Multi-modal abnormality detection in video with unknown data segmentation

This paper examines a new problem in large scale stream data: abnormality detection which is localized to a data segmentation process. Unlike traditional abnormality detection methods which typically build one unified model across data stream, we propose that building multiple detection models focused on different coherent sections of the video stream would result in better detection performance. One key challenge is to segment the data into coherent sections as the number of segments is not known in advance and can vary greatly across cameras; and a principled way approach is required. To this end, we first employ the recently proposed infinite HMM and collapsed Gibbs inference to automatically infer data segmentation followed by constructing abnormality detection models which are localized to each segmentation. We demonstrate the superior performance of the proposed framework in a real-world surveillance camera data over 14 days.

Resisting web proxy-based HTTP attacks by temporal and spatial locality behavior

A novel server-side defense scheme is proposed to resist the Web proxy-based distributed denial of service attack. The approach utilizes the temporal and spatial locality to extract the behavior features of the proxy-to-server traffic, which makes the scheme independent of the traffic intensity and frequently varying Web contents. A nonlinear mapping function is introduced to protect weak signals from the interference of infrequent large values. Then, a new hidden semi-Markov model parameterized by Gaussian-mixture and Gamma distributions is proposed to describe the time-varying traffic behavior of Web proxies. The new method reduces the number of parameters to be estimated, and can characterize the dynamic evolution of the proxy-to-server traffic rather than the static statistics. Two diagnosis approaches at different scales are introduced to meet the requirement of both fine-grained and coarse-grained detection. Soft control is a novel attack response method proposed in this work. It converts a suspicious traffic into a relatively normal one by behavior reshaping rather than rudely discarding. This measure can protect the quality of services of legitimate users. The experiments confirm the effectiveness of the proposed scheme.

Motion trajectory analysis for evaluating the performance of functional upper extremity tasks in daily living : a pilot study

Since 1998, tele-rehabilitation has been extensively studied for its potential capacity of saving time and cost for both therapists and patients. However, one gap hindering the deployment of tele-rehabilitation service is the approach to evaluate the outcome after tele-rehabilitation exercises without the presence of professional clinicians. In this paper, we propose an approach to model jerky and jerky-free movement trajectories with hidden Markov models (HMMs). The HMMs are then utilised to identify the jerky characteristics in a motion trajectory, thereby providing the number and amplitude of jerky movements in the specific length of the trajectory. Eventually, the ability of performing functional upper extremity tasks can be evaluated by classifying the motion trajectory into one of the pre-defined ability levels by looking at the number and amplitude of jerky movements. The simulation experiment confirmed that the proposed method is able to correctly classify motion trajectories into various ability levels to a high degree.

Discovering semantics from multiple correlated time series stream

In this paper, we study a challenging problem of mining data generating rules and state transforming rules (i.e., semantics) underneath multiple correlated time series streams. A novel Correlation field-based Semantics Learning Framework (CfSLF) is proposed to learn the semantic. In the framework, we use Hidden Markov Random Field (HMRF) method to model relationship between latent states and observations in multiple correlated time series to learn data generating rules. The transforming rules are learned from corresponding latent state sequence of multiple time series based on Markov chain character. The reusable semantics learned by CfSLF can be fed into various analysis tools, such as prediction or anomaly detection. Moreover, we present two algorithms based on the semantics, which can later be applied to next-n step prediction and anomaly detection. Experiments on real world data sets demonstrate the efficiency and effectiveness of the proposed method. © Springer-Verlag 2013.

A general collaborative framework for modeling and perceiving distributed network behavior

Collaborative Anomaly Detection (CAD) is an emerging field of network security in both academia and industry. It has attracted a lot of attention, due to the limitations of traditional fortress-style defense modes. Even though a number of pioneer studies have been conducted in this area, few of them concern about the universality issue. This work focuses on two aspects of it. First, a unified collaborative detection framework is developed based on network virtualization technology. Its purpose is to provide a generic approach that can be applied to designing specific schemes for various application scenarios and objectives. Second, a general behavior perception model is proposed for the unified framework based on hidden Markov random field. Spatial Markovianity is introduced to model the spatial context of distributed network behavior and stochastic interaction among interconnected nodes. Algorithms are derived for parameter estimation, forward prediction, backward smooth, and the normality evaluation of both global network situation and local behavior. Numerical experiments using extensive simulations and several real datasets are presented to validate the proposed solution. Performance-related issues and comparison with related works are discussed.

