A local context-aware LDA model for topic modeling in a document network

2017 ◽  
Vol 68 (6) ◽  
pp. 1429-1448 ◽  
Author(s):  
Yang Liu ◽  
Songhua Xu
Keyword(s):  
Topic Modeling ◽  
Local Context ◽  
Context Aware

Enhanced context-aware recommendation using topic modeling and particle swarm optimization

2021 ◽  
pp. 1-16
Author(s):  
Ibtissem Gasmi ◽  
Mohamed Walid Azizi ◽  
Hassina Seridi-Bouchelaghem ◽  
Nabiha Azizi ◽  
Samir Brahim Belhaouari
Keyword(s):  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
State Of The Art ◽  
Weighting Function ◽  
Contextual Factors ◽  
Pearson Correlation ◽  
Correlation Coefficients ◽  
Pso Algorithm ◽  
Context Aware ◽  
Proposed Model

Context-Aware Recommender System (CARS) suggests more relevant services by adapting them to the user’s specific context situation. Nevertheless, the use of many contextual factors can increase data sparsity while few context parameters fail to introduce the contextual effects in recommendations. Moreover, several CARSs are based on similarity algorithms, such as cosine and Pearson correlation coefficients. These methods are not very effective in the sparse datasets. This paper presents a context-aware model to integrate contextual factors into prediction process when there are insufficient co-rated items. The proposed algorithm uses Latent Dirichlet Allocation (LDA) to learn the latent interests of users from the textual descriptions of items. Then, it integrates both the explicit contextual factors and their degree of importance in the prediction process by introducing a weighting function. Indeed, the PSO algorithm is employed to learn and optimize weights of these features. The results on the Movielens 1 M dataset show that the proposed model can achieve an F-measure of 45.51% with precision as 68.64%. Furthermore, the enhancement in MAE and RMSE can respectively reach 41.63% and 39.69% compared with the state-of-the-art techniques.

Model and Infrastructure for Communications in Context-Aware Services

2011 ◽  
pp. 472-486
Author(s):  
Cristina Rodriguez-Sanchez ◽  
Susana Borromeo ◽  
Juan Hernandez-Tamames
Keyword(s):  
Mobile Communications ◽  
Local Environment ◽  
Local Context ◽  
Context Aware ◽  
The Novel ◽  
Novel Approach ◽  
Complex Process ◽  
Wide Range ◽  
Ambient Intelligent ◽  
New Type

The appearance of concepts such as “Ambient Intelligent”, “Ubiquitous Computing” and “Context-Awareness” is causing the development of a new type of services called “Context-Aware Services” that in turn may affect users of mobile communications. This technology revolution is a a complex process because of the heterogeneity of contents, devices, objects, technologies, resources and users that can coexist at the same local environment. The novel approach of our work is the development of a ”Local Infrastructure” in order to provide intelligent, transparent and adaptable services to the user as well as to solve the problem of local context control. Two contributions will be presented: conceptual model for developing a local infrastructure and an architecture design to control the service offered by the local infrastructure. This infrastructure proposed consists of an intelligent device network to link the personal portable device with the contextual services. The device design is modular, flexible, scalable, adaptable and reconfigurable remotely in order to tolerate new demanding services whenever are needed. Finally, the result suggests that we will be able to develop a wide range of new and useful applications, not conceived at origin.

scholarly journals Towards Context-Aware Opinion Summarization for Monitoring Social Impact of News

Information ◽  
2020 ◽  
Vol 11 (11) ◽  
pp. 535 ◽  
Author(s):  
Alejandro Ramón-Hernández ◽  
Alfredo Simón-Cuevas ◽  
María Matilde García Lorenzo ◽  
Leticia Arco ◽  
Jesús Serrano-Guerrero
Keyword(s):  
Topic Modeling ◽  
Social Impact ◽  
Opinion Mining ◽  
Relevant Information ◽  
User Generated Content ◽  
Context Aware ◽  
Government Programs ◽  
Digital Platforms ◽  
Opinion Summarization ◽  
Digital Governance

Opinion mining and summarization of the increasing user-generated content on different digital platforms (e.g., news platforms) are playing significant roles in the success of government programs and initiatives in digital governance, from extracting and analyzing citizen’s sentiments for decision-making. Opinion mining provides the sentiment from contents, whereas summarization aims to condense the most relevant information. However, most of the reported opinion summarization methods are conceived to obtain generic summaries, and the context that originates the opinions (e.g., the news) has not usually been considered. In this paper, we present a context-aware opinion summarization model for monitoring the generated opinions from news. In this approach, the topic modeling and the news content are combined to determine the “importance” of opinionated sentences. The effectiveness of different developed settings of our model was evaluated through several experiments carried out over Spanish news and opinions collected from a real news platform. The obtained results show that our model can generate opinion summaries focused on essential aspects of the news, as well as cover the main topics in the opinionated texts well. The integration of term clustering, word embeddings, and the similarity-based sentence-to-news scoring turned out the more promising and effective setting of our model.

scholarly journals When learning Italian as a Second Language, tourism and technology go hand in hand

