Characterising an ECG signal using statistical modelling : a feasibility study

For clinical use, in electrocardiogram (ECG) signal analysis it is important to detect not only the centre of the P wave, the QRS complex and the T wave, but also the time intervals, such as the ST segment. Much research focused entirely on qrs complex detection, via methods such as wavelet transforms, spline fitting and neural networks. However, drawbacks include the false classification of a severe noise spike as a QRS complex, possibly requiring manual editing, or the omission of information contained in other regions of the ECG signal. While some attempts were made to develop algorithms to detect additional signal characteristics, such as P and T waves, the reported success rates are subject to change from person-to-person and beat-to-beat. To address this variability we propose the use of Markov-chain Monte Carlo statistical modelling to extract the key features of an ECG signal and we report on a feasibility study to investigate the utility of the approach. The modelling approach is examined with reference to a realistic computer generated ECG signal, where details such as wave morphology and noise levels are variable.

Online self-supervised multi-instance segmentation of dynamic objects

This paper presents a method for the continuous segmentation of dynamic objects using only a vehicle mounted monocular camera without any prior knowledge of the object’s appearance. Prior work in online static/dynamic segmentation is extended to identify multiple instances of dynamic objects by introducing an unsupervised motion clustering step. These clusters are then used to update a multi-class classifier within a self-supervised framework. In contrast to many tracking-by-detection based methods, our system is able to detect dynamic objects without any prior knowledge of their visual appearance shape or location. Furthermore, the classifier is used to propagate labels of the same object in previous frames, which facilitates the continuous tracking of individual objects based on motion. The proposed system is evaluated using recall and false alarm metrics in addition to a new multi-instance labelled dataset to evaluate the performance of segmenting multiple instances of objects.

Critical analysis of spam prevention techniques

E-mail spam has remained a scourge and menacing nuisance for users, internet and network service operators and providers, in spite of the anti-spam techniques available; and spammers are relentlessly circumventing these anti-spam techniques embedded or installed in form of software products on both client and server sides of both fixed and mobile devices to their advantage. This continuous evasion degrades the capabilities of these anti-spam techniques as none of them provides a comprehensive reliable solution to the problem posed by spam and spammers. Major problem for instance arises when these anti-spam techniques misjudge or misclassify legitimate emails as spam (false positive); or fail to deliver or block spam on the SMTP server (false negative); and the spam passes-on to the receiver, and yet this server from where it originates does not notice or even have an auto alert service to indicate that the spam it was designed to prevent has slipped and moved on to the receiver’s SMTP server; and the receiver’s SMTP server still fail to stop the spam from reaching user’s device and with no auto alert mechanism to inform itself of this inability; thus causing a staggering cost in loss of time, effort and finance. This paper takes a comparative literature overview of some of these anti-spam techniques, especially the filtering technological endorsements designed to prevent spam, their merits and demerits to entrench their capability enhancements, as well as evaluative analytical recommendations that will be subject to further research.

Classification of cancer-related death certificates using machine learning

Background Cancer monitoring and prevention relies on the critical aspect of timely notification of cancer cases. However, the abstraction and classification of cancer from the free-text of pathology reports and other relevant documents, such as death certificates, exist as complex and time-consuming activities. Aims In this paper, approaches for the automatic detection of notifiable cancer cases as the cause of death from free-text death certificates supplied to Cancer Registries are investigated. Method A number of machine learning classifiers were studied. Features were extracted using natural language techniques and the Medtex toolkit. The numerous features encompassed stemmed words, bi-grams, and concepts from the SNOMED CT medical terminology. The baseline consisted of a keyword spotter using keywords extracted from the long description of ICD-10 cancer related codes. Results Death certificates with notifiable cancer listed as the cause of death can be effectively identified with the methods studied in this paper. A Support Vector Machine (SVM) classifier achieved best performance with an overall F-measure of 0.9866 when evaluated on a set of 5,000 free-text death certificates using the token stem feature set. The SNOMED CT concept plus token stem feature set reached the lowest variance (0.0032) and false negative rate (0.0297) while achieving an F-measure of 0.9864. The SVM classifier accounts for the first 18 of the top 40 evaluated runs, and entails the most robust classifier with a variance of 0.001141, half the variance of the other classifiers. Conclusion The selection of features significantly produced the most influences on the performance of the classifiers, although the type of classifier employed also affects performance. In contrast, the feature weighting schema created a negligible effect on performance. Specifically, it is found that stemmed tokens with or without SNOMED CT concepts create the most effective feature when combined with an SVM classifier.

