scholarly journals Mobile Multimedia

2010 ◽  
Vol 2 (3) ◽  
pp. 19-39 ◽  
Author(s):  
Michael O’Grady ◽  
Gregory O’Hare ◽  
Rem Collier
Keyword(s):  
Service Provision ◽  
Contextual Information ◽  
Mobile Multimedia ◽  
Context Aware ◽  
Multimedia Services ◽  
Dynamic Content ◽  
Conflicting Demands ◽  
Contextual Elements ◽  
Content Provision ◽  
Mobile Subscriber

Delivering multimedia services to roaming subscribers raises significant challenges for content providers. There are a number of reasons for this; however, the principal difficulties arise from the inherent differences between the nature of mobile computing usage, and that of its static counterpart. The harnessing of appropriate contextual elements pertaining to a mobile subscriber at any given time offers significant opportunities for enhancing and customising service delivery. Dynamic content provision is a case in point. The versatile nature of the mobile subscriber offers opportunities for the delivery of content that is most appropriate to the subscriber’s prevailing context, and hence is most likely to be welcomed. To succeed in this endeavour requires an innate understanding of the technologies, the mobile usage paradigm and the application domain in question, such that conflicting demands may be reconciled to the subscriber’s benefit. In this paper, multimedia-augmented service provision for mobile subscribers is considered in light of the availability of contextual information. In particular, context-aware pre-caching is advocated as a means of maximising the possibilities for delivering context-aware services to mobile subscribers in scenarios of dynamic contexts.

Mobile Multimedia

2011 ◽  
pp. 157-178
Author(s):  
Michael J. O’Grady ◽  
Gregory M.P. O’Hare ◽  
Rem Collier
Keyword(s):  
Service Delivery ◽  
Contextual Information ◽  
Context Aware ◽  
Application Domain ◽  
Multimedia Services ◽  
Dynamic Content ◽  
Conflicting Demands ◽  
Contextual Elements ◽  
Content Provision ◽  
Mobile Subscriber

Delivering multimedia services to roaming subscribers raises significant challenges for content providers. There are a number of reasons for this; however, the principal difficulties arise from the inherent differences between the nature of mobile computing usage, and that of its static counterpart. The harnessing of appropriate contextual elements pertaining to a mobile subscriber at any given time offers significant opportunities for enhancing and customising service delivery. Dynamic content provision is a case in point. The versatile nature of the mobile subscriber offers opportunities for the delivery of content that is most appropriate to the subscriber’s prevailing context, and hence is most likely to be welcomed. To succeed in this endeavour requires an innate understanding of the technologies, the mobile usage paradigm and the application domain in question, such that conflicting demands may be reconciled to the subscriber’s benefit. In this paper, multimedia-augmented service provision for mobile subscribers is considered in light of the availability of contextual information. In particular, context-aware pre-caching is advocated as a means of maximising the possibilities for delivering context-aware services to mobile subscribers in scenarios of dynamic contexts.

scholarly journals Context-Aware Neural Machine Translation for Korean Honorific Expressions

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1589
Author(s):  
Yongkeun Hwang ◽  
Yanghoon Kim ◽  
Kyomin Jung
Keyword(s):  
Machine Translation ◽  
Deep Neural Networks ◽  
Contextual Information ◽  
Context Aware ◽  
Neural Machine Translation ◽  
Translation Quality ◽  
Sentence Level ◽  
Proposed Model ◽  
The Given ◽  
The Relationship

Neural machine translation (NMT) is one of the text generation tasks which has achieved significant improvement with the rise of deep neural networks. However, language-specific problems such as handling the translation of honorifics received little attention. In this paper, we propose a context-aware NMT to promote translation improvements of Korean honorifics. By exploiting the information such as the relationship between speakers from the surrounding sentences, our proposed model effectively manages the use of honorific expressions. Specifically, we utilize a novel encoder architecture that can represent the contextual information of the given input sentences. Furthermore, a context-aware post-editing (CAPE) technique is adopted to refine a set of inconsistent sentence-level honorific translations. To demonstrate the efficacy of the proposed method, honorific-labeled test data is required. Thus, we also design a heuristic that labels Korean sentences to distinguish between honorific and non-honorific styles. Experimental results show that our proposed method outperforms sentence-level NMT baselines both in overall translation quality and honorific translations.

scholarly journals A Context-Aware Accurate Wellness Determination (CAAWD) Model for Elderly People Using Lazy Associative Classification

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1613 ◽  
Author(s):  
Farhan Sabir Ujager ◽  
Azhar Mahmood
Keyword(s):  
Elderly People ◽  
Data Extraction ◽  
Contextual Information ◽  
Elderly Person ◽  
The Elderly ◽  
Sensor Nodes ◽  
Behavior Patterns ◽  
Context Aware ◽  
Classification Rules ◽  
Training Time