2021 ◽  
Author(s):  
Anna Zingaro ◽  
Cristiana Cervini
Keyword(s):  
Language Learning ◽  
Mobile Devices ◽  
Mobile Application ◽  
Incidental Learning ◽  
Local Context ◽  
Learning Needs ◽  
Context Aware ◽  
Direct Experience ◽  
Language And Culture ◽  
The City

This paper aims to describe the development of CALL-ER, an application for mobile devices, produced within the CALL-ER project (Context-Aware Language Learning in Emilia Romagna). An ever-increasing availability of applications for language learning that meet the different learning needs of users, as well as the ubiquitous wireless communication, led applications for mobile devices to become gradually more context-aware. This means that language is acquired by users through the direct experience with the local context where they are. An example in this regard is represented by the CALL-ER mobile application, that supports mobility students through the incidental learning of Italian language and culture in the city of Forlì. We will begin this contribution with an outline of the theoretical underpinnings that supported the project and a presentation of the project itself. We will then present the first stage of the project, during which the application was developed before its first testing. At this point, an overall description of the application will be given. A special attention will be paid throughout this paper both to how language learning has been conceived through experiential tourism and to the multimodality of the contents.

Adult Image Classification by a Local-Context Aware Network

2018 ◽  
Author(s):  
Xizi Wang ◽  
Feng Cheng ◽  
Shilin Wang ◽  
Huanrong Sun ◽  
Gongshen Liu ◽  
...  
Keyword(s):  
Image Classification ◽  
Local Context ◽  
Context Aware

scholarly journals A Residual Attention and Local Context-Aware Network for Road Extraction from High-Resolution Remote Sensing Imagery

2021 ◽  
Vol 13 (24) ◽  
pp. 4958
Author(s):  
Ziwei Liu ◽  
Mingchang Wang ◽  
Fengyan Wang ◽  
Xue Ji
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Spectral Characteristics ◽  
Attention Mechanism ◽  
Image Resolution ◽  
Feature Representation ◽  
Local Context ◽  
Context Aware ◽  
Multi Scale ◽  
Feature Information

Extracting road information from high-resolution remote sensing images (HRI) can provide crucial geographic information for many applications. With the improvement of remote sensing image resolution, the image data contain more abundant feature information. However, this phenomenon also enhances the spatial heterogeneity between different types of roads, making it difficult to accurately discern the road and non-road regions using only spectral characteristics. To remedy the above issues, a novel residual attention and local context-aware network (RALC-Net) is proposed for extracting a complete and continuous road network from HRI. RALC-Net utilizes a dual-encoder structure to improve the feature extraction capability of the network, whose two different branches take different feature information as input data. Specifically, we construct the residual attention module using the residual connection that can integrate spatial context information and the attention mechanism, highlighting local semantics to extract local feature information of roads. The residual attention module combines the characteristics of both the residual connection and the attention mechanism to retain complete road edge information, highlight essential semantics, and enhance the generalization capability of the network model. In addition, the multi-scale dilated convolution module is used to extract multi-scale spatial receptive fields to improve the model’s performance further. We perform experiments to verify the performance of each component of RALC-Net through the ablation study. By combining low-level features with high-level semantics, we extract road information and make comparisons with other state-of-the-art models. The experimental results show that the proposed RALC-Net has excellent feature representation ability and robust generalizability, and can extract complete road information from a complex environment.

scholarly journals PornNet: A Unified Deep Architecture for Pornographic Video Recognition

2021 ◽  
Vol 11 (7) ◽  
pp. 3066
Author(s):  
Zhikang Fu ◽  
Jun Li ◽  
Guoqing Chen ◽  
Tianbao Yu ◽  
Tiansheng Deng
Keyword(s):  
Pattern Discrimination ◽  
Local Context ◽  
Context Aware ◽  
Large Dataset ◽  
Multimedia Resources ◽  
Deep Architecture ◽  
Recognition Result ◽  
Video Recognition ◽  
Image Context ◽  
Mental And Physical Health

In the era of big data, massive harmful multimedia resources publicly available on the Internet greatly threaten children and adolescents. In particular, recognizing pornographic videos is of great importance for protecting the mental and physical health of the underage. In contrast to the conventional methods which are only built on image classifier without considering audio clues in the video, we propose a unified deep architecture termed PornNet integrating dual sub-networks for pornographic video recognition. More specifically, with image frames and audio clues extracted from the pornographic videos from scratch, they are respectively delivered to two deep networks for pattern discrimination. For discriminating pornographic frames, we propose a local-context aware network that takes into account the image context in capturing the key contents, whilst leveraging an attention network which can capture temporal information for recognizing pornographic audios. Thus, we incorporate the recognition scores generated from the two sub-networks into a unified deep architecture, while making use of a pre-defined aggregation function to produce the whole video recognition result. The experiments on our newly-collected large dataset demonstrate that our proposed method exhibits a promising performance, achieving an accuracy at 93.4% on the dataset including 1 k pornographic samples along with 1 k normal videos and 1 k sexy videos.

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