Ahmad Nurzid Rosli

Alleviating the cold-start problem by incorporating movies facebook pages

Recommender systems are generally known as predictive ecosystem which recommends an appropriate list of items that may imply their similar preference or interest. Nevertheless, most discussed issues in recommendation system research domain are the cold-start problem. In this paper we proposed a novel approach to address this problem by combining similarity values obtain from a movie “Facebook Pages”. To achieve this, we first compute users’ similarity according to the rating cast on our Movie Rating System. Then, we combined similarity value obtain from user’s genre interest in “Like” information extracted from “Facebook Pages”. Finally, all the similarity values are combined to produce a new user’s similarity value. Our experiment results show that our approach is outperformed in cold-start problem compared to the benchmark algorithms. To evaluate whether our system is strong enough to recommend higher accuracy recommendation to users, we also conducted prediction coverage in this research work.

Dynamic sampling-based interpolation algorithm for representation of clickable moving object in collaborative video annotation

In recent years, many studies have addressed interactive services in smart TV context. For example, any interactive service should be able to allow users to create annotations in collaborative environments. This paper proposes a system architecture that can provide augmented information (interactive content) synchronized with video content in collaborative video annotation environments. The paper also proposes the dynamic sampling-based cubic spline interpolation (DSCSI) algorithm, a method for efficiently storing moving trajectories (x- and y-coordinates) and object shapes (width and height). A collaborative video annotation competition is organized using the closed beta service to evaluate the performance of the proposed system. The experimental results based on metadata obtained from the participants demonstrate that the proposed method can successfully reduce up to 74% of the size of trajectory data with an average error of 1.23pixels.

Ontology-driven visualization system for semantic searching

Technical manuals are very diverse, ranging from software to commodities, general instructions and technical manuals that deal with specific domains such as mechanical maintenance. Due to the vast amount of documentation, finding the information is a tedious and time consuming task, especially for the mechanics. It is also difficult to grasp relationships among contents in manuals. Many researchers have adopted ontology to solve these problems and semantically represent contents of manuals. However, if ontology becomes very large and complex, it is not easy to work with ontology. Visualization has been an effective way to grasp and manipulate ontology. In this research, we propose a new ontology model to represent and retrieve contents from the manuals. We have also designed a visualization system based on our proposed ontology. In order to model the ontology, we have analyzed aircraft maintenance process, extracted the concepts and defined relationships between concepts. After modeling ontology schema, all instances of ontology are created by instance creator. From here, raw data of maintenance manuals are preprocessed to well-formed format. Next, we create a set of rule mapping well-formed document and ontology schema. For the Component class, instance creator uses a classifier to separate all parts into Component and Primitive part class. If population task is complete, validity of data for created instances will be checked by JENA engine. The inference process will create inferred triples based on the ontology schema, and then the triples are saved into a triple repository. Our system then will use this triples repository to search necessary information and visualize the search results. We use the Prefuse toolkit to visualize the search results. With this, the mechanics can intuitively grasp the relationship between maintenance manuals using the provided information. This will allow the mechanics to easily obtain information for given tasks, reduce their time to search related information and understand the information through visualization.

Mobile landmark guide system based on LOD and SURF

In this paper, we present a mobile collaborative outdoor augmented reality system. The system will provide a user who captures image of a building facade with related introduction. The proposed system mainly consists of two major parts - annotation module and AR browsing module. Annotation module works with existing standards and linked data to allow users create annotations of the building. Annotation data in our system which only contains URI and metadata, such cluster ID, cluster centroid and the image feature vectors. When user using the system browsing the landmark in outdoor, the system first recognize the landmark and calculate the camera pose from the matching, and then the related introduction is retrieved from linked data through its URI. Experiments show that the proposed augmented reality system is efficient and robust in many cases.

E-Commerce video annotation using goodrelations-based LODs with faceted search in smart TV environment

TV-commerce is a new form of shopping that allows consumer to view, select and buy products from Smart TV. To do so, sellers annotate videos and associate it with information from online e-commerce systems in a semantic manner. In this work, we propose an e-commerce information derivation mechanism for video annotation using Linked Open Data (LOD) with faceted search. Annotation information is derived from e-commerce LODs, which linked distributed data across e-commerce web. We incorporated faceted search to allow consumer to easily make a information derivation query defined by GoodRelations ontology. The derived information is displayed as a faceted graph facilitating information choice.

Context-Aware Navigation Element for Collaborative Nonlinear Video Authoring Tool

In recent years, online video streaming service has become more popular. High internet bandwidth, powerful mobile devices, and advance of video annotation techniques have raised the popularity of the rapidly growing interactive video genre. This research focuses on enabling collaboration among authors of interactive nonlinear videos that provide alternative story plots for viewers to choose as part of their interactive behaviors. We discuss the “irrelevant navigation elements” problem, which could occur when multiple nonlinear video authors want to reuse a shared interactive video. Then, we propose a system called MAVINS, a managed navigation element for interactive nonlinear videos, to solve the aforementioned problem. The system is implemented as a web-based authoring tool and interactive video player for user-creator and user-viewer, respectively. Experimentation in self-directed learning was conducted to demonstrate the problem that occurs in current approaches as well as to evaluate the effectiveness of the proposed system to solve that particular problem. As the result, the proposed system eliminated all the irrelevant navigation elements and, on average, reduced 54.55% of the total amount of displayed navigation elements on the shared videos.