Some human dimensions of computer virus creation and infection Infection of computer systems by destructive computer viruses is a commonplace occurrence. Consequently, an extensive literature exists concerning the technical means of virus prevention, detection and disinfection. By contrast, in this paper we consider the human dimensions and implications behind the invention and release of computer viruses. We examine and discuss some possible conscious motivations: these include political, commercial and malicious. However, the paper is also concerned with unconscious motivations and goes on to look at possible meanings for these disruptive activities from within a psychodynamic framework based on the work of Melanie Klein. The paper draws upon previously published information about viruses and their makers in order to furnish material for these discussions. Of equal import in understanding the e!ect that virus infection has upon computer users. A personal anecdote illustrates the disruption to peace of mind brought about simply by the fear of virus infection. We conclude that virus creation means di!erent things for di!erent perpetrators, but that generally it is a destructive act aimed at dismantling what is apparently &whole' and satisfactory. This re#ects the reality that human life involves a constant struggle with processes of destructiveness as well as creativity. Paradoxically, the orderly, constructed world may become stronger through the process of learning and defending against each new virus, but this strengthening of defences may itself in#ame the problem. We conclude by considering some concrete consequences for computer users, and areas for future investigation. Computer viruses have become a common problem for computer users over the last decade, in fact it is claimed that 90% of companies experience a destructive virus attack each month (Taylor, 1997a) . Computer viruses were initially theorized by Cohen (1994) , and a re"nement of his work has recently been presented by Thimbleby, Anderson and Cairns (1998) . Both Cohen (1994) and Thimbleby (1990) have suggested that the mechanism of the computer virus can be employed for useful e!ect, for example in practical &housekeeping' tasks around a network of computers.. Unfortunately, this benign aspect of computer viruses has not been widely, if at all, exploited. The overwhelming majority of computer viruses are destructive in their e!ect. The damage is often done even when the virus does not have an explicit destructive &payload', since viruses often have unforeseen side-e!ects. The construction and propagation of such computer viruses is clearly a highly unethical practice. Their main e!ect is to disrupt and deny the resources of a computer or computer system to the people using it. This may result in considerable if not complete loss of work, and can have extreme emotional and "nancial consequences, or even safety or political consequences. Meckbach (1997) cites the case of an unnamed US &"nancial institution' shutting down for three days due to a virus infection, at a cost of $2.3 million in lost transactions. The US National Computer Security Association (NCSA) estimated that in 1996 the costs of viruses to US companies and organizations was between $2 billion and $3 billion, up from approximately $1 billion in 1995. Virus infections had increased almost 10-fold compared to 1995 (Kehoe, 1996) .. In 1997, an NCSA survey of 300 medium-and large-sized (Fortune 500) organizations in North America found that over 99% had encountered a virus within the previous six months ( Berg, 1997; Meckbach, 1997) . These organizations expected approximately 35 virus incidents per thousand PCs per month in 1997 (Berg, 1997) . The survey shows that the rate of infection is rising, with the number of virus encounters in January 1997 almost equal to the total number of encounters between July and December of 1996. This increase is not surprising given the rate at which new viruses are being invented and released.. It is noteworthy, however, that many of these &new' viruses are produced by making slight modi"cations to existing viruses. For example, the Stoned virus existed in a version which prints the message &legalise marijuana' rather than &legalize marijuana', and these are counted as two distinct viruses (Kane, 1994) . Kane goes on to note that many &new' but trivially altered viruses seem to satisfy their creators once they are o$cially noticed and recorded, the aim being to have one's virus entered on the &roll of honour'. This is akin to the gra$ti artist's &tag' being prominently displayed.. The 1997 NCSA survey found that the average amount of server downtime caused by a &virus disaster' (de"ned as the infection of 25 or more PCs) dropped from almost 6 h per incident in 1996 to 40 min in 1997 (Berg, 1997) . The survey implies that companies are learning to deal with viruses more e!ectively. The cost of recovery from a virus disaster averaged $8300, and the time taken for full recovery averaged 44 h. These "gures are similar for the previous year, 1996. However, the survey shows that the number of person-days needed to e!ect a complete recovery rose from 10 in 1996 to 22 in 1997. This may re#ect an increased ease of virus propagation through inter and intranets*more people need to respond in an incident, although each for a smaller amount of time. Thus, it seems that virus encounters have become routine. Kephart and White (1994) have noted the distinctive plateau phenomenon in the rate of infection by a given virus. However, distinctly greater disruption is caused when a virus embodies a new way of propagating itself before the speci"c anti-virus measures are in place (Ford, 1999) .. Several authors make the omnious point that once a virus has entered the public domain, it is a much simpler