Wireless Sensor Network (WSN) based smart homes are proving to be an ideal candidate to provide better healthcare facilities to elderly people in their living areas. Several currently proposed techniques have implementation and usage complexities (such as wearable devices and the charging of these devices) which make these proposed techniques less acceptable for elderly people, while the behavioral analysis based on visual techniques lacks privacy. In this paper, a context-aware accurate wellness determination (CAAWD) model for elderly people is presented, where behavior monitoring information is extracted by using simple sensor nodes attached to household objects and appliances for the analysis of daily, frequent behavior patterns of elderly people in a simple and non-obtrusive manner. A contextual data extraction algorithm (CDEA) is proposed for the generation of contextually comprehensive behavior-training instances for accurate wellness classification. The CDEA presents an activity’s spatial–temporal information along with behavioral contextual correlation aspects (such as the object/appliance of usage and sub-activities of an activity) which are vital for accurate wellness analysis and determination. As a result, the classifier is trained in a more logical manner in the context of behavior parameters which are more relevant for wellness determination. The frequent behavioral patterns are classified using the lazy associative classifier (LAC) for wellness determination. The associative nature of LAC helps to integrate spatial–temporal and related contextual attributes (provided by CDEA) of elderly behavior to generate behavior-focused classification rules. Similarly, LAC provides high accuracy with less training time of the classifier, includes minimum-support behavior patterns, and selects highly accurate classification rules for the classification of a test instance. CAAWD further introduces the ability to contextually validate the authenticity of the already classified instance by taking behavioral contextual information (of the elderly person) from the caregiver. Due to the consideration of spatial–temporal behavior contextual attributes, the use of an efficient classifier, and the ability to contextually validate the classified instances, it has been observed that the CAAWD model out-performs currently proposed techniques in terms of accuracy, precision, and f-measure.

scholarly journals MASEMUL: A Simulation Tool for Movement-Aware MANET Scheduling Strategies for Multimedia Communications

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Moustafa M. Nasralla ◽  
Iván García-Magariño ◽  
Jaime Lloret
Keyword(s):  
Mobile Networks ◽  
Content Delivery ◽  
Multimedia Communications ◽  
Multimedia Traffic ◽  
Simulation Tool ◽  
Context Aware ◽  
Mobile Nodes ◽  
Multimedia Services ◽  
Agent Based ◽  
Common Strategy

The last decade has witnessed a steep growth in multimedia traffic due to real-time content delivery such as in online games and video conferencing. In some contexts, MANETs play a key role in the hyperconnectivity of everything in multimedia services. In this context, this work proposes a new scheduling approach based on context-aware mobile nodes for their connectivity. The contribution relies on reporting not only the locations of devices in the network but also their movement identified by sensors. In order to illustrate this approach, we have developed a novel agent-based simulator called MASEMUL for illustrating the proposed approach. The results show that a movement-aware scheduling strategy defined with the proposed approach has decreased the ratio of channel interruptions over another common strategy in mobile networks.

scholarly journals IMPLEMENTASI CONTEXT AWARE PADA SISTEM TRAVEL PLANNER BERBASIS MOBILE

2021 ◽  
Vol 5 (1) ◽  
pp. 23-27
Author(s):  
Viery Darmawan ◽  
◽  
Rengga Asmara ◽  
Ira Prasetyaningrum
Keyword(s):  
Contextual Information ◽  
The Internet ◽  
Context Aware ◽  
New Approach ◽  
Tourist Attractions ◽  
Technological Advances ◽  
Long Time ◽  
Travel Efficiency ◽  
Opening And Closing

In the era of technological advances, tourists will first seek information about the tourist object to be addressed, even tourists often don't have a destination, so they have to search one by one via the internet. In determining travel plans, it is often to see one by one the review of tourist attractions and conclude the results will take a long time, while tourists need actual and fast information to determine the travel plans. In this study, the authors take a new approach, namely by creating a mobile-based travel planner system that compiles travel plans automatically by considering contextual information related to tourist location points, whether of tourist locations during travel days, travel opening and closing hours, so that it will increase travel efficiency without having to do the research manually which takes a long time. The system can also provide travel recommendations based on visitor comments sentiment on Google Places and is equipped with a trip route that will be generated automatically. This research is useful for helping tourists plan their trip actually because of the consideration of contextual information so that it will make it easier and save tourists time.

scholarly journals Efficient Context-Aware Neural Machine Translation with Layer-Wise Weighting and Input-Aware Gating

