Developing trust networks based on user tagging information for recommendation making

Recommender systems are one of the recent inventions to deal with ever growing information overload. Collaborative filtering seems to be the most popular technique in recommender systems. With sufficient background information of item ratings, its performance is promising enough. But research shows that it performs very poor in a cold start situation where previous rating data is sparse. As an alternative, trust can be used for neighbor formation to generate automated recommendation. User assigned explicit trust rating such as how much they trust each other is used for this purpose. However, reliable explicit trust data is not always available. In this paper we propose a new method of developing trust networks based on user’s interest similarity in the absence of explicit trust data. To identify the interest similarity, we have used user’s personalized tagging information. This trust network can be used to find the neighbors to make automated recommendations. Our experiment result shows that the proposed trust based method outperforms the traditional collaborative filtering approach which uses users rating data. Its performance improves even further when we utilize trust propagation techniques to broaden the range of neighborhood.

Fast content-based file type identification

Digital forensic examiners often need to identify the type of a file or file fragment based only on the content of the file. Content-based file type identification schemes typically use a byte frequency distribution with statistical machine learning to classify file types. Most algorithms analyze the entire file content to obtain the byte frequency distribution, a technique that is inefficient and time consuming. This paper proposes two techniques for reducing the classification time. The first technique selects a subset of features based on the frequency of occurrence. The second speeds classification by sampling several blocks from the file. Experimental results demonstrate that up to a fifteen-fold reduction in file size analysis time can be achieved with limited impact on accuracy.

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.

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.

Digital broadcasting : a double edge sword in protecting children from disturbing news content

New technologies have the potential to both expose children to and protect them from television news footage likely to disturb or frighten. The advent of cheap, portable and widely available digital technology has vastly increased the possibility of violent news events being captured and potentially broadcast. This material has the potential to be particularly disturbing and harmful to young children. But on the flipside, available digital technology could be used to build in protection for young viewers especially when it comes to preserving scheduled television programming and guarding against violent content being broadcast during live crosses from known trouble spots. Based on interviews with news directors, parents and a review of published material two recommendations are put forward: 1. Digital television technology should be employed to prevent news events "overtaking" scheduled children's programming and to protect safe harbours placed in the classifications zones to protect children. 2. Broadcasters should regain control of the images that go to air during "live" feeds from obviously volatile situations by building in short delays in G classification zones.

Business Process Management (BPM) education in Australia : a critical review based on content analysis

Business Process Management (BPM) is a top priority in organisations and is rapidly proliferating as an emerging discipline in practice. However, the current studies show lack of appropriate BPM skilled professionals in the field and a dearth of opportunities to develop BPM expertise. This paper analyses the gap between available BPM-related education in Australia and required BPM capabilities. BPM courses offered by Australian universities and training institutions have been critically analysed and mapped against leading BPM capability frameworks to determine how well current BPM education and training offerings in Australia actually address the core capabilities required for BPM professionals. The outcomes reported here can be used by Australian universities and training institutions to better align and position their training materials to the BPM required capabilities. It could also be beneficial to individuals looking for a systematic and in-depth understanding of BPM capabilities and trainings.

Open content licensing of public sector information and the risk of tortious liability for Australian Governments

There has been an increasing interest by governments worldwide in the potential benefits of open access to public sector information (PSI). However, an important question remains: can a government incur tortious liability for incorrect information released online under an open content licence? This paper argues that the release of PSI online for free under an open content licence, specifically a Creative Commons licence, is within the bounds of an acceptable level of risk to government, especially where users are informed of the limitations of the data and appropriate information management policies and principles are in place to ensure accountability for data quality and accuracy.