task to alter the existing virus rather than to invent a completely new one, and this implies ample scope for malicious augmentation of the 900 A. BISSETT AND G. SHIPTON destructive e!ects. Since 1992 virus-making kits have been available, and these are widely disseminated via Internet facilities (Hruska, 1998) .. By 1997, some 12 000 di!erent viruses had been launched (Meckbach, 1997) , and many more continue to be conceived and released, for it is generally held that there is no completely secure defence against them in the present environment. However, once a virus has been identi"ed then a means of combating it may be formulated. Production and distribution of anti-virus programs has therefore become a lucrative industry. Thimbleby (1994) has proposed a technical means of preventing the spread of computer viruses, by using common encryption techniques in a radical way. Unfortunately, his scheme appears unlikely to be adopted wholesale due to entrenched commercial interests and practices.. In the next section, we brie#y survey the di!erent types of virus and some conscious motives behind them. Using examples of viruses we survey the e!ects, both intended and unintended. In Section 3 we examine likely unconscious motivations behind virus creation and dissemination. We conclude that virus creation means di!erent things for di!erent perpetrators, but that generally it is a destructive act aimed at dismantling what is apparently &whole' and satisfactory to others, and that it enviously disrupts the holding relationship between a computer user and the computer. This re#ects the reality that we have to deal with destructiveness as well as creativity in ourselves and other people and the consequences of mass access to technological modes of operationalizing our plans. Paradoxically, in SOME HUMAN DIMENSIONS OF COMPUTER VIRUS CREATION AND INFECTION developing anti-virus measures, the constructed world becomes more forti"ed against each new virus, but freedom of information is then likely to be more curtailed, thus generally increasing the sense of alienation and, disenfranchizement that has exercised hackers in particular. Moreover, such practical defense measures will not alter the virus-makers' envy and sense of being excluded, and it may intensify their attacks and encourage a siege mentality in computer users which can be exploited by anti-virus product makers.

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Hybrid probabilities and error-domain structural identification using ambient vibration monitoring For the assessment of structural behavior, many approaches are available to compare model predictions with measurements. However, few approaches include uncertainties along with dependencies associated with models and observations. In this paper, an error-domain structural identification approach is proposed using ambient vibration monitoring (AVM) as the input. This approach is based on the principle that in science, data cannot truly validate an hypothesis, it can only be used to falsity it. Error-domain model falsification generates a space of possible model instances (combination of parameters), obtains predictions for each of them and then, rejects instances that have unlikely differences (residuals) between predictions and measurements. Models are filtered in a two step process. Firstly a comparison of mode shapes based on MAC criterion ensures that the same modes are compared. Secondly, the frequencies from each model instance are compared with the measurements. The instances for which the difference between the predicted and measured value lie outside threshold bounds are discarded. In order to include "uncertainty of uncertainty" in the identification process, a hybrid probability scheme is also presented. The approach is used for the identification of the Langensand Bridge in Switzerland. It is used to falsify the hypothesis that the bridge was behaving as designed when subjected to ambient vibration inputs, before opening to the traffic. Such small amplitudes may be affected by lowlevel bearing-device friction. This inadvertently increased the apparent stiffness of the structure by 17%. This observation supports the premiss that ambient vibration surveys In the early history of dynamic monitoring of bridges, researchers were interested in the modal properties of these structures. For instance Carder [11] , used monitoring to predict resonance during an earthquake. From the 70s, the development of recording capabilities (e.g. [27] ) and of numerical modelling lead researchers to validate bridge models using experimental ambient vibration data [1] . Later, researchers began optimizing model parameters to improve the fit between predicted and observed data.. Mottershead and Friswell [32] reviewed some of these applications. Since then, there has been an increasing popularity for Bayesian-updating methodologies [3, 4] . Many applications of dynamic monitoring of bridges can be found in the literature, for example [3, 9, 13, 14, 23, 26, 31, 33, 38, 40, 41, 46, 49] . Some of these studies, such. as [3] and [14] , explicitly included uncertainties in the identification process. These studies however did not include modelling uncertainties due to aspects such as model simplification, omissions and mesh refinement.. Error-domain model falsification was proposed by Goulet and Smith [19] for situations where bias in models introduce dependencies between predictions. This approach is based on the principle that in science, data cannot convincingly validate an hypothesis, it can only be used to falsity it [36, 45] . Error-domain model falsification is built upon more than a decade of research [37, 39, 43] . In a previous study [17] , a similar identification approach was used to perform structural identification on bridges based on