is the new large-scale knowledge base of the WikimediaFoundation. Its knowledge is increasingly used within Wikipedia itself and various other kinds of information systems imposing high demands on its integrity. Wikidata, it can be edited by anyone and as a result, unfortunately it frequently gets vandalized exposing all information systems using it to the risk of spreading vandalized and falsified information. In this paper a new machine learning based approach to detect vandalism in wikidata is presented. We propose sector 47 features that exploit both content and context information and we report on 4 classifiers as of increasing effectiveness tailored to this learning task.

Context-Aware Adaptation in an Ecology of Applications

2011 ◽  
pp. 1-25 ◽  
Author(s):  
Davy Preuveneers ◽  
Koen Victor ◽  
Yves Vanrompay ◽  
Peter Rigole ◽  
Manuele Kirsch Pinheiro
Keyword(s):  
Context Awareness ◽  
Large Scale ◽  
Context Information ◽  
Context Aware ◽  
Case Scenario ◽  
Levels Of Abstraction ◽  
Everyday Objects ◽  
Use Context ◽  
Large Scale Networks ◽  
Application Adaptation

In recent years, many researchers have studied context-awareness to support non-intrusive adaptability of context-aware applications. Context-aware applications benefit from emerging technology that connects everyday objects and provides opportunities to collect and use context information from various sources. Context-awareness helps to adapt continuously to new situations and to turn a static computing environment into a dynamic ecology of smart and proactive applications. In this chapter, we present our framework that manages and uses context information to adapt applications and the content they provide. We show how application adaptation can be handled at the composition level, by reconfiguring, redeploying and rewiring components, e.g. to fall back into reduced functionality mode when redeploying an application on a handheld. The key features of our context-aware adaptation framework notonly include local adaptations of context-aware applications and content, but also the addressing of context in large scale networks and the contextaware redeployment of running applications in a distributed setting. We discuss how adaptation is handled along various levels of abstraction (user, content, application, middleware, network) and illustrate the flexibility of context-aware content and application adaptation by means of a realistic use case scenario.

scholarly journals CFGNN: Cross Flow Graph Neural Networks for Question Answering on Complex Tables

2020 ◽  
Vol 34 (05) ◽  
pp. 9596-9603
Author(s):  
Xuanyu Zhang
Keyword(s):  
Neural Networks ◽  
Large Scale ◽  
Question Answering ◽  
Cross Flow ◽  
Context Information ◽  
Flow Graph ◽  
Graph Neural Networks ◽  
Relationship Of ◽  
The Relationship ◽  
Parent Child

Question answering on complex tables is a challenging task for machines. In the Spider, a large-scale complex table dataset, relationships between tables and columns can be easily modeled as graph. But most of graph neural networks (GNNs) ignore the relationship of sibling nodes and use summation as aggregation function to model the relationship of parent-child nodes. It may cause nodes with less degrees, like column nodes in schema graph, to obtain little information. And the context information is important for natural language. To leverage more context information flow comprehensively, we propose novel cross flow graph neural networks in this paper. The information flows of parent-child and sibling nodes cross with history states between different layers. Besides, we use hierarchical encoding layer to obtain contextualized representation in tables. Experiments on the Spider show that our approach achieves substantial performance improvement comparing with previous GNN models and their variants.

Research on Pattern Representation Based on Keyword and Word Embedding in Chinese Entity Relation Extraction

2018 ◽  
Vol 22 (4) ◽  
pp. 475-482
Author(s):  
Feiyue Ye ◽  
◽  
Zhentao Qin
Keyword(s):  
Large Scale ◽  
Rapid Development ◽  
Relation Extraction ◽  
High Accuracy ◽  
Word Embedding ◽  
Context Information ◽  
Extraction Technology ◽  
Entity Relation Extraction ◽  
The Relationship

