Knowledge-based urban development of multimedia super corridor, Malaysia : an overview

In recent years, with the impact of the global knowledge economy, a more comprehensive urban development approach, so called 'knowledge-based urban development', has gained significant popularity. This paper discusses the critical connections among knowledge-based urban development strategies, knowledge-intensive industries and information and communication technology infrastructures. In particular, the research focuses on investigating the application of the knowledge-based urban development concept by discussing one of the South East Asia's large scale knowledge-based urban development manifestations of Malaysia's Multimedia Super Corridor. The paper scrutinises Malaysia's experience in the development and evolution of the Multimedia Super Corridor from the angle of knowledge-based urban development policy implementation, infrastructural implications, and actors involved in its development and management. This paper provides a number of lessons learned from the Multimedia Super Corridor on the orchestration of knowledge-based development that is a necessity for cities seeking successful knowledge city and knowledge economy transformations.

User profiling based on folksonomy information in Web 2.0 for personalized recommender systems

Information overload has become a serious issue for web users. Personalisation can provide effective solutions to overcome this problem. Recommender systems are one popular personalisation tool to help users deal with this issue. As the base of personalisation, the accuracy and efficiency of web user profiling affects the performances of recommender systems and other personalisation systems greatly. In Web 2.0, the emerging user information provides new possible solutions to profile users. Folksonomy or tag information is a kind of typical Web 2.0 information. Folksonomy implies the users‘ topic interests and opinion information. It becomes another source of important user information to profile users and to make recommendations. However, since tags are arbitrary words given by users, folksonomy contains a lot of noise such as tag synonyms, semantic ambiguities and personal tags. Such noise makes it difficult to profile users accurately or to make quality recommendations. This thesis investigates the distinctive features and multiple relationships of folksonomy and explores novel approaches to solve the tag quality problem and profile users accurately. Harvesting the wisdom of crowds and experts, three new user profiling approaches are proposed: folksonomy based user profiling approach, taxonomy based user profiling approach, hybrid user profiling approach based on folksonomy and taxonomy. The proposed user profiling approaches are applied to recommender systems to improve their performances. Based on the generated user profiles, the user and item based collaborative filtering approaches, combined with the content filtering methods, are proposed to make recommendations. The proposed new user profiling and recommendation approaches have been evaluated through extensive experiments. The effectiveness evaluation experiments were conducted on two real world datasets collected from Amazon.com and CiteULike websites. The experimental results demonstrate that the proposed user profiling and recommendation approaches outperform those related state-of-the-art approaches. In addition, this thesis proposes a parallel, scalable user profiling implementation approach based on advanced cloud computing techniques such as Hadoop, MapReduce and Cascading. The scalability evaluation experiments were conducted on a large scaled dataset collected from Del.icio.us website. This thesis contributes to effectively use the wisdom of crowds and expert to help users solve information overload issues through providing more accurate, effective and efficient user profiling and recommendation approaches. It also contributes to better usages of taxonomy information given by experts and folksonomy information contributed by users in Web 2.0.

Personalized recommender system based on item taxonomy and folksonomy

Item folksonomy or tag information is popularly available on the web now. However, since tags are arbitrary words given by users, they contain a lot of noise such as tag synonyms, semantic ambiguities and personal tags. Such noise brings difficulties to improve the accuracy of item recommendations. In this paper, we propose to combine item taxonomy and folksonomy to reduce the noise of tags and make personalized item recommendations. The experiments conducted on the dataset collected from Amazon.com demonstrated the effectiveness of the proposed approaches. The results suggested that the recommendation accuracy can be further improved if we consider the viewpoints and the vocabularies of both experts and users.

Mining users’ opinions based on item folksonomy and taxonomy for personalized recommender systems

Item folksonomy or tag information is a kind of typical and prevalent web 2.0 information. Item folksonmy contains rich opinion information of users on item classifications and descriptions. It can be used as another important information source to conduct opinion mining. On the other hand, each item is associated with taxonomy information that reflects the viewpoints of experts. In this paper, we propose to mine for users’ opinions on items based on item taxonomy developed by experts and folksonomy contributed by users. In addition, we explore how to make personalized item recommendations based on users’ opinions. The experiments conducted on real word datasets collected from Amazon.com and CiteULike demonstrated the effectiveness of the proposed approaches.

Parallel user profiling based on folksonomy for Large Scaled Recommender Systems : an implimentation of Cascading MapReduce

The Large scaled emerging user created information in web 2.0 such as tags, reviews, comments and blogs can be used to profile users’ interests and preferences to make personalized recommendations. To solve the scalability problem of the current user profiling and recommender systems, this paper proposes a parallel user profiling approach and a scalable recommender system. The current advanced cloud computing techniques including Hadoop, MapReduce and Cascading are employed to implement the proposed approaches. The experiments were conducted on Amazon EC2 Elastic MapReduce and S3 with a real world large scaled dataset from Del.icio.us website.

