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Articles 1 - 6 of 6
Full-Text Articles in Engineering
Investigating Perceptions Of A Location-Based Annotation System, Huynh Nhu Hop Quach, Khasfariyati Razikin, Dion Hoe-Lian Goh, Thi Nhu Quynh Kim, Tan Phat Pham, Yin-Leng Theng, Ee-Peng Lim
Investigating Perceptions Of A Location-Based Annotation System, Huynh Nhu Hop Quach, Khasfariyati Razikin, Dion Hoe-Lian Goh, Thi Nhu Quynh Kim, Tan Phat Pham, Yin-Leng Theng, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
We introduce MobiTOP, a Web-based system for organizing and retrieving hierarchical location-based annotations. Each annotation contains multimedia content (such as text, images, video) associated with a location, and users are able to annotate existing annotations to an arbitrary depth, in effect creating a hierarchy. An evaluation was conducted on a group of potential users to ascertain their perceptions of the usability of the application. The results were generally positive and the majority of the participants saw MobiTOP as a useful platform to share location-based information. We conclude with implications of our work and opportunities for future research.
Pagesense: Style-Wise Web Page Advertising, Lusong Li, Tao Mei, Xiang Niu, Chong-Wah Ngo
Pagesense: Style-Wise Web Page Advertising, Lusong Li, Tao Mei, Xiang Niu, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
This paper presents an innovative style-wise advertising platform for web page. Web page “style” mainly refers to visual effects, such as color and layout. Unlike the most popular ad-network such as Google AdSense which needs publishers to change the original structure of their pages and define the position and style of the embedded ads manually, stylewise page advertising aims to automatically deliver styleconsistent ads at proper positions within the web page, without breaking the layout of the original page. Our system is motivated from the fact that almost 90% web pages contain blank regions without any content. Given a web …
Managing Media Rich Geo-Spatial Annotations For A Map-Based Mobile Application Using Clustering, Khasfariyati Razikin, Dion Hoe-Lian Goh, Ee Peng Lim, Aixin Sun, Yin-Leng Theng, Thi Nhu Quynh Kim, Kalyani Chatterjea, Chew-Hung Chang
Managing Media Rich Geo-Spatial Annotations For A Map-Based Mobile Application Using Clustering, Khasfariyati Razikin, Dion Hoe-Lian Goh, Ee Peng Lim, Aixin Sun, Yin-Leng Theng, Thi Nhu Quynh Kim, Kalyani Chatterjea, Chew-Hung Chang
Research Collection School Of Computing and Information Systems
With the prevalence of mobile devices that are equipped with wireless Internet capabilities and Global Positioning System (GPS) functionality, the creation and access of user-generated content are extended to users on the go. Such content are tied to real world objects, in the form of geospatial annotations, and it is only natural that these annotations are visualized using a map-based approach. However, viewing maps that are filled with annotations could hinder the serendipitous discovery of data, especially on the small screens of mobile devices. This calls for a need to manage the annotations. In this paper, we introduce a mobile …
Continuous Spatial Assignment Of Moving Users, Hou U Leong, Kyriakos Mouratidis, Nikos Mamoulis
Continuous Spatial Assignment Of Moving Users, Hou U Leong, Kyriakos Mouratidis, Nikos Mamoulis
Research Collection School Of Computing and Information Systems
Consider a set of servers and a set of users, where each server has a coverage region (i.e., an area of service) and a capacity (i.e., a maximum number of users it can serve). Our task is to assign every user to one server subject to the coverage and capacity constraints. To offer the highest quality of service, we wish to minimize the average distance between users and their assigned server. This is an instance of a well-studied problem in operations research, termed optimal assignment. Even though there exist several solutions for the static case (where user locations are fixed), …
A Self-Organizing Neural Architecture Integrating Desire, Intention And Reinforcement Learning, Ah-Hwee Tan, Yu-Hong Feng, Yew-Soon Ong
A Self-Organizing Neural Architecture Integrating Desire, Intention And Reinforcement Learning, Ah-Hwee Tan, Yu-Hong Feng, Yew-Soon Ong
Research Collection School Of Computing and Information Systems
This paper presents a self-organizing neural architecture that integrates the features of belief, desire, and intention (BDI) systems with reinforcement learning. Based on fusion Adaptive Resonance Theory (fusion ART), the proposed architecture provides a unified treatment for both intentional and reactive cognitive functionalities. Operating with a sense-act-learn paradigm, the low level reactive module is a fusion ART network that learns action and value policies across the sensory, motor, and feedback channels. During performance, the actions executed by the reactive module are tracked by a high level intention module (also a fusion ART network) that learns to associate sequences of actions …
Crctol: A Semantic Based Domain Ontology Learning System, Xing Jiang, Ah-Hwee Tan
Crctol: A Semantic Based Domain Ontology Learning System, Xing Jiang, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
Domain ontologies play an important role in supporting knowledge‐based applications in the Semantic Web. To facilitate the building of ontologies, text mining techniques have been used to perform ontology learning from texts. However, traditional systems employ shallow natural language processing techniques and focus only on concept and taxonomic relation extraction. In this paper we present a system, known as Concept‐Relation‐Concept Tuple‐based Ontology Learning (CRCTOL), for mining ontologies automatically from domain‐specific documents. Specifically, CRCTOL adopts a full text parsing technique and employs a combination of statistical and lexico‐syntactic methods, including a statistical algorithm that extracts key concepts from a document collection, …