Effective Knowledge Based Recommender System for Tailored Multiple Point of Interest Recommendation

2019 ◽  
Vol 11 (1) ◽  
pp. 1-18 ◽  
Author(s):  
V. Vijayakumar ◽  
Subramaniyaswamy Vairavasundaram ◽  
R. Logesh ◽  
A. Sivapathi
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Multiple Point ◽  
New Paradigm ◽  
Knowledge Based ◽  
Massive Growth ◽  
Point Of Interest ◽  
Temporal Features ◽  
Large Scale Dataset ◽  
Location Based Social Networks

With the massive growth of the internet, a new paradigm of recommender systems (RS's) is introduced in various real time applications. In the research for better RS's, especially in the travel domain, the evolution of location-based social networks have helped RS's to understand the changing interests of users. In this article, the authors present a new travel RS employed on the mobile device to generate personalized travel planning comprising of multiple Point of Interests (POIs). The recommended personalized list of travel locations will be predicted by generating a heat map of already visited POIs and the highly relevant POIs will be selected for recommendation as destinations. To enhance the recommendation quality, this article exploits the temporal features for increased user visits. A personalized travel plan is recommended to the user based on the user selected POIs and the proposed travel RS is experimentally evaluated with the real-time large-scale dataset. The obtained results of the developed RS are found to be proficient by means of improved diversity and accuracy of generated recommendations.

Towards learning line descriptors from patches: a new paradigm and large-scale dataset

2020 ◽  
Author(s):  
Hongmin Liu ◽  
Yujie Liu ◽  
Miaomiao Fu ◽  
Yuhui Wei ◽  
Zhanqiang Huo ◽  
...  
Keyword(s):  
Large Scale ◽  
New Paradigm ◽  
Large Scale Dataset

scholarly journals Finding Potential Propagators and Customers in Location-Based Social Networks: An Embedding-Based Approach

2020 ◽  
Vol 10 (22) ◽  
pp. 8003
Author(s):  
Yi-Chun Chen ◽  
Cheng-Te Li
Keyword(s):  
Social Networks ◽  
Large Scale ◽  
Main Idea ◽  
Feature Representation ◽  
Specific Point ◽  
Point Of Interest ◽  
The Future ◽  
Location Based Social Networks ◽  
Low Dimensional ◽  
Embedding Methods

In the scenarios of location-based social networks (LBSN), the goal of location promotion is to find information propagators to promote a specific point-of-interest (POI). While existing studies mainly focus on accurately recommending POIs for users, less effort is made for identifying propagators in LBSN. In this work, we propose and tackle two novel tasks, Targeted Propagator Discovery (TPD) and Targeted Customer Discovery (TCD), in the context of Location Promotion. Given a target POI l to be promoted, TPD aims at finding a set of influential users, who can generate more users to visit l in the future, and TCD is to find a set of potential users, who will visit l in the future. To deal with TPD and TCD, we propose a novel graph embedding method, LBSN2vec. The main idea is to jointly learn a low dimensional feature representation for each user and each location in an LBSN. Equipped with learned embedding vectors, we propose two similarity-based measures, Influential and Visiting scores, to find potential targeted propagators and customers. Experiments conducted on a large-scale Instagram LBSN dataset exhibit that LBSN2vec and its variant can significantly outperform well-known network embedding methods in both tasks.

scholarly journals Deepfake video detection: YOLO-Face convolution recurrent approach

2021 ◽  
Vol 7 ◽  
pp. e730
Author(s):  
Aya Ismail ◽  
Marwa Elpeltagy ◽  
Mervat Zaki ◽  
Kamal A. ElDahshan
Keyword(s):  
Large Scale ◽  
Operating Characteristic ◽  
Short Term Memory ◽  
Negative Impact ◽  
Characteristic Curve ◽  
Spatial Features ◽  
Temporal Features ◽  
Large Scale Dataset ◽  
Long Short Term Memory ◽  
Face Detector

Recently, the deepfake techniques for swapping faces have been spreading, allowing easy creation of hyper-realistic fake videos. Detecting the authenticity of a video has become increasingly critical because of the potential negative impact on the world. Here, a new project is introduced; You Only Look Once Convolution Recurrent Neural Networks (YOLO-CRNNs), to detect deepfake videos. The YOLO-Face detector detects face regions from each frame in the video, whereas a fine-tuned EfficientNet-B5 is used to extract the spatial features of these faces. These features are fed as a batch of input sequences into a Bidirectional Long Short-Term Memory (Bi-LSTM), to extract the temporal features. The new scheme is then evaluated on a new large-scale dataset; CelebDF-FaceForencics++ (c23), based on a combination of two popular datasets; FaceForencies++ (c23) and Celeb-DF. It achieves an Area Under the Receiver Operating Characteristic Curve (AUROC) 89.35% score, 89.38% accuracy, 83.15% recall, 85.55% precision, and 84.33% F1-measure for pasting data approach. The experimental analysis approves the superiority of the proposed method compared to the state-of-the-art methods.

scholarly journals Smart Environmental Monitoring and Assessment Technologies (SEMAT)—A New Paradigm for Low-Cost, Remote Aquatic Environmental Monitoring

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2248 ◽  
Author(s):  
Jarrod Trevathan ◽  
Ron Johnstone
Keyword(s):  
Environmental Monitoring ◽  
Real Time ◽  
Large Scale ◽  
Low Cost ◽  
Waste Recycling ◽  
Data Management System ◽  
New Paradigm ◽  
Analysis And Design ◽  
Systems Analysis And Design ◽  
Open Standard

