Personalised information gathering and recommender systems : techniques and trends

With the explosive growth of resources available through the Internet, information mismatching and overload have become a severe concern to users. Web users are commonly overwhelmed by huge volume of information and are faced with the challenge of finding the most relevant and reliable information in a timely manner. Personalised information gathering and recommender systems represent state-of-the-art tools for efficient selection of the most relevant and reliable information resources, and the interest in such systems has increased dramatically over the last few years. However, web personalization has not yet been well-exploited; difficulties arise while selecting resources through recommender systems from a technological and social perspective. Aiming to promote high quality research in order to overcome these challenges, this paper provides a comprehensive survey on the recent work and achievements in the areas of personalised web information gathering and recommender systems. The report covers concept-based techniques exploited in personalised information gathering and recommender systems.

Iris based identity verification robust to sample presentation security attacks

Iris based identity verification is highly reliable but it can also be subject to attacks. Pupil dilation or constriction stimulated by the application of drugs are examples of sample presentation security attacks which can lead to higher false rejection rates. Suspects on a watch list can potentially circumvent the iris based system using such methods. This paper investigates a new approach using multiple parts of the iris (instances) and multiple iris samples in a sequential decision fusion framework that can yield robust performance. Results are presented and compared with the standard full iris based approach for a number of iris degradations. An advantage of the proposed fusion scheme is that the trade-off between detection errors can be controlled by setting parameters such as the number of instances and the number of samples used in the system. The system can then be operated to match security threat levels. It is shown that for optimal values of these parameters, the fused system also has a lower total error rate.

Robust image hash functions using higher order spectra

Robust hashing is an emerging field that can be used to hash certain data types in applications unsuitable for traditional cryptographic hashing methods. Traditional hashing functions have been used extensively for data/message integrity, data/message authentication, efficient file identification and password verification. These applications are possible because the hashing process is compressive, allowing for efficient comparisons in the hash domain but non-invertible meaning hashes can be used without revealing the original data. These techniques were developed with deterministic (non-changing) inputs such as files and passwords. For such data types a 1-bit or one character change can be significant, as a result the hashing process is sensitive to any change in the input. Unfortunately, there are certain applications where input data are not perfectly deterministic and minor changes cannot be avoided. Digital images and biometric features are two types of data where such changes exist but do not alter the meaning or appearance of the input. For such data types cryptographic hash functions cannot be usefully applied. In light of this, robust hashing has been developed as an alternative to cryptographic hashing and is designed to be robust to minor changes in the input. Although similar in name, robust hashing is fundamentally different from cryptographic hashing. Current robust hashing techniques are not based on cryptographic methods, but instead on pattern recognition techniques. Modern robust hashing algorithms consist of feature extraction followed by a randomization stage that introduces non-invertibility and compression, followed by quantization and binary encoding to produce a binary hash output. In order to preserve robustness of the extracted features, most randomization methods are linear and this is detrimental to the security aspects required of hash functions. Furthermore, the quantization and encoding stages used to binarize real-valued features requires the learning of appropriate quantization thresholds. How these thresholds are learnt has an important effect on hashing accuracy and the mere presence of such thresholds are a source of information leakage that can reduce hashing security. This dissertation outlines a systematic investigation of the quantization and encoding stages of robust hash functions. While existing literature has focused on the importance of quantization scheme, this research is the first to emphasise the importance of the quantizer training on both hashing accuracy and hashing security. The quantizer training process is presented in a statistical framework which allows a theoretical analysis of the effects of quantizer training on hashing performance. This is experimentally verified using a number of baseline robust image hashing algorithms over a large database of real world images. This dissertation also proposes a new randomization method for robust image hashing based on Higher Order Spectra (HOS) and Radon projections. The method is non-linear and this is an essential requirement for non-invertibility. The method is also designed to produce features more suited for quantization and encoding. The system can operate without the need for quantizer training, is more easily encoded and displays improved hashing performance when compared to existing robust image hashing algorithms. The dissertation also shows how the HOS method can be adapted to work with biometric features obtained from 2D and 3D face images.