A computational framework for nucleic acid sub-sequence identification

Identification of nucleic acid sub-sequences within larger background sequences is a fundamental need of the biology community. The applicability correlates to research studies looking for homologous regions, diagnostic purposes and many other related activities. This paper serves to detail the approaches taken leading to sub-sequence identification through the use of hidden Markov models and associated scoring optimisations. The investigation of techniques for locating conserved basal promoter elements correlates to promoter thus gene identification techniques. The case study centred on the TATA box basal promoter element, as such the background is a gene sequence with the TATA box the target. Outcomes from the research conducted, highlights generic algorithms for sub-sequence identification, as such these generic processes can be transposed to any case study where identification of a target sequence is required. Paths extending from the work conducted in this investigation have led to the development of a generic framework for the future applicability of hidden Markov models to biological sequence analysis in a computational context.

A pHMM-ANN based discriminative approach to promoter identification in prokaryote genomic contexts

The computational approach for identifying promoters on increasingly large genomic sequences has led to many false positives. The biological significance of promoter identification lies in the ability to locate true promoters with and without prior sequence contextual knowledge. Prior approaches to promoter modelling have involved artificial neural networks (ANNs) or hidden Markov models (HMMs), each producing adequate results on small scale identification tasks, i.e. narrow upstream regions. In this work, we present an architecture to support prokaryote promoter identification on large scale genomic sequences, i.e. not limited to narrow upstream regions. The significant contribution involved the hybrid formed via aggregation of the profile HMM with the ANN, via Viterbi scoring optimizations. The benefit obtained using this architecture includes the modelling ability of the profile HMM with the ability of the ANN to associate elements composing the promoter. We present the high effectiveness of the hybrid approach in comparison to profile HMMs and ANNs when used separately. The contribution of Viterbi optimizations is also highlighted for supporting the hybrid architecture in which gains in sensitivity (+0.3), specificity (+0.65) and precision (+0.54) are achieved over existing approaches.

Cost-effectiveness analysis of screening for lung cancer with low dose spiral CT (computed tomography) in the Australian setting

Introduction:
Low dose spiral computed tomography (CT) is a sensitive screening tool for lung cancer that is currently being evaluated in both non-randomised studies and randomised controlled trials.
Methods:
We conducted a quantitative decision analysis using a Markov model to determine whether, in the Australian setting, offering spiral CT screening for lung cancer to high risk individuals would be cost-effective compared with current practice. This exploratory analysis was undertaken predominantly from the perspective of the government as third-party funder. In the base-case analysis, the costs and health outcomes (life-years saved and quality-adjusted life years) were calculated in a hypothetical cohort of 10,000 male current smokers for two alternatives: (1) screen for lung cancer with annual CT for 5 years starting at age 60 year and treat those diagnosed with cancer or (2) no screening and treat only those who present with symptomatic cancer.
Results:
For male smokers aged 60–64 years, with an annual incidence of lung cancer of 552 per 100,000, the incremental cost-effectiveness ratio was $57,325 per life-year saved and $105,090 per QALY saved. For females aged 60–64 years with the same annual incidence of lung cancer, the cost-effectiveness ratio was $51,001 per life-year saved and $88,583 per QALY saved. The model was used to examine the relationship between efficacy in terms of the expected reduction in lung cancer mortality at 7 years and cost-effectiveness. In the base-case analysis lung cancer mortality was reduced by 27% and all cause mortality by 2.1%. Changes in the estimated proportion of stage I cancers detected by screening had the greatest impact on the efficacy of the intervention and the cost-effectiveness. The results were also sensitive to assumptions about the test performance characteristics of CT scanning, the proportion of lung cancer cases overdiagnosed by screening, intervention rates for benign disease, the discount rate, the cost of CT, the quality of life in individuals with early stage screen-detected cancer and disutility associated with false positive diagnoses. Given current knowledge and practice, even under favourable assumptions, reductions in lung cancer mortality of less than 20% are unlikely to be cost-effective, using a value of $50,000 per life-year saved as the threshold to define a “cost-effective” intervention.
Conclusion:
The most feasible scenario under which CT screening for lung cancer could be cost-effective would be if very high-risk individuals are targeted and screening is either highly effective or CT screening costs fall substantially.

Options for change in the Australian cervical cancer screening program in context of HPV detection and vaccination

Introduction:
Cervical cancer screening has been implemented for over a decade in Australia and has significantly reduced the mortality and morbidity of the disease. The emergence of new technologies for cervical cancer, such as the Human Papillomavirus (HPV) vaccine and DNA testing has encouraged debate regarding the effective use of resources in cervical cancer prevention. The present study evaluates the cost-effectiveness, from a health sector perspective, of various screening strategies in the era of these new technologies.