Treatment plan complexity metrics for predicting IMRT pre-treatment quality assurance results

The planning of IMRT treatments requires a compromise between dose conformity (complexity) and deliverability. This study investigates established and novel treatment complexity metrics for 122 IMRT beams from prostate treatment plans. The Treatment and Dose Assessor software was used to extract the necessary data from exported treatment plan files and calculate the metrics. For most of the metrics, there was strong overlap between the calculated values for plans that passed and failed their quality assurance (QA) tests. However, statistically significant variation between plans that passed and failed QA measurements was found for the established modulation index and for a novel metric describing the proportion of small apertures in each beam. The ‘small aperture score’ provided threshold values which successfully distinguished deliverable treatment plans from plans that did not pass QA, with a low false negative rate.

The genome of the domesticated apple (Malus × domestica Borkh.)

We report a high-quality draft genome sequence of the domesticated apple (Malus × domestica). We show that a relatively recent (>50 million years ago) genome-wide duplication (GWD) has resulted in the transition from nine ancestral chromosomes to 17 chromosomes in the Pyreae. Traces of older GWDs partly support the monophyly of the ancestral paleohexaploidy of eudicots. Phylogenetic reconstruction of Pyreae and the genus Malus, relative to major Rosaceae taxa, identified the progenitor of the cultivated apple as M. sieversii. Expansion of gene families reported to be involved in fruit development may explain formation of the pome, a Pyreae-specific false fruit that develops by proliferation of the basal part of the sepals, the receptacle. In apple, a subclade of MADS-box genes, normally involved in flower and fruit development, is expanded to include 15 members, as are other gene families involved in Rosaceae-specific metabolism, such as transport and assimilation of sorbitol.

Recursive actuator fault detection and diagnosis for emergency landing of UASs

This paper presents a practical recursive fault detection and diagnosis (FDD) scheme for online identification of actuator faults for unmanned aerial systems (UASs) based on the unscented Kalman filtering (UKF) method. The proposed FDD algorithm aims to monitor health status of actuators and provide indication of actuator faults with reliability, offering necessary information for the design of fault-tolerant flight control systems to compensate for side-effects and improve fail-safe capability when actuator faults occur. The fault detection is conducted by designing separate UKFs to detect aileron and elevator faults using a nonlinear six degree-of-freedom (DOF) UAS model. The fault diagnosis is achieved by isolating true faults by using the Bayesian Classifier (BC) method together with a decision criterion to avoid false alarms. High-fidelity simulations with and without measurement noise are conducted with practical constraints considered for typical actuator fault scenarios, and the proposed FDD exhibits consistent effectiveness in identifying occurrence of actuator faults, verifying its suitability for integration into the design of fault-tolerant flight control systems for emergency landing of UASs.