static measurements. Error-domain identification was proposed to overcome the limitations of other approaches based on statistical inference that relies on the correct evaluation of uncertainties and of their dependencies [5, 44] . Error-domain model falsification generates a space of possible model instances (combination of parameters), obtains predictions and then falsifies wrong instances based on the difference between predictions and measurements (observed residual). A model instance is falsified, if the observed residuals lies outside threshold bounds computed from the combination of modelling and measurement uncertainties.. Goulet, J., Michel, C., and . Hybrid probabilities and error-domain structural identification using ambient vibration monitoring. Mechanical Systems and Signal Processing, 37(1-2):199-212.. Elements of error-domain model falsification is presented in Equations 1 to 3. A model class g( θ) is used to perform predictions based on vector containing n p input. If real quantities for these parameter are known θ * , the difference between the value predicted by the model and the modelling error model is equal to the physical quantity R for the system (see Equation 1 ). The difference between the measured value y and the measurement error measure is also equal to this physical quantity. In practical situations, neither errors nor the real physical quantity is available.. In Equation 2 , exact values of errors are replaced by a random variables U model and U measure each described by a probability density function (PDF) f U ( ) : R → R + .. The relation used to falsify models can be obtained by reordering the terms in Equation Error-domain structural identification can be performed using ambient vibration monitoring results as input. The model filtering process is carried out in two steps:. firstly a comparison of mode shapes based on MAC value is done to ensure that the same modes are compared; secondly, the frequencies from each model instance are compared with the measurements. The instances for which the difference between the predicted and measured value lie outside the threshold bounds are discarded.. This system identification method is able to show failures in the compatibility between predicted and recorded data. In the case-study presented here, the approach falsified the hypothesis that bearing devices allowed free longitudinal expansion of the structure. Under ambient vibration, the small amplitudes of deformations may not allow the bearing devices to move freely.. Including uncertainties in system identification process is important to avoid misinterpretation of the data. The hybrid probability scheme presented in this paper goes beyond traditional approaches by including uncertainties associated with uncertainty definition. It offers a compromise between conventional statistical descriptions and interval based approaches. This approach to structural identification is proposed as an alternative to more traditional methods, especially for methods that assume that the model class contains the right model.. Goulet, J., Michel, C., and . Hybrid probabilities and error-domain structural identification using ambient vibration monitoring. Mechanical Systems and Signal Processing, 37(1-2):199-212. Results indicate that under ambient vibrations, the bearings are partially blocked.. During the second measurement [50] , the higher noise level due to the traffic partially Goulet, J., Michel, C., and . Hybrid probabilities and error-domain structural identification using ambient vibration monitoring. Mechanical Systems and Signal Processing, 37(1-2):199-212.. frees the bearings and modifies noticeably the fundamental mode with a decrease in the resonance frequency and an increase in the damping ratio (Fig. 10 ).. Interpreting the data using an approach that adjusts the structural parameters of the structure in order to minimize the discrepancy between predicted and measured values is dangerous. If wrong assumptions are made at the beginning -for instance if the hypothesis that bearing devices do not work properly under the measured conditions is not included -wrong conclusions could be obtained. In the case of the approach presented here, when the comparison of model instances and measurements was first performed without the hypotheses of bearing device malfunction, all model instances were initially falsified. Such a situation indicated that the model class (template model). used was an inadequate explanation of the observations. The capacity to falsify an entire model class is a unique advantage of this approach.

O O O O B I O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


An efficient rate control algorithm for a wavelet video codec Rate control plays an essential role in video coding and transmission to provide the best video quality at the receiver's end given the constraint of certain network conditions. In 17 this paper, a rate control algorithm using the Quality Factor (QF ) optimization method is proposed for the wavelet-based video codec and implemented on an open source Dirac 19 video encoder. A mathematical model which we call Rate-QF (R − QF ) model is derived to generate the optimum QF for the current coding frame according to the target bitrate.The proposed algorithm is a complete one pass process and does not require complex mathematical calculation. The process of calculating the QF is quite simple and further 23 calculation is not required for each coded frame. The experimental results show that the proposed algorithm can control the bitrate precisely (within 1% of target bitrate in 25 average). Moreover, the variation of bitrate over each Group of Pictures (GOPs) is lower than that of H.264. This is an advantage in preventing the buffer overflow and underflowfor real-time multimedia data streaming.