A restrike waveform predictive model for shunt capacitor bank switching with point-on-wave recommendation

Circuit-breakers (CBs) are subject to electrical stresses with restrikes during capacitor bank operation. Stresses are caused by the overvoltages across CBs, the interrupting currents and the rate of rise of recovery voltage (RRRV). Such electrical stresses also depend on the types of system grounding and the types of dielectric strength curves. The aim of this study is to demonstrate a restrike waveform predictive model for a SF6 CB that considered the types of system grounding: grounded and non-grounded and the computation accuracy comparison on the application of the cold withstand dielectric strength and the hot recovery dielectric strength curve including the POW (point-on-wave) recommendations to make an assessment of increasing the CB remaining life. The simulation of SF6 CB stresses in a typical 400 kV system was undertaken and the results in the applications are presented. The simulated restrike waveforms produced with the identified features using wavelet transform can be used for restrike diagnostic algorithm development with wavelet transform to locate a substation with breaker restrikes. This study found that the hot withstand dielectric strength curve has less magnitude than the cold withstand dielectric strength curve for restrike simulation results. Computation accuracy improved with the hot withstand dielectric strength and POW controlled switching can increase the life for a SF6 CB.

Influence of preexercise muscle glycogen content on transcriptional activity of metabolic and myogenic genes in well-trained humans

To determine whether pre-exercise muscle glycogen content influences the transcription of several early-response genes involved in the regulation of muscle growth, seven male strength-trained subjects performed one-legged cycling exercise to exhaustion to lower muscle glycogen levels (Low) in one leg compared with the leg with normal muscle glycogen (Norm) and then the following day completed a unilateral bout of resistance training (RT). Muscle biopsies from both legs were taken at rest, immediately after RT, and after 3 h of recovery. Resting glycogen content was higher in the control leg (Norm leg) than in the Low leg (435 ± 87 vs. 193 ± 29 mmol/kg dry wt; P < 0.01). RT decreased glycogen content in both legs (P < 0.05), but postexercise values remained significantly higher in the Norm than the Low leg (312 ± 129 vs. 102 ± 34 mmol/kg dry wt; P < 0.01). GLUT4 (3-fold; P < 0.01) and glycogenin mRNA abundance (2.5-fold; not significant) were elevated at rest in the Norm leg, but such differences were abolished after exercise. Preexercise mRNA abundance of atrogenes was also higher in the Norm compared with the Low leg [atrogin: ?14-fold, P < 0.01; RING (really interesting novel gene) finger: ?3-fold, P < 0.05] but decreased for atrogin in Norm following RT (P < 0.05). There were no differences in the mRNA abundance of myogenic regulatory factors and IGF-I in the Norm compared with the Low leg. Our results demonstrate that 1) low muscle glycogen content has variable effects on the basal transcription of select metabolic and myogenic genes at rest, and 2) any differences in basal transcription are completely abolished after a single bout of heavy resistance training. We conclude that commencing resistance exercise with low muscle glycogen does not enhance the activity of genes implicated in promoting hypertrophy.

Learning domain ontology for tag recommendation

Recently, user tagging systems have grown in popularity on the web. The tagging process is quite simple for ordinary users, which contributes to its popularity. However, free vocabulary has lack of standardization and semantic ambiguity. It is possible to capture the semantics from user tagging and represent those in a form of ontology, but the application of the learned ontology for recommendation making has not been that flourishing. In this paper we discuss our approach to learn domain ontology from user tagging information and apply the extracted tag ontology in a pilot tag recommendation experiment. The initial result shows that by using the tag ontology to re-rank the recommended tags, the accuracy of the tag recommendation can be improved.

Ontology learning from user tagging for recommendation making

Recently, user tagging systems have grown in popularity on the web. The tagging process is quite simple for ordinary users, which contributes to its popularity. However, free vocabulary has lack of standardization and semantic ambiguity. It is possible to capture the semantics from user tagging into some form of ontology, but the application of the resulted ontology for recommendation making has not been that flourishing. In this paper we discuss our approach to learn domain ontology from user tagging information and apply the extracted tag ontology in a pilot tag recommendation experiment. The initial result shows that by using the tag ontology to re-rank the recommended tags, the accuracy of the tag recommendation can be improved.