Methodology for developing real-time motorway traffic risk identification models using individual-vehicle data

Most of existing motorway traffic safety studies using disaggregate traffic flow data aim at developing models for identifying real-time traffic risks by comparing pre-crash and non-crash conditions. One of serious shortcomings in those studies is that non-crash conditions are arbitrarily selected and hence, not representative, i.e. selected non-crash data might not be the right data comparable with pre-crash data; the non-crash/pre-crash ratio is arbitrarily decided and neglects the abundance of non-crash over pre-crash conditions; etc. Here, we present a methodology for developing a real-time MotorwaY Traffic Risk Identification Model (MyTRIM) using individual vehicle data, meteorological data, and crash data. Non-crash data are clustered into groups called traffic regimes. Thereafter, pre-crash data are classified into regimes to match with relevant non-crash data. Among totally eight traffic regimes obtained, four highly risky regimes were identified; three regime-based Risk Identification Models (RIM) with sufficient pre-crash data were developed. MyTRIM memorizes the latest risk evolution identified by RIM to predict near future risks. Traffic practitioners can decide MyTRIM’s memory size based on the trade-off between detection and false alarm rates. Decreasing the memory size from 5 to 1 precipitates the increase of detection rate from 65.0% to 100.0% and of false alarm rate from 0.21% to 3.68%. Moreover, critical factors in differentiating pre-crash and non-crash conditions are recognized and usable for developing preventive measures. MyTRIM can be used by practitioners in real-time as an independent tool to make online decision or integrated with existing traffic management systems.

Why are early maturing girls less active? Links between pubertal development, psychological well-being, and physical activity among girls at ages 11 and 13

Previous research has shown that early maturing girls at age I I have lower subsequent physical activity at age 13 in comparison to later maturing girls. Possible reasons for this association have not been assessed. This study examines girls' psychological response to puberty and their enjoyment of physical activity as intermediary factors linking pubertal maturation and physical activity. Participants included 178 girls who were assessed at age 11, of whom 168 were reassessed at age 13. All participants were non-Hispanic white and resided in the US. Three measures of pubertal development were obtained at age I I including Tanner breast stage, estradiol levels, and mothers' reports of girls' development on the Pubertal Development Scale (PDS). Measures of psychological well-being at ages I I and 13 included depression, global self-worth, perceived athletic competence, maturation fears, and body esteem. At age 13, girls' enjoyment of physical activity was assessed using the Physical Activity Enjoyment Scale and their daily minutes of moderate-to-vigorous physical activity (MVPA) were assessed using objective monitoring. Structural Equation Modeling was used to assess direct and indirect pathways between pubertal development at age I I and MVPA at age 13. In addition to a direct effect of pubertal development on MVPA, indirect effects were found for depression, global self-worth and maturity fears controlling for covariates. In each instance, more advanced pubertal development at age I I was associated with lower psychological wellbeing at age 13, which predicted lower enjoyment of physical activity at age 13 and in turn lower MVPA. Results from this study suggest that programs designed to increase physical activity among adolescent girls should address the self-consciousness and discontent that girls' experience with their bodies during puberty, particularly if they mature earlier than their peers, and identify activities or settings that make differences in body shape less conspicuous.

An attack-localizing watermarking scheme for natural language documents

We present a text watermarking scheme that embeds a bitstream watermark Wi in a text document P preserving the meaning, context, and flow of the document. The document is viewed as a set of paragraphs, each paragraph being a set of sentences. The sequence of paragraphs and sentences used to embed watermark bits is permuted using a secret key. Then, English language sentence transformations are used to modify sentence lengths, thus embedding watermarking bits in the Least Significant Bits (LSB) of the sentences’ cardinalities. The embedding and extracting algorithms are public, while the secrecy and security of the watermark depends on a secret key K. The probability of False Positives is extremely small, hence avoiding incidental occurrences of our watermark in random text documents. Majority voting provides security against text addition, deletion, and swapping attacks, further reducing the probability of False Positives. The scheme is secure against the general attacks on text watermarks such as reproduction (photocopying, FAX), reformatting, synonym substitution, text addition, text deletion, text swapping, paragraph shuffling and collusion attacks.