2020 ◽  
Author(s):  
Hongfei Xu ◽  
Deyi Xiong ◽  
Josef van Genabith ◽  
Qiuhui Liu
Keyword(s):  
Machine Translation ◽  
Contextual Information ◽  
Computational Cost ◽  
Representation Learning ◽  
Vital Role ◽  
Context Aware ◽  
Neural Machine Translation ◽  
Gating Mechanism ◽  
Sentence Level ◽  
Parallel Data

Existing Neural Machine Translation (NMT) systems are generally trained on a large amount of sentence-level parallel data, and during prediction sentences are independently translated, ignoring cross-sentence contextual information. This leads to inconsistency between translated sentences. In order to address this issue, context-aware models have been proposed. However, document-level parallel data constitutes only a small part of the parallel data available, and many approaches build context-aware models based on a pre-trained frozen sentence-level translation model in a two-step training manner. The computational cost of these approaches is usually high. In this paper, we propose to make the most of layers pre-trained on sentence-level data in contextual representation learning, reusing representations from the sentence-level Transformer and significantly reducing the cost of incorporating contexts in translation. We find that representations from shallow layers of a pre-trained sentence-level encoder play a vital role in source context encoding, and propose to perform source context encoding upon weighted combinations of pre-trained encoder layers' outputs. Instead of separately performing source context and input encoding, we propose to iteratively and jointly encode the source input and its contexts and to generate input-aware context representations with a cross-attention layer and a gating mechanism, which resets irrelevant information in context encoding. Our context-aware Transformer model outperforms the recent CADec [Voita et al., 2019c] on the English-Russian subtitle data and is about twice as fast in training and decoding.

A Recommendation Model for Energy-Efficient Data Mining Services Based on Data Properties

2021 ◽  
Author(s):  
Zainab Al-Zanbouri
Keyword(s):  
Data Mining ◽  
Energy Consumption ◽  
Web Services ◽  
Recommender System ◽  
Energy Efficient ◽  
Contextual Information ◽  
Three Dimensions ◽  
Context Aware ◽  
Rating Data ◽  
Recommendation Accuracy

Currently, there is a big increase in the usage of data analytics applications and services because of the growth in the data produced from different sources. The QoS properties such as response time and latency of these services are important factors to decide which services to select. As a result of IT expansion, energy consumption has become a big issue. Therefore, establishing a QoS-based web service recommender system that considers energy consumption as one of the essential QoS properties represents a significant step towards selecting the energy efficient web services. This dissertation presents an experimental study on energy consumption levels and latency behavior collected from a set of data mining web services running on different datasets. Our study shows that there is a strong relation between the dataset properties and the QoS properties. Based on the findings from this study, a recommender system is built which considers three dimensions (user, service, dataset). The energy consumption values of candidate services invoked by specific users can be predicted for a given dataset. Afterwards, these services can be ranked according to their predicted energy values and presented to users. We propose three approaches to build our recommender system and we treat it as a context-aware recommendation problem. The dataset is considered as contextual information and we use a context-aware matrix factorization model to predict energy values. In the first approach, we adopt the pre-filtering model where the contextual information serves as a query for filtering relevant rating data. In the second approach, we propose a new method for the pre-filtering implementation. Finally, in the last approach, we adopt the contextual modeling method and we explore different ways of representing dataset information as contextual factors to investigate their impacts on the recommendation accuracy. We compare the proposed approaches with the baseline approaches and the results show the effectiveness of the proposed ones. Also, we compare the performance of the three approaches to discover the best-fit approach when being measured using different metrics. Both prediction and recommendation accuracy of the proposed approaches are significantly better than the baseline models.

Using Notification Systems to Create Social Places for Online Learning

2011 ◽  
pp. 1040-1050
Author(s):  
James M. Laffey ◽  
Christopher J. Amelung
Keyword(s):  
Online Learning ◽  
Context Awareness ◽  
Contextual Information ◽  
Learning Activity ◽  
Course Management Systems ◽  
Context Aware ◽  
Learning Context ◽  
Notification System ◽  
The Social ◽  
Critical Components

Context-aware activity notification systems have potential to improve and support the social experience of online learning. The authors of this chapter have developed a Context-aware Activity Notification System (CANS) that monitors online learning activities and represents relevant contextual information by providing notification and making the learning activity salient to other participants. The chapter describes previous efforts to develop and support online learning context awareness systems; it also defines the critical components and features of such a system. It is argued that notification systems can provide methods for using the context of activity to support members’ understanding of the meaning of activity. When designed and implemented effectively, CANS can turn course management systems (CMS) into technologies of social interaction to support the social requirements of learning.

Sign in / Sign up

Export Citation Format

Share Document