With the rapid development of the Internet, it is becoming more and more important to extract the relationship between the entity from the massive network text and then to build the knowledge graph or the knowledge base. In this paper, we focus on the research of the pattern representation in relation extraction, and extract the high accuracy Chinese entity pairs from large scale web texts. Past relation patterns only consider shallow lexical and syntax, not accurately and deeply express pattern context information, and do not consider keywords information. According to the new entity relation extraction technology and the characteristics of Chinese corpora, we define pattern representation based on keywords and word embedding information, extract deep semantic feature of context information, and strengthen keywords information effect for relation extraction. In addition, we propose a method for obtaining sentence keyword based on word embedding. In the experiment, we use ChineseHudongEncyclopedia corpus to implement the character relation extraction system, and test the character relation extraction effect. The experimental results show that this method effectively improves the quality of the pattern, and obtains a favorable relation extraction performance.

scholarly journals Class-Specific Anchor Based and Context-Guided Multi-Class Object Detection in High Resolution Remote Sensing Imagery with a Convolutional Neural Network

2019 ◽  
Vol 11 (3) ◽  
pp. 272 ◽  
Author(s):  
Nan Mo ◽  
Li Yan ◽  
Ruixi Zhu ◽  
Hong Xie
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
High Resolution ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Large Scale ◽  
Shape Change ◽  
Context Information ◽  
False Detection ◽  
Multi Scale

In this paper, the problem of multi-scale geospatial object detection in High Resolution Remote Sensing Images (HRRSI) is tackled. The different flight heights, shooting angles and sizes of geographic objects in the HRRSI lead to large scale variance in geographic objects. The inappropriate anchor size to propose the objects and the indiscriminative ability of features for describing the objects are the main causes of missing detection and false detection in multi-scale geographic object detection. To address these challenges, we propose a class-specific anchor based and context-guided multi-class object detection method with a convolutional neural network (CNN), which can be divided into two parts: a class-specific anchor based region proposal network (RPN) and a discriminative feature with a context information classification network. A class-specific anchor block providing better initial values for RPN is proposed to generate the anchor of the most suitable scale for each category in order to increase the recall ratio. Meanwhile, we proposed to incorporate the context information into the original convolutional feature to improve the discriminative ability of the features and increase classification accuracy. Considering the quality of samples for classification, the soft filter is proposed to select effective boxes to improve the diversity of the samples for the classifier and avoid missing or false detection to some extent. We also introduced the focal loss in order to improve the classifier in classifying the hard samples. The proposed method is tested on a benchmark dataset of ten classes to prove the superiority. The proposed method outperforms some state-of-the-art methods with a mean average precision (mAP) of 90.4% and better detects the multi-scale objects, especially when objects show a minor shape change.

scholarly journals Improving Entity Recommendation with Search Log and Multi-Task Learning

2018 ◽  
Author(s):  
Jizhou Huang ◽  
Wei Zhang ◽  
Yaming Sun ◽  
Haifeng Wang ◽  
Ting Liu
Keyword(s):  
Search Engine ◽  
Large Scale ◽  
Web Search ◽  
Context Information ◽  
Context Aware ◽  
Time Step ◽  
Current Time ◽  
Task Learning ◽  
Web Search Engine ◽  
Search Logs

Entity recommendation, providing search users with an improved experience by assisting them in finding related entities for a given query, has become an indispensable feature of today's Web search engine. Existing studies typically only consider the query issued at the current time step while ignoring the in-session preceding queries. Thus, they typically fail to handle the ambiguous queries such as "apple" because the model could not understand which apple (company or fruit) is talked about. In this work, we believe that the in-session contexts convey valuable evidences that could facilitate the semantic modeling of queries, and take that into consideration for entity recommendation. Furthermore, in order to better model the semantics of queries, we learn the model in a multi-task learning setting where the query representation is shared across entity recommendation and context-aware ranking. We evaluate our approach using large-scale, real-world search logs of a widely used commercial Web search engine. The experimental results show that incorporating context information significantly improves entity recommendation, and learning the model in a multi-task learning setting could bring further improvements.

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