A hybrid recommender systems based on weighted tags

Social tags in web 2.0 are becoming another important information source to describe the content of items as well as to profile users’ topic preferences. However, as arbitrary words given by users, tags contains a lot of noise such as tag synonym and semantic ambiguity a large number personal tags that only used by one user, which brings challenges to effectively use tags to make item recommendations. To solve these problems, this paper proposes to use a set of related tags along with their weights to represent semantic meaning of each tag for each user individually. A hybrid recommendation generation approaches that based on the weighted tags are proposed. We have conducted experiments using the real world dataset obtained from Amazon.com. The experimental results show that the proposed approaches outperform the other state of the art approaches.

Development and pilot test of a peer-support based Cardiac-Diabetes Self-Management program : a study protocol

Background: People with cardiac disease and type 2 diabetes have higher hospital readmission rates (22%)compared to those without diabetes (6%). Self-management is an effective approach to achieve better health outcomes; however there is a lack of specifically designed programs for patients with these dual conditions. This project aims to extend the development and pilot test of a Cardiac-Diabetes Self-Management Program incorporating user-friendly technologies and the preparation of lay personnel to provide follow-up support. Methods/Design: A randomised controlled trial will be used to explore the feasibility and acceptability of the Cardiac-Diabetes Self-Management Program incorporating DVD case studies and trained peers to provide follow-up support by telephone and text-messaging. A total of 30 cardiac patients with type 2 diabetes will be randomised, either to the usual care group, or to the intervention group. Participants in the intervention group will received the Cardiac-Diabetes Self-Management Program in addition to their usual care. The intervention consists of three faceto- face sessions as well as telephone and text-messaging follow up. The face-to-face sessions will be provided by a trained Research Nurse, commencing in the Coronary Care Unit, and continuing after discharge by trained peers. Peers will follow up patients for up to one month after discharge using text messages and telephone support. Data collection will be conducted at baseline (Time 1) and at one month (Time 2). The primary outcomes include self-efficacy, self-care behaviour and knowledge, measured by well established reliable tools. Discussion: This paper presents the study protocol of a randomised controlled trial to pilot evaluates a Cardiac- Diabetes Self-Management program, and the feasibility of incorporating peers in the follow-ups. Results of this study will provide directions for using such mode in delivering a self-management program for patients with both cardiac condition and diabetes. Furthermore, it will provide valuable information of refinement of the intervention program.

Raman spectroscopy of gallium- and zinc-based hydrotalcites

Insight into the unique structure of hydrotalcites (HTs) has been obtained using Raman spectroscopy. Gallium-contg. HTs of formula Zn4 Ga2(CO3)(OH)12 · xH2O (2:1 ZnGa-HT), Zn6 Ga2(CO3)(OH)16 · xH2O (3:1 ZnGa-HT) and Zn8 Ga2(CO3)(OH)18 · xH2O (4:1 ZnGa-HT) have been successfully synthesized and characterised by X-ray diffraction (XRD) and Raman spectroscopy. The d(003) spacing varies from 7.62 Å for the 2:1 ZnGa-HT to 7.64 Å for the 3:1 ZnGa-HT. The 4:1 ZnGa-HT showed a decrease in the d(003) spacing, compared to the 2:1 and 3:1 compds. Raman spectroscopy complemented with selected IR data has been used to characterize the synthesized gallium-contg. HTs. Raman bands obsd. at around 1050, 1060 and 1067 cm-1 are attributed to the sym. stretching modes of the (CO32-) units. Multiple ν3 (CO32-) antisym. stretching modes are found between 1350 and 1520 cm-1, confirming multiple carbonate species in the HT structure. The splitting of this mode indicates that the carbonate anion is in a perturbed state. Raman bands obsd. at 710 and 717 cm-1 and assigned to the ν4 (CO32-) modes support the concept of multiple carbonate species in the interlayer.

Synthesis and Raman spectroscopic characterisation of hydrotalcites based on the formula Ca6Al2(CO3)(OH)16· 4H2O

We have successfully synthesized hydrotalcites (HTs) contg. calcium, which are naturally occurring minerals. Insight into the unique structure of HTs has been obtained using a combination of X-ray diffraction (XRD) as well as IR and Raman spectroscopies. Calcium-contg. hydrotalcites (Ca-HTs) of the formula Ca4Al2(CO3)(OH)12·4H2O (2:1 Ca-HT) to Ca8Al2(CO3)(OH)20· 4H2O (4:1 Ca-HT) have been successfully synthesized and characterised by XRD and Raman spectroscopy. XRD has shown that 3:1 calcium HTs have the largest interlayer distance. Raman spectroscopy complemented with selected IR data has been used to characterize the synthesized Ca-HTs. The Raman bands obsd. at around 1086 and 1077 cm-1 were attributed to the ν1 sym. stretching modes of the (CO32-) units of calcite and carbonate intercalated into the HT interlayer. The corresponding ν3 CO32- antisym. stretching modes are found at around 1410 and 1475 cm-1.