Hyperlipidemia impaired innate immune response to periodontal pathogen Porphyromonas gingivalis in apolipoprotein E knockout mice

A finely-tuned innate immune response plays a pivotal role in protecting host against bacterial invasion during periodontal disease progression. Hyperlipidemia has been suggested to exacerbate periodontal health condition. However, the underlying mechanism has not been addressed. In the present study, we investigated the effect of hyperlipidemia on innate immune responses to periodontal pathogen Porphyromonas gingivalis infection. Apolipoprotein E-deficient and wild-type mice at the age of 20 weeks were used for the study. Peritoneal macrophages were isolated and subsequently used for the study of viable P. gingivalis infection. ApoE−/− mice demonstrated inhibited iNOS production and impaired clearance of P. gingivalis in vitro and in vivo; furthermore, ApoE−/− mice displayed disrupted cytokine production pattern in response to P. gingivalis, with a decreased production of tumor necrosis factor-α, interleukin-6 (IL-6), IL-1β and monocyte chemotactic protein-1. Microarray data demonstrated that Toll-like receptor (TLR) and NOD-like receptor (NLR) pathway were altered in ApoE−/− mice macrophages; further analysis of pattern recognition receptors (PRRs) demonstrated that expression of triggering receptors on myeloid cells-1 (TREM-1), an amplifier of the TLR and NLR pathway, was decreased in ApoE−/− mice macrophages, leading to decreased recruitment of NF-κB onto the promoters of the TNF-α and IL-6. Our data suggest that in ApoE−/− mice hyperlipidemia disrupts the expression of PRRs, and cripples the host’s capability to generate sufficient innate immune response to P. gingivalis, which may facilitate immune evasion, subgingival colonization and establishment of P. gingivalis in the periodontal niche.

Noise removal from wave-forms and spectrograms derived from natural recordings of the environment

The work described in this technical report is part of an ongoing project to build practical tools for the manipulation, analysis and visualisation of recordings of the natural environment. This report describes the methods we use to remove background noise from spectrograms. It updates techniques previously described in Towsey and Planitz (2011), Technical report: acoustic analysis of the natural environment, downloadable from: http://eprints.qut.edu.au/41131/. It also describes noise removal from wave-forms, a technique not described in the above 2011 technical report.

Liveness detection based on 3D face shape analysis

In recent years face recognition systems have been applied in various useful applications, such as surveillance, access control, criminal investigations, law enforcement, and others. However face biometric systems can be highly vulnerable to spoofing attacks where an impostor tries to bypass the face recognition system using a photo or video sequence. In this paper a novel liveness detection method, based on the 3D structure of the face, is proposed. Processing the 3D curvature of the acquired data, the proposed approach allows a biometric system to distinguish a real face from a photo, increasing the overall performance of the system and reducing its vulnerability. In order to test the real capability of the methodology a 3D face database has been collected simulating spoofing attacks, therefore using photographs instead of real faces. The experimental results show the effectiveness of the proposed approach.

Metabolomic analysis characterizes tissue specific indomethacin-induced metabolic perturbations of rats

In this study, the promising metabolomic approach integrating with ingenuity pathway analysis (IPA) was applied to characterize the tissue specific metabolic perturbation of rats that was induced by indomethacin. The selective pattern recognition analyses were applied to analyze global metabolic profiling of urine of rats treated by indomethacin at an acute dosage of reference that has been proven to induce tissue disorders in rats, evaluated throughout the time-course of -24-72 h. The results preliminarily revealed that modifications of amino acid metabolism, fatty acid metabolism and energetically associated metabolic pathways accounted for metabolic perturbation of the rats that was induced by indomethacin. Furthermore, IPA was applied to deeply analyze the biomarkers and their relations with the metabolic perturbations evidenced by pattern recognition analyses. Specific biochemical functions affected by indomethacin suggested that there is an important correlation of its effects in kidney and liver metabolism, based on the determined metabolites and their pathway-based analysis. The IPA correlation of the three major biomarkers, identified as creatinine, prostaglandin E2 and guanosine, suggested that the administration of indomethacin induced certain levels of toxicity in the kidneys and liver. The changes in the levels of biomarker metabolites allowed the phenotypical determination of the metabolic perturbations induced by indomethacin in a time-dependent manner.

Ingenuity pathways analysis of urine metabonomics phenotypes toxicity of gentamicin in multiple organs