O O B I O O O B O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Savannah woody structure modelling and mapping using multi-frequency (X-, C- and L-band) Synthetic Aperture Radar data Abstract Structural parameters of the woody component in African savannahs provide estimates of carbon stocks that are vital to the understanding of fuelwood reserves, which is the primary source of energy for 90% of households in South Africa (80% in Sub-Saharan Africa) and are at risk of over utilisation. The woody component can be characterised by various quantifiable woody structural parameters, such as tree cover, tree height, above ground biomass (AGB) or canopy volume, each been useful for different purposes. In contrast to the limited spatial coverage of ground-based approaches, remote sensing has the ability to sense the high spatio-temporal variability of e.g. woody canopy height, cover and biomass, as well as species diversity and phenological status – a defining but challenging set of characteristics typical of African savannahs. Active remote sensing systems (e.g. Light Detection and Ranging – LiDAR; Synthetic Aperture Radar – SAR), on the other hand, may be more effective in quantifying the savannah woody component because of their ability to sense within-canopy properties of the vegetation and its insensitivity to atmosphere and clouds and shadows. Additionally, the various components of a particular target’s structure can be sensed differently with SAR depending on the frequency or wavelength of the sensor being utilised. This study sought to test and compare the accuracy of modelling, in a Random Forest machine learning environment, woody above ground biomass (AGB), canopy cover (CC) and total canopy volume (TCV) in South African savannahs using a combination of X-band (TerraSAR-X), C-band (RADARSAT-2) and L-band (ALOS PALSAR) radar datasets. Training and validation data were derived from airborne LiDAR data to evaluate the SAR modelling accuracies. It was concluded that the L-band SAR frequency was more effective in the modelling of the CC (coefficient of determination or R 2 of 0.77), TCV ( R 2 of 0.79) and AGB ( R 2 of 0.78) metrics in Southern African savannahs than the shorter wavelengths (X- and C-band) both as individual and combined (X + C-band) datasets. The addition of the shortest wavelengths also did not assist in the overall reduction of prediction error across different vegetation conditions (e.g. dense forested conditions, the dense shrubby layer and sparsely vegetated conditions). Although the integration of all three frequencies (X + C + L-band) yielded the best overall results for all three metrics ( R 2 = 0.83 for CC and AGB and R 2 = 0.85 for TCV), the improvements were noticeable but marginal in comparison to the L-band alone. The results, thus, do not warrant the acquisition of all three SAR frequency datasets for tree structure monitoring in this environment. After reviewing all the modelling and error assessment results, it can be concluded the Lband SAR frequency was more effective in the modelling of the CC, TCV and AGB metrics in Southern African savannahs than the shorter wavelengths (X-and C-band) both as individual and combined (X+C-band) datasets. Although the integration of all three frequencies (X+C+L-band) yielded the best overall results for all three metrics, the improvements were noticeable but marginal in comparison to the L-band alone. The results do not warrant the acquisition of all three SAR frequency datasets for tree structure monitoring. Further the addition of the shortest wavelengths did not assist in the overall reduction of prediction error specifically of the shrubby layer as hypothesized. With the imminent launch of the ALOS PALSAR-2 L-band sensor, the use of such L-band based models will be critical for future accurate tree structure modelling and monitoring at the regional and provincial scale.. The modelling results obtained from the C-band SAR frequency alone, however, does yield promising results which would make the implementation of similar models to the free data As a way forward beyond this study, in order to reduce the error experienced in the AGB results (at field collection, LiDAR and SAR levels), new and more robust savannah tree allometric equations, with a greater range of representative tree stem and height sizes, will need to be produced but such efforts will require extensive ground level harvesting campaigns. Due to the success of this study, particularly the positive results using L-band SAR data, future work will seek to up-scale these results to greater regional and provincial areas using more extensive LiDAR calibration and validation datasets. The assessment of data mining algorithms for modelling savannah woody cover using multi-frequency (X-, C-and L-band) synthetic aperture radar Where M = biomass in kg/Ha, D = Diameter above breast height (DBH) in cm, H = height of tree in metres and ƿ = mean wood specific gravity (fixed at a mean value of 0.9) which is unitless. This study investigated the accuracy of modelling and mapping above ground biomass (AGB), woody canopy cover (CC) and total canopy volume (TCV) in heterogeneous South. African savannahs using multi-frequency SAR datasets (X-band, C-band and L-band including their combinations). Various studies have implemented L-band SAR data for tree structural assessment in a savannah type environment (Carreiras et al., 2013 , Mitchard et al., 2012 but the use of shorter wavelengths, such as C-band, have also been proven to perform relatively well (Mathieu et al., 2013) . This study also served to compare the three SAR frequency datasets (X-, C-and L-band) in the same study region of Mathieu et al. (2013) and is the first attempt in an African Savannah context. It was hypothesized that the shorter SAR wavelengths (e.g. X-band, C-band), since interacting with the finer woody plant elements (e.g. branchlets) would be useful for mapping the shrubby/thicket layer while the longer SAR wavelengths (e.g. L-band) would interact with larger vegetation elements such as major branches and trunks typical of forested areas (Vollrath, 2010; Mitchard et al, 2009 ). It was thus proposed that the combination of these different SAR frequencies would provide a better assessment of the savannah woody element than the individual SAR frequencies , 2003) . Further investigation will need to be done to ascertain the exact cause of these results but these results, however, advocate the suitability of the L-band for analysing dense forested environments and thus confirms the second part of the main hypothesis which stated that the L-band SAR signal interacts with the major tree structural components (e.g. trunk and main branches typical of forested areas) (Lucas et al., 2006 , Carreiras et al., 2013 , Mitchard et al., 2012 .. Among the three structural metrics, TCV was consistently modelled with higher accuracies, amongst all seven SAR scenarios (table 2) . This result concurs with that of Mathieu et al. (2013) . TCV is a metric which indicates the volume of vegetation present within the vertical structure and its higher modelled accuracies could be attributed to the leaf-off conditions typical of the dry winter season which allowed for greater wave penetration into the canopy for all wavelengths, even the shorter wavelengths. CC and AGB metrics yielded similar R 2 values with a higher SEP values observed for AGB which may be due to the associated error propagated through the allometric equation and the LiDAR model (results of figure 4 ). Since SAR is a system which utilises penetrating radio waves, the SAR signals will be expected to be more related to 3D structural metrics such as TCV and AGB rather than to the 2D CC metric (which achieved marginally poorer modelled results). This is due to the fact that CC is a metric for which the 2D horizontal coverage fluctuates seasonally depending on the phenological state of the vegetation, at least in comparison to TCV and AGB, which relies on the 3D nature of the woody structure which includes height and is thus more consistent across seasons (in the absence of disturbance).

O B I O O O O B O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O B O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O B O O O O B O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O B B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O B O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O


A Blind Watermarking Scheme Using New Nontensor Product Wavelet Filter Banks Abstract-As an effective method for copyright protection of digital products against illegal usage, watermarking in wavelet domain has recently received considerable attention due to the desirable multiresolution property of wavelet transform. In general, images can be represented with different resolutions by the wavelet decomposition, analogous to the human visual system (HVS). Usually, human eyes are insensitive to image singularities revealed by different high frequency subbands of wavelet decomposed images. Hence, adding watermarks into these singularities will improve the imperceptibility that is a desired property of a watermarking scheme. That is, the capability for revealing singularities of images plays a key role in designing wavelet-based watermarking algorithms. Unfortunately, the existing wavelets have a limited ability in revealing singularities in different directions. This motivates us to construct new wavelet filter banks that can reveal singularities in all directions. In this paper, we utilize special symmetric matrices to construct the new nontensor product wavelet filter banks, which can capture the singularities in all directions. Empirical studies will show their advantages of revealing singularities in comparison with the existing wavelets. Based upon these new wavelet filter banks, we, therefore, propose a modified significant difference watermarking algorithm. Experimental results show its promising results.Index Terms-Nontensor product wavelet filter, singularities, watermarking.

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The Unification Space implemented as a localist neural net: predictions and error-tolerance in a constraint-based parser Abstract We introduce a novel computer implementation of the Unification-Space parser (Vosse and Kempen in Cognition 75:105-143, 2000) in the form of a localist neural network whose dynamics is based on interactive activation and inhibition. The wiring of the network is determined by Performance Grammar (Kempen and Harbusch in Verb constructions in German and Dutch. Benjamins, Amsterdam, 2003), a lexicalist formalism with feature unification as binding operation. While the network is processing input word strings incrementally, the evolving shape of parse trees is represented in the form of changing patterns of activation in nodes that code for syntactic properties of words and phrases, and for the grammatical functions they fulfill. The system is capable, at least qualitatively and rudimentarily, of simulating several important dynamic aspects of human syntactic parsing, including garden-path phenomena and reanalysis, effects of complexity (various types of clause embeddings), faulttolerance in case of unification failures and unknown words, and predictive parsing (expectation-based analysis, surprisal effects). English is the target language of the parser described. The Unification-Space model of parsing that we developed 10 years ago (henceforth U-Space2000; Vosse and Kempen 2000 ) is a dynamic model of syntactic parsing based on activation and inhibitory competition. Its dynamics enable it to simulate a considerable range of psycholinguistic phenomena related to the syntactic aspects of human sentence comprehension. In the decade that passed since its publication, two important developments took place that incited us to design an entirely new implementation. First, contrary to what we expected at the time of developing USpace2000, predictive syntactic parsing has convincingly been shown to be an important component of human sentence comprehension (Kamide and Mitchell 1999; Konieczny 2000; Hale 2003; Van Berkum et al. 2005; Pickering and Garrod 2007; Levy 2008) . Second, thanks to the