Field validation of controlled Monte Carlo data generation for statistical damage identification employing Mahalanobis squared distance

This article presents the field applications and validations for the controlled Monte Carlo data generation scheme. This scheme was previously derived to assist the Mahalanobis squared distance–based damage identification method to cope with data-shortage problems which often cause inadequate data multinormality and unreliable identification outcome. To do so, real-vibration datasets from two actual civil engineering structures with such data (and identification) problems are selected as the test objects which are then shown to be in need of enhancement to consolidate their conditions. By utilizing the robust probability measures of the data condition indices in controlled Monte Carlo data generation and statistical sensitivity analysis of the Mahalanobis squared distance computational system, well-conditioned synthetic data generated by an optimal controlled Monte Carlo data generation configurations can be unbiasedly evaluated against those generated by other set-ups and against the original data. The analysis results reconfirm that controlled Monte Carlo data generation is able to overcome the shortage of observations, improve the data multinormality and enhance the reliability of the Mahalanobis squared distance–based damage identification method particularly with respect to false-positive errors. The results also highlight the dynamic structure of controlled Monte Carlo data generation that makes this scheme well adaptive to any type of input data with any (original) distributional condition.

Asymptotic minimax robust and misspecified Lorden quickest change detection for dependent stochastic processes

The quick detection of an abrupt unknown change in the conditional distribution of a dependent stochastic process has numerous applications. In this paper, we pose a minimax robust quickest change detection problem for cases where there is uncertainty about the post-change conditional distribution. Our minimax robust formulation is based on the popular Lorden criteria of optimal quickest change detection. Under a condition on the set of possible post-change distributions, we show that the widely known cumulative sum (CUSUM) rule is asymptotically minimax robust under our Lorden minimax robust formulation as a false alarm constraint becomes more strict. We also establish general asymptotic bounds on the detection delay of misspecified CUSUM rules (i.e. CUSUM rules that are designed with post- change distributions that differ from those of the observed sequence). We exploit these bounds to compare the delay performance of asymptotically minimax robust, asymptotically optimal, and other misspecified CUSUM rules. In simulation examples, we illustrate that asymptotically minimax robust CUSUM rules can provide better detection delay performance at greatly reduced computation effort compared to competing generalised likelihood ratio procedures.

On-the-fly web content integrity check boosts users' confidence

Web servers are accessible by anyone who can access the Internet. Although this universal accessibility is attractive for all kinds of Web-based applications, Web servers are exposed to attackers who may want to alter their contents. Alterations range from humorous additions or changes, which are typically easy to spot, to more sinister tampering, such as providing false or damaging information.

Robust numeric set watermarking : numbers don’t lie

Ever since Cox et. al published their paper, “A Secure, Robust Watermark for Multimedia” in 1996 [6], there has been tremendous progress in multimedia watermarking. The same pattern re-emerged with Agrawal and Kiernan publishing their work “Watermarking Relational Databases” in 2001 [1]. However, little attention has been given to primitive data collections with only a handful works of research known to the authors [11, 10]. This is primarily due to the absence of an attribute that differentiates marked items from unmarked item during insertion and detection process. This paper presents a distribution-independent, watermarking model that is secure against secondary-watermarking in addition to conventional attacks such as data addition, deletion and distortion. The low false positives and high capacity provide additional strength to the scheme. These claims are backed by experimental results provided in the paper.

Visualization of long-duration acoustic recordings of the environment

Acoustic recordings of the environment are an important aid to ecologists monitoring biodiversity and environmental health. However, rapid advances in recording technology, storage and computing make it possible to accumulate thousands of hours of recordings, of which, ecologists can only listen to a small fraction. The big-data challenge addressed in this paper is to visualize the content of long-duration audio recordings on multiple scales, from hours, days, months to years. The visualization should facilitate navigation and yield ecologically meaningful information. Our approach is to extract (at one minute resolution) acoustic indices which reflect content of ecological interest. An acoustic index is a statistic that summarizes some aspect of the distribution of acoustic energy in a recording. We combine indices to produce false-color images that reveal acoustic content and facilitate navigation through recordings that are months or even years in duration.