We introduce the use of Ingenuity Pathway Analysis to analyzing global metabonomics in order to characterize phenotypically biochemical perturbations and the potential mechanisms of the gentamicin-induced toxicity in multiple organs. A single dose of gentamicin was administered to Sprague Dawley rats (200 mg/kg, n = 6) and urine samples were collected at -24-0 h pre-dosage, 0-24, 24-48, 48-72 and 72-96 h post-dosage of gentamicin. The urine metabonomics analysis was performed by UPLC/MS, and the mass spectra signals of the detected metabolites were systematically deconvoluted and analyzed by pattern recognition analyses (Heatmap, PCA and PLS-DA), revealing a time-dependency of the biochemical perturbations induced by gentamicin toxicity. As result, the holistic metabolome change induced by gentamicin toxicity in the animal's organisms was characterized. Several metabolites involved in amino acid metabolism were identified in urine, and it was confirmed that gentamicin biochemical perturbations can be foreseen from these biomarkers. Notoriously, it was found that gentamicin induced toxicity in multiple organs system in the laboratory rats. The proof-of-knowledge based Ingenuity Pathway Analysis revealed gentamicin induced liver and heart toxicity, along with the previously known toxicity in kidney. The metabolites creatine, nicotinic acid, prostaglandin E2, and cholic acid were identified and validated as phenotypic biomarkers of gentamicin induced toxicity. Altogether, the significance of the use of metabonomics analyses in the assessment of drug toxicity is highlighted once more; furthermore, this work demonstrated the powerful predictive potential of the Ingenuity Pathway Analysis to study of drug toxicity and its valuable complementation for metabonomics based assessment of the drug toxicity.

Plasma metabolomics reveals biomarkers of the atherosclerosis

Atherosclerotic cardiovascular disease remains the leading cause of morbidity and mortality in industrialized societies. The lack of metabolite biomarkers has impeded the clinical diagnosis of atherosclerosis so far. In this study, stable atherosclerosis patients (n=16) and age- and sex-matched non-atherosclerosis healthy subjects (n=28) were recruited from the local community (Harbin, P. R. China). The plasma was collected from each study subject and was subjected to metabolomics analysis by GC/MS. Pattern recognition analyses (principal components analysis, orthogonal partial least-squares discriminate analysis, and hierarchical clustering analysis) commonly demonstrated plasma metabolome, which was significantly different from atherosclerotic and non-atherosclerotic subjects. The development of atherosclerosis-induced metabolic perturbations of fatty acids, such as palmitate, stearate, and 1-monolinoleoylglycerol, was confirmed consistent with previous publication, showing that palmitate significantly contributes to atherosclerosis development via targeting apoptosis and inflammation pathways. Altogether, this study demonstrated that the development of atherosclerosis directly perturbed fatty acid metabolism, especially that of palmitate, which was confirmed as a phenotypic biomarker for clinical diagnosis of atherosclerosis.

An ontology-based method for sparsity problem in tag recommendation

Tags or personal metadata for annotating web resources have been widely adopted in Web 2.0 sites. However, as tags are freely chosen by users, the vocabularies are diverse, ambiguous and sometimes only meaningful to individuals. Tag recommenders may assist users during tagging process. Its objective is to suggest relevant tags to use as well as to help consolidating vocabulary in the systems. In this paper we discuss our approach for providing personalized tag recommendation by making use of existing domain ontology generated from folksonomy. Specifically we evaluated the approach in sparse situation. The evaluation shows that the proposed ontology-based method has improved the accuracy of tag recommendation in this situation.

A combined method for mitigating sparsity problem in tag recommendation

Tag recommendation is a specific recommendation task for recommending metadata (tag) for a web resource (item) during user annotation process. In this context, sparsity problem refers to situation where tags need to be produced for items with few annotations or for user who tags few items. Most of the state of the art approaches in tag recommendation are rarely evaluated or perform poorly under this situation. This paper presents a combined method for mitigating sparsity problem in tag recommendation by mainly expanding and ranking candidate tags based on similar items’ tags and existing tag ontology. We evaluated the approach on two public social bookmarking datasets. The experiment results show better accuracy for recommendation in sparsity situation over several state of the art methods.

Novel NOD2 haplotype strengthens the association between TLR4 Asp299gly and Crohnʼs disease in an Australian population

Background:: The first major Crohn's disease (CD) susceptibility gene, NOD2, implicates the innate intestinal immune system and other pattern recognition receptors in the pathogenesis of this chronic, debilitating disorder. These include the Toll‐like receptors, specifically TLR4 and TLR5. A variant in the TLR4 gene (A299G) has demonstrated variable association with CD. We aimed to investigate the relationship between TLR4 A299G and TLR5 N392ST, and an Australian inflammatory bowel disease cohort, and to explore the strength of association between TLR4 A299G and CD using global meta‐analysis. Methods:: Cases (CD = 619, ulcerative colitis = 300) and controls (n = 360) were genotyped for TLR4 A299G, TLR5 N392ST, and the 4 major NOD2 mutations. Data were interrogated for case‐control analysis prior to and after stratification by NOD2 genotype. Genotype–phenotype relationships were also sought. Meta‐analysis was conducted via RevMan. Results:: The TLR4 A299G variant allele showed a significant association with CD compared to controls (P = 0.04) and a novel NOD2 haplotype was identified which strengthened this (P = 0.003). Furthermore, we identified that TLR4 A299G was associated with CD limited to the colon (P = 0.02). In the presence of the novel NOD2 haplotype, TLR4 A299G was more strongly associated with colonic disease (P < 0.001) and nonstricturing disease (P = 0.009). A meta‐analysis of 11 CD cohorts identified a 1.5‐fold increase in risk for the variant TLR4 A299G allele (P < 0.00001). Conclusions:: TLR 4 A299G appears to be a significant risk factor for CD, in particular colonic, nonstricturing disease. Furthermore, we identified a novel NOD2 haplotype that strengthens the relationship between TLR4 A299G and these phenotypes.