Methods:
A stochastic epidemiological model using a discrete event and continuous algorithm was developed to describe the natural history of cervical cancer. By allowing one member of the cohort into the model at a time, this micro-simulation model encompasses the characteristics of heterogeneity and can track individual life histories. To evaluate the cost-effectiveness of the HPV vaccine a Markov model was built to simulate the effect on the incidence of HPV and subsequent cervical cancer. A number of proposed screening strategies were evaluated with the stochastic model for the application of HPV DNA testing, with changes in the screening interval and target population. Health outcomes were measured by Disability-Adjusted Life-Years (DALYs), adjusted for application within an evaluation setting (i.e. the mortality component of the DALY was adjusted by a disability weight when early mortality due to cervical cancer is avoided). Costs in complying with the Australian updated guidelines were assessed by pathway analysis to estimate the resources associated with cervical cancer and its pre-cancerous lesion treatment. Sensitivity analyses were performed to investigate the key parameters that influenced the cost-effectiveness results.

Results:
Current practice has already brought huge health gain by preventing more than 4,000 deaths and saving more than 86,000 life-years in a cohort of a million women. Any of the alternative screening strategies alter the total amount of health gain by a small margin compared to current practice. The results of incremental analyses of the alternative screening strategies compared to current practice suggest the adoption of the HPV DNA test as a primary screening tool every 3 years commencing at age 18, or the combined pap smear/HPV test every 3 years commencing at age 25, are more costly than current practice but with reasonable ICERs (AUD$1,810 per DALY and AUD$18,600 per DALY respectively). Delaying commencement of Pap test screening to age 25 is less costly than current practice, but involves considerable health loss. The sensitivity analysis shows, however, that the screening test accuracy has a significant impact on these conclusions. Threshold analysis indicates that a sensitivity ranging from 0.80 to 0.86 for the combined test in women younger than 30 is required to produce an acceptable incremental cost-effectiveness ratio.

Conclusions:
The adoption of HPV and combined test with an extended screening interval is more costly but affordable, resulting in reasonable ICERs. They appear good value for money for the Australian health care system, but need more information on test accuracy to make an informed decision. Potential screening policy change under current Australian HPV Vaccination Program is current work in progress. A Markov model is built to simulate the effect on the incidence of HPV and subsequent cervical cancer. Adoption of HPV DNA test as a primary screening tool in the context of HPV vaccination is under evaluation.

Modelling the public health consequences of cannabis use in Australia

Aims: To detail and validate a simulation model that describes the dynamics of cannabis use, including its probable causal relationships with schizophrenia, road traffic accidents (RTA) and heroin/poly-drug use (HPU).

Methods: A Markov model with 17 health-states was constructed. Annual cycles were used to simulate the initiation of cannabis use, progression in use, reduction and complete remission. The probabilities of transition between health-states were derived from observational data. Following 10-year-old Australian children for 90 years, the model estimated age-specific prevalence for cannabis use. By applying the relative risks according to the extent of cannabis use, the age-specific prevalence of schizophrenia and HPU, and the annual RTA incidence and fatality rate were also estimated. Predictive validity of the model was tested by comparing modelled outputs with data from other credible sources. Sensitivity and scenario analyses were conducted to evaluate technical validity and face validity.

Results: The estimated cannabis use prevalence in individuals aged 10-65 years was 12.2% which comprised 27.4% weekly and 18.0% daily users. The modelled prevalence and age profile were comparable to the reported cross-sectional data. The model also provided good approximations to the prevalence of schizophrenia (Modelled: 4.75/1,000 persons vs Observed: 4.6/1,000 persons), HPU (3.2/1,000 vs 3.1/1,000) and the RTA fatality rate (8.1 per 100,000 vs 8.2 per 100,000). Sensitivity analyses and scenario analysis provided expected and explainable trends.

Conclusions: The validated model provides a valuable tool to assess the likely effectiveness and cost-effectiveness of interventions designed to affect patterns of cannabis use. It can be updated as new data becomes available and/or applied to other countries.