Expense and the logistical difficulties with deploying scientific monitoring equipment are the biggest limitations to undertaking large scale monitoring of aquatic environments. The Smart Environmental Monitoring and Assessment Technologies (SEMAT) project is aimed at addressing this problem by creating an open standard for low-cost, near real-time, remote aquatic environmental monitoring systems. This paper presents the latest refinement of the SEMAT system in-line with the evolution of existing technologies, inexpensive sensors and environmental monitoring expectations. We provide a systems analysis and design of the SEMAT remote monitoring units and the back-end data management system. The system’s value is augmented through a unique e-waste recycling and repurposing model which engages/educates the community in the production of the SEMAT units using social enterprise. SEMAT serves as an open standard for the community to innovate around to further the state of play with low-cost environmental monitoring. The latest SEMAT units have been trialled in a peri-urban lake setting and the results demonstrate the system’s capabilities to provide ongoing data in near real-time to validate an environmental model of the study site.

scholarly journals JRMOT: A Real-Time 3D Multi-Object Tracker and a New Large-Scale Dataset

2020 ◽  
Author(s):  
Abhijeet Shenoi ◽  
Mihir Patel ◽  
JunYoung Gwak ◽  
Patrick Goebel ◽  
Amir Sadeghian ◽  
...  
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Large Scale Dataset

scholarly journals Measure Term Similarity using a Semantic Network Approach

2021 ◽  
pp. 5-9
Author(s):  
D. M. Kulkarni ◽  
◽  
Swapnaja S. Kulkarni ◽  
Keyword(s):  
Semantic Similarity ◽  
Search Engines ◽  
Large Scale ◽  
Semantic Network ◽  
Network Approach ◽  
Traditional System ◽  
Knowledge Based ◽  
Text Understanding ◽  
Large Scale Dataset ◽  
Term Similarity

Computing semantic similarity between two words comes with variety of approaches. This is mainly essential for the applications such as text analysis, text understanding. In traditional system search engines are used to compute the similarity between words. In that search engines are keyword based. There is one drawback that user should know what exactly they are looking for. There are mainly two main approaches for computation namely knowledge based and corpus based approaches. But there is one drawback that these two approaches are not suitable for computing similarity between multi-word expressions. This system provides efficient and effective approach for computing term similarity using semantic network approach. A clustering approach is used in order to improve the accuracy of the semantic similarity. This approach is more efficient than other computing algorithms. This technique can also apply to large scale dataset to compute term similarity.

scholarly journals Response Generation by Context-Aware Prototype Editing

2019 ◽  
Vol 33 ◽  
pp. 7281-7288 ◽  
Author(s):  
Yu Wu ◽  
Furu Wei ◽  
Shaohan Huang ◽  
Yunli Wang ◽  
Zhoujun Li ◽  
...  
Keyword(s):  
Large Scale ◽  
Generative Models ◽  
Context Aware ◽  
Open Domain ◽  
New Paradigm ◽  
Large Scale Dataset ◽  
Current Context ◽  
Editing Process ◽  
Remarkable Progress

Open domain response generation has achieved remarkable progress in recent years, but sometimes yields short and uninformative responses. We propose a new paradigm, prototypethen-edit for response generation, that first retrieves a prototype response from a pre-defined index and then edits the prototype response according to the differences between the prototype context and current context. Our motivation is that the retrieved prototype provides a good start-point for generation because it is grammatical and informative, and the post-editing process further improves the relevance and coherence of the prototype. In practice, we design a contextaware editing model that is built upon an encoder-decoder framework augmented with an editing vector. We first generate an edit vector by considering lexical differences between a prototype context and current context. After that, the edit vector and the prototype response representation are fed to a decoder to generate a new response. Experiment results on a large scale dataset demonstrate that our new paradigm significantly increases the relevance, diversity and originality of generation results, compared to traditional generative models. Furthermore, our model outperforms retrieval-based methods in terms of relevance and originality.

scholarly journals Orchestrating Knowledge-Based Urban Development

2010 ◽  
pp. 281-295 ◽  
Author(s):  
Tan Yigitcanlar ◽  
Muna Sarimin
Keyword(s):  
Urban Development ◽  
Knowledge Economy ◽  
Large Scale ◽  
South East Asia ◽  
New Paradigm ◽  
Knowledge Based ◽  
Urban Planning And Development ◽  
Multimedia Super Corridor ◽  
Development And Management ◽  
Development And Evolution

In the era of knowledge economy, cities and regions have started increasingly investing on their physical, social and knowledge infrastructures so as to foster, attract and retain global talent and investment. Knowledge-based urban development as a new paradigm in urban planning and development is being implemented across the globe in order to increase the competitiveness of cities and regions. This chapter provides an overview of the lessons from Multimedia Super Corridor, Malaysia as one of the first large scale manifestations of knowledge-based urban development in South East Asia. The chapter investigates the application of the knowledge-based urban development concept within the Malaysian context, and, particularly, scrutinises the development and evolution of Multimedia Super Corridor by focusing on strategies, implementation policies, infrastructural implications, and agencies involved in the development and management of the corridor. In the light of the literature and case findings, the chapter provides generic recommendations, on the orchestration of knowledge-based urban development, for other cities and regions seeking such development.

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