ascent of novel neurophysiological research methods, there is a rapidly growing need for neural-network models of human cognition, including human syntactic parsing. Hence, we decided to embark on a project aiming at extending U-Space2000 with facilities for predictive parsing, and to re-implement it as a localist neural network. We expected, in addition, that this connectionist approach would bring nearer a parsing mechanism with the highly desirable but elusive property of ''graceful degradation.'' In particular, we aimed at a parser that, when confronted with an unknown word or an incorrectly inflected known word, does not halt immediately but attempts to guess the correct grammatical properties of that word. To our knowledge, no symbolic or connectionist model embodying these properties of human syntactic parsing has been implemented. (For pointers to recent literature on neurocomputational models of human sentence processing, we refer to van der Velde and de Kamps (2006) , beim Graben et al. (2008) , Mayberry et al. (2009 ), Huyck (2009 ; and several other articles in this special issue of Cognitive Neurodynamics.). How to represent, in a neural net with a fixed pattern of connections, not only the declarative lexical and syntactic knowledge underlying linguistic competence but also the syntactic structures that are assembled online in the course of the parsing process? Since we wanted the network to compute (some equivalent of) real parse trees, we could not start out from familiar neural network architectures published in the literature. These typically aim at performing more limited linguistic tasks, e.g., predicting the next word of a sentence. Simple Recurrent Nets (SRNs), in particular, have often been shown capable of learning such tasks, even if the training set consists of sentences generated by a relatively complex grammar (Elman 1990 (Elman , 1991 . It is far from certain, though, that this type of performance requires an internal representation of sentence structure (van der Velde et al. 2004) .. Therefore, we decided to start from scratch. Given that the U-Space2000 model centered around the notions of activation and competition, it should not come as a surprise that we ended up with a model that has some similarities with Interactive Activation and Competition (IAC) models which originated in the 1980s, for instance the TRACE model of auditory word recognition (McClelland and Elman 1986 ). The resulting system we dubbed SINUS, where US stands for Unification Space, N for Neural Network, I for IAC, and S for Simple or Semi (to bring out that it is anything but finished).. We start with an outline of the grammar formalism underlying the parsing system: Performance Grammar (PG; Harbusch and Kempen 2002; Kempen and Harbusch 2003) . PG defines the structures to be formed during parsing. Then, we sketch the dynamic aspects of the parsing process, which we adopt from U-Space2000: Activation spreading and competitive inhibition Kempen 2000, 2009 ). The pièce de résistance of the paper is the description of the neural re-implementation: the wiring and the activation flow in the SINUS network, and the training/optimization procedure used to set the 30? free parameters. Finally, we show that SINUS achieves the desired psycholinguistic effects, at least in a qualitative and rudimentary fashion, and conclude with some evaluative remarks and suggestions for improvements and extensions. We have shown that SINUS fulfills quite a few important criteria that any neurocognitive model of the syntactic aspects of human sentence comprehension should meet. We hasten to add, though, that the coverage of psycholinguistic phenomena is smaller than that of U-Space2000, the predecessor. This shortcoming is compensated, we belief, by the fact that SINUS has a higher level of neurocognitive plausibility (being able to represent the online construction of syntactic trees in the form of patterns of activation in a localist neural net), and that it can simulate, at least qualitatively and rudimentarily, several psycholinguistic phenomena that are beyond reach of U-Space2000, viz. graceful degradation (error-tolerance) and predictive parsing.. Despite these achievements, much remains to be desired. Most urgent is the addition of a parallel conceptual processing component-maybe operating on similar principles- Fig. 11 Expectation-based parsing of NP A sweet rabbit. The curves show the development of the pre-activation of the NP node (in the pre-activated column) during processing the Determiner (lower curve) and the Modifier (upper curve). The higher the pre-activation level of the NP node, the stronger the expectation of an upcoming NP lexical frame. This expectation is relatively weak while the Determiner is being processed, and gets stronger while processing the Modifier. The jagged shape of the curves is a consequence of oscillatory feedback from mutually inhibiting U-nodes that can evaluate syntactically proposed unifications against criteria of word meaning and world knowledge, and depending on the outcome, transmit excitatory or inhibitory signals to the U-nodes involved. Such a combination could help SINUS to succeed on longer sentences than the ones we have worked with thus far. Moreover, it would shift the criterion of success from delivery of a complete and grammatically correct parse tree for the input sentence to faithful reconstruction of the (or an) underlying communicative intention.. But staying within the syntactic domain, we would advocate a re-implementation where the relation between the topologies and the information represented there is less tightly linked to the information in the individual columns, so that linear-order constraints can have more impact. Presently, we have incipient ideas on how to accomplish this. Another fond wish is to extend the coverage of the parser with syntactic structures that-in terms of generative grammar-embody cross-clausal movement, as in Which elephant did you say hugged the dog? Performance Grammar analyzes these phenomena in terms of unification of left-peripheral slots in clausal topologies (Kempen and Harbusch 2003; Kempen 2009 ), but this part of PG has not yet been incorporated into SINUS. Finally, we would welcome attempts to implement SINUS-like parsers for languages other than English and German (as for the latter, see Vosse and Kempen 2008) , especially for non-Germanic languages that have been targeted in empirical sentence comprehension research.. Open Access This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Job shop scheduling with beam search Beam Search is a heuristic method for solving optimization problems. It is an adaptation of the branch and bound method in which only some nodes are evaluated in the search tree. At any level, only the promising nodes are kept for further branching and remaining nodes are pruned off permanently. In this paper, we develop a beam search based scheduling algorithm for the job shop problem. Both the makespan and mean tardiness are used as the performance measures. The proposed algorithm is also compared with other well known search methods and dispatching rules for a wide variety of problems. The results indicate that the beam search technique is a very competitive and promising tool which deserves further research in the scheduling literature. Beam search method was first used in artificial intelligence for the speech recognition problem (Lowerre, 1976) . There have been a number of applications reported in the literature since then. Fox (1983) used beam search for solving complex scheduling problems by a system called ISIS. Even through beam search has been used to solve a wide variety of optimization problems, its performance is not generally known for scheduling problems. Besides, it has not been thoroughly studies as a problem solving strategy with certain evaluation functions and search parameters. This paper attempts to systematically investigate the effectiveness and performance of the beam search method.. The job shop problem is to determine the start and completion time of operations of a set of jobs on a set of machines, subject to the constraints that each machine can handle at most one job at a time and each job has a specified processing order through the machines. Due to the NP-hard feature of this problem, the early studies focus on the development of effective priority dispatching rules. Later, more complex techniques like simulated annealing and Tabu search are applied to this field.

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Systematic searching for theory to inform systematic reviews: is it feasible? Is it desirable? AbstractBackground: In recognising the potential value of theory in understanding how interventions work comesa challenge – how to make identiﬁcation of theory less haphazard?Objectives: To explore the feasibility of systematic identiﬁcation of theory.Method: We searched PubMed for published reviews (1998–2012) that had explicitly sought to identifytheory. Systematic searching may be characterised by a structured question, methodological ﬁlters andan itemised search procedure. We constructed a template (BeHEMoTh – Behaviour of interest; Healthcontext; Exclusions; Models or Theories) for use when systematically identifying theory. The authorstested the template within two systematic reviews.Results: Of 34 systematic reviews, only 12 reviews (35%) reported a method for identifying theory.Nineteen did not specify how they identiﬁed studies containing theory. Data were unavailable for threereviews. Candidate terms include concept(s)/conceptual, framework(s), model(s), and theory/theories/theoretical. Information professionals must overcome inadequate reporting and the use of theory out ofcontext. The review team faces an additional concern in lack of ‘theory ﬁdelity’.Conclusions: Based on experience with two systematic reviews, the BeHEMoTh template and procedureoffers a feasible and useful approach for identiﬁcation of theory. Applications include realist synthesis,framework synthesis or review of complex interventions. The procedure requires rigorous evaluation.Keywords: bibliographic databases; database searching; information retrieval; literature searching; reviewand systematic searchKey Messages As systematic review methodologies become increasingly sophisticated, review teams are able to address more challenging types of review question. Domains such as public health, health policy and social care are frequently characterised by use of complex interventions. In turn, the design of complex interventions will likely be informed by application of one or more theories or models.1. A theory is 'a set of inter-related concepts, definitions and propositions that present a systematic view of events or situations by specifying relations among variables, to explain and predict the events or situations. The notion of generality, or broad application, is important'.2 A theory is made up of concepts, the 'building blocks of theory or the primary elements'.2 A framework is a structure for presenting those concepts, without necessarily preserving interrelationships between individual concepts. We can think of a model as being 'a generalised or hypothetical description used to analyse or explain something'.2 Increasingly logic models are used to explain how a complex intervention is thought to work. 3, 4 Identification of theory is important because researchers may use systematic reviews, variously, to generate, explain or test theory.5 If a review team fails to identify relevant theory, they may be unable to explain how an intervention, demonstrated to be effective, is believed to work. This may make it difficult to replicate an intervention in another context or to customise its active ingredients within a particular setting. Specifically, reviewers use theories6:. • To explain how the mechanisms of different interventions are related;. • To guide classification of interventions; and, at a practical level • To provide a framework against which data from primary studies are extracted, analysed and interpreted.. To illustrate, a review team working on brief interventions for alcohol misuse may identify 'relapse' as explaining why interventions are sometimes effective and, under different circumstances, ineffective. A theory that incorporates relapse, for example Bandura's self-efficacy theory,7 may help to differentiate interventions that specifically address a relapse phase from those that do not. Subsequently, when extracting data for their review, the review team may use a framework based on self-efficacy theory against which to extract contextual and effectiveness data, including data on relapse.. Systematic identification of theory has become time-critical given the emergence of review techniques that explicitly seek to identify, explore or validate theory. The prodigious growth in realist synthesis, where theory is used to explore elements critical to an intervention's success, testifies to this phenomenon. 