Cost-effectiveness analysis of a two-stage screening intervention for hepatocellular carcinoma in Taiwan

Background/Purpose

Hepatocellular carcinoma (HCC) has been the leading cause of cancer death in Taiwan since the 1980s. A two-stage screening intervention was introduced in 1996 and has been implemented in a limited number of hospitals. The present study assessed the costs and health outcomes associated with the introduction of screening intervention, from the perspective of the Taiwanese government. The cost-effectiveness analysis aimed to assist informed decision making by the health authority in Taiwan.
Methods

A two-phase economic model, 1-year decision analysis and a 60-year Markov simulation, was developed to conceptualize the screening intervention within current practice, and was compared with opportunistic screening alone. Incremental analyses were conducted to compare the incremental costs and outcomes associated with the introduction of the intervention. Sensitivity analyses were performed to investigate the uncertainties that surrounded the model.
Results

The Markov model simulation demonstrated an incremental cost-effectiveness ratio (ICER) of NT$498,000 (US$15,600) per life-year saved, with a 5% discount rate. An ICER of NT$402,000 (US$12,600) per quality-adjusted life-year was achieved by applying utility weights. Sensitivity analysis showed that excess mortality reduction of HCC by screening and HCC incidence rates were the most influential factors on the ICERs. Scenario analysis also indicated that expansion of the HCC screening intervention by focusing on regular monitoring of the high-risk individuals could achieve a more favorable result.
Conclusion

Screening the population of high-risk individuals for HCC with the two-stage screening intervention in Taiwan is considered potentially cost-effective compared with opportunistic screening in the target population of an HCC endemic area.

Molecular genetics and comparative genomics reveal RNAi is not functional in malaria parasites

Techniques for targeted genetic disruption in Plasmodium, the causative agent of malaria, are currently intractable for those genes that are essential for blood stage development. The ability to use RNA interference (RNAi) to silence gene expression
would provide a powerful means to gain valuable insight into the pathogenic blood stages but its functionality in Plasmodium remains controversial. Here we have used various RNA-based gene silencing approaches to test the utility of RNAi in malaria
parasites and have undertaken an extensive comparative genomics search using profile hidden Markov models to clarify whether RNAi machinery
exists in malaria. These investigative approaches revealed that Plasmodium lacks the enzymology required for RNAi-based ablation of gene expression
and indeed no experimental evidence for RNAi was observed. In its absence, the most likely explanations for previously reported RNAi-mediated knockdown are either the general toxicity of introduced RNA (with global down-regulation of gene expression) or a specific antisense effect mechanistically distinct from RNAi, which will need systematic
analysis if it is to be of use as a molecular genetic tool for malaria parasites.

Browsing behavior mimicking attacks on popular web sites for large botnets

With the significant growth of botnets, application layer DDoS attacks are much easier to launch using large botnet, and false negative is always a problem for intrusion detection systems in real practice. In this paper, we propose a novel application layer DDoS attack tool, which mimics human browsing behavior following three statistical distributions, the Zipf-like distribution for web page popularity, the Pareto distribution for page request time interval for an individual browser, and the inverse Gaussian distribution for length of browsing path. A Markov model is established for individual bot to generate attack request traffic. Our experiments indicated that the attack traffic that generated by the proposed tool is pretty similar to the real traffic. As a result, the current statistics based detection algorithms will result high false negative rate in general. In order to counter this kind of attacks, we discussed a few preliminary solutions at the end of this paper.

Design of multiple sequence alignment algorithms on parallel, distributed memory supercomputers

The challenge of comparing two or more genomes that have undergone recombination and substantial amounts of segmental loss and gain has recently been addressed for small numbers of genomes. However, datasets of hundreds of genomes are now common and their sizes will only increase in the future. Multiple sequence alignment of hundreds of genomes remains an intractable problem due to quadratic increases in compute time and memory footprint. To date, most alignment algorithms are designed for commodity clusters without parallelism. Hence, we propose the design of a multiple sequence alignment algorithm on massively parallel, distributed memory supercomputers to enable research into comparative genomics on large data sets. Following the methodology of the sequential progressiveMauve algorithm, we design data structures including sequences and sorted k-mer lists on the IBM Blue Gene/P supercomputer (BG/P). Preliminary results show that we can reduce the memory footprint so that we can potentially align over 250 bacterial genomes on a single BG/P compute node. We verify our results on a dataset of E.coli, Shigella and S.pneumoniae genomes. Our implementation returns results matching those of the original algorithm but in 1/2 the time and with 1/4 the memory footprint for scaffold building. In this study, we have laid the basis for multiple sequence alignment of large-scale datasets on a massively parallel, distributed memory supercomputer, thus enabling comparison of hundreds instead of a few genome sequences within reasonable time.