TLR2, but Not TLR4, is required for effective host defence against Chlamydia respiratory tract infection in early life

Chlamydia pneumoniae commonly causes respiratory tract infections in children, and epidemiological investigations strongly link infection to the pathogenesis of asthma. The immune system in early life is immature and may not respond appropriately to pathogens. Toll-like receptor (TLR)2 and 4 are regarded as the primary pattern recognition receptors that sense bacteria, however their contribution to innate and adaptive immunity in early life remains poorly defined. We investigated the role of TLR2 and 4 in the induction of immune responses to Chlamydia muridarum respiratory infection, in neonatal wild-type (Wt) or TLR2-deficient (−/−), 4−/− or 2/4−/− BALB/c mice. Wt mice had moderate disease and infection. TLR2−/− mice had more severe disease and more intense and prolonged infection compared to other groups. TLR4−/− mice were asymptomatic. TLR2/4−/− mice had severe early disease and persistent infection, which resolved thereafter consistent with the absence of symptoms in TLR4−/− mice. Wt mice mounted robust innate and adaptive responses with an influx of natural killer (NK) cells, neutrophils, myeloid (mDCs) and plasmacytoid (pDCs) dendritic cells, and activated CD4+ and CD8+ T-cells into the lungs. Wt mice also had effective production of interferon (IFN)γ in the lymph nodes and lung, and proliferation of lymph node T-cells. TLR2−/− mice had more intense and persistent innate (particularly neutrophil) and adaptive cell responses and IL-17 expression in the lung, however IFNγ responses and T-cell proliferation were reduced. TLR2/4−/− mice had reduced innate and adaptive responses. Most importantly, neutrophil phagocytosis was impaired in the absence of TLR2. Thus, TLR2 expression, particularly on neutrophils, is required for effective control of Chlamydia respiratory infection in early life. Loss of control of infection leads to enhanced but ineffective TLR4-mediated inflammatory responses that prolong disease symptoms. This indicates that TLR2 agonists may be beneficial in the treatment of early life Chlamydia infections and associated diseases.

The heterogeneous cluster ensemble method using hubness for clustering text documents

We propose a cluster ensemble method to map the corpus documents into the semantic space embedded in Wikipedia and group them using multiple types of feature space. A heterogeneous cluster ensemble is constructed with multiple types of relations i.e. document-term, document-concept and document-category. A final clustering solution is obtained by exploiting associations between document pairs and hubness of the documents. Empirical analysis with various real data sets reveals that the proposed meth-od outperforms state-of-the-art text clustering approaches.

Pre-crash traffic flow trend analysis on motorways

Crashes on motorway contribute to a significant proportion (40-50%) of non-recurrent motorway congestions. Hence reduce crashes will help address congestion issues (Meyer, 2008). Crash likelihood estimation studies commonly focus on traffic conditions in a Short time window around the time of crash while longer-term pre-crash traffic flow trends are neglected. In this paper we will show, through data mining techniques, that a relationship between pre-crash traffic flow patterns and crash occurrence on motorways exists, and that this knowledge has the potential to improve the accuracy of existing models and opens the path for new development approaches. The data for the analysis was extracted from records collected between 2007 and 2009 on the Shibuya and Shinjuku lines of the Tokyo Metropolitan Expressway in Japan. The dataset includes a total of 824 rear-end and sideswipe crashes that have been matched with traffic flow data of one hour prior to the crash using an incident detection algorithm. Traffic flow trends (traffic speed/occupancy time series) revealed that crashes could be clustered with regards of the dominant traffic flow pattern prior to the crash. Using the k-means clustering method allowed the crashes to be clustered based on their flow trends rather than their distance. Four major trends have been found in the clustering results. Based on these findings, crash likelihood estimation algorithms can be fine-tuned based on the monitored traffic flow conditions with a sliding window of 60 minutes to increase accuracy of the results and minimize false alarms.