HIFiRE re-entry observation using an image-based visual servoing system

This paper presents an image based visual servoing system that is intended to be used for tracking and obtaining scientific observations of the HIFiRE vehicles. The primary aim of this tracking platform is to acquire and track the thermal signature emitted from the surface of the vehicle during the re-entry phase of the mission using an infra-red camera. The implemented visual servoing scheme uses a classical image based approach to identify and track the target using visual kinematic control. The paper utilizes simulation and experimental results to show the tracking performance of the system using visual feedback. Discussions on current implementation and control techniques to further improve the performance of the system are also explored.

Mining positive and negative patterns for relevance feature discovery

It is a big challenge to guarantee the quality of discovered relevance features in text documents for describing user preferences because of the large number of terms, patterns, and noise. Most existing popular text mining and classification methods have adopted term-based approaches. However, they have all suffered from the problems of polysemy and synonymy. Over the years, people have often held the hypothesis that pattern-based methods should perform better than term-based ones in describing user preferences, but many experiments do not support this hypothesis. The innovative technique presented in paper makes a breakthrough for this difficulty. This technique discovers both positive and negative patterns in text documents as higher level features in order to accurately weight low-level features (terms) based on their specificity and their distributions in the higher level features. Substantial experiments using this technique on Reuters Corpus Volume 1 and TREC topics show that the proposed approach significantly outperforms both the state-of-the-art term-based methods underpinned by Okapi BM25, Rocchio or Support Vector Machine and pattern based methods on precision, recall and F measures.

Ontology-based specific and exhaustive user profiles for constraint information fusion for multi-agents

Intelligent agents are an advanced technology utilized in Web Intelligence. When searching information from a distributed Web environment, information is retrieved by multi-agents on the client site and fused on the broker site. The current information fusion techniques rely on cooperation of agents to provide statistics. Such techniques are computationally expensive and unrealistic in the real world. In this paper, we introduce a model that uses a world ontology constructed from the Dewey Decimal Classification to acquire user profiles. By search using specific and exhaustive user profiles, information fusion techniques no longer rely on the statistics provided by agents. The model has been successfully evaluated using the large INEX data set simulating the distributed Web environment.

Selected new training documents to update user profile

Relevance Feedback (RF) has been proven very effective for improving retrieval accuracy. Adaptive information filtering (AIF) technology has benefited from the improvements achieved in all the tasks involved over the last decades. A difficult problem in AIF has been how to update the system with new feedback efficiently and effectively. In current feedback methods, the updating processes focus on updating system parameters. In this paper, we developed a new approach, the Adaptive Relevance Features Discovery (ARFD). It automatically updates the system's knowledge based on a sliding window over positive and negative feedback to solve a nonmonotonic problem efficiently. Some of the new training documents will be selected using the knowledge that the system currently obtained. Then, specific features will be extracted from selected training documents. Different methods have been used to merge and revise the weights of features in a vector space. The new model is designed for Relevance Features Discovery (RFD), a pattern mining based approach, which uses negative relevance feedback to improve the quality of extracted features from positive feedback. Learning algorithms are also proposed to implement this approach on Reuters Corpus Volume 1 and TREC topics. Experiments show that the proposed approach can work efficiently and achieves the encouragement performance.

Image-based safety assessment : automated spatial safety risk identification of earthmoving and surface mining activities

This paper presents an automated image‐based safety assessment method for earthmoving and surface mining activities. The literature review revealed the possible causes of accidents on earthmoving operations, investigated the spatial risk factors of these types of accident, and identified spatial data needs for automated safety assessment based on current safety regulations. Image‐based data collection devices and algorithms for safety assessment were then evaluated. Analysis methods and rules for monitoring safety violations were also discussed. The experimental results showed that the safety assessment method collected spatial data using stereo vision cameras, applied object identification and tracking algorithms, and finally utilized identified and tracked object information for safety decision making.

Security analysis and enhancement of one-way hash based low-cost authentication protocol (OHLCAP)

Choi et al. recently proposed an efficient RFID authentication protocol for a ubiquitous computing environment, OHLCAP(One-Way Hash based Low-Cost Authentication Protocol). However, this paper reveals that the protocol has several security weaknesses : 1) traceability based on the leakage of counter information, 2) vulnerability to an impersonation attack by maliciously updating a random number, and 3) traceability based on a physically-attacked tag. Finally, a security enhanced group-based authentication protocol is presented.