8 Techniques such as framework synthesis are considerably enhanced when reviewers identify an a priori framework against which to extract and analyse data and, then, to organise results.9,10 As Alderson summarises:. 'The choice of theory, although often unacknowledged, shapes the way practitioners and researchers collect and interpret evidence '11 Some commentators contest whether it is feasible to identify theory systematically.12 However, alternatives lack rigour, feasibility or both. Alternatives include: 1 Drawing on the backgrounds, experience and resources of the review team and its advisors. 13 2 Noting 'formal theories' referenced in the literature as the reviewer reads each article.. Drawing on the backgrounds, experience and resources of the review team relies disproportionately on theories already known to the review team. The review team may be perceived as 'magicking' a theory out of thin air. Alternatively, if a review team only references theories encountered within included studies, they ignore a potential disconnect between the theoretical and empirical literatures. They require authors of empirical studies to identify and report potential connections between theory and practice. Furthermore, relevant theory may not be present in an 'index' intervention study but may lie in associated pilot studies, feasibility studies, process evaluations or commentaries ('sibling' studies).. To date, we have not identified any published studies detailing search methods for systematic identification of theory. Scoping searches did reveal, however, increasing numbers of published systematic reviews of theory. In the absence of cross-sectional literature survey of published reviews of theory. In surveying current practice within health research, including public health, we considered it neither necessary nor feasible to conduct a systematic review of reviews of theory. We sought to capture typical examples of such reviews. We focused our brief literature survey on the health database with the widest coverage (i.e. PUBMED MEDLINE).. The main objective of the study, therefore, was to explore the feasibility of systematically identifying theory for reviews of health behaviour change interventions. Our principal driver was pragmatic (i.e. we needed a workable method for two reviews in which we were individually involved). Only subsequently did we identify that our approach might be valuable for other reviews and review teams. The next few years are likely to witness increasing engagement with theory, within systematic reviews and when designing behavioural interventions. Are existing tools for identification of theory adequate for meeting this future challenge? Our conclusion is a qualified 'No' in that the field remains immature, especially when compared to other retrieval domains. Closer scrutiny of the role of theory within systematic reviews is likely to improve the quality of reporting, and hence retrieval, of theory in primary studies. We believe that the BeHEMoTh procedure offers a feasible starting point for identifying theory. We look forward to working with information specialists and researchers alike in meeting the challenges of rigorous evaluation. Systematic methods for information retrieval have been developed over many years. They often involve significant numbers of empirical studies, designed opportunistically to address identified retrieval problems as they occur. As a consequence, there is broad agreement on what is required to make searching more 'systematic'. This study encourages a strategic approach by first identifying characteristics that make searching systematic (i.e. question formulation, methodological filters and formal search procedures). The potential of such approaches is then explored within two opportunistic case studies. These three elements require further testing in a rigorous way. It is particularly important to evaluate the added value, if any, offered using a particular theory when conducting a review (e.g. as a practical framework for data extraction) or in understanding how a particular intervention might work (e.g. in providing a coherent and convincing explanation for a poorly understood intervention).. The BeHEMoTh Procedure addresses two needs related to the use of theory in systematic reviews. First, reviewers need to identify candidate theories that authors explicitly reference to explain the possible mechanisms by which an intervention, policy or programme may achieve its effect. Subsequently, reviewers need to identify reports where authors have applied a theory in practice.. Our two case studies identify contrasting reasons why an information professional might be asked to identify theory. The workplace smoking review sought a 'good enough' framework for use in organising data from included studies. In contrast, the community engagement review sought a rich, credible interpretation of how the intervention might work. This difference explains why it was sufficient, in the first case, to utilise only part of the procedure, while the second adhered to the entire BeHEMoTh procedure.. These contrasting case studies demonstrate that the BeHEMoTh procedure acknowledges a 'law of diminishing returns' so that each review team decides how far to progress through the procedure. In both cases, the procedure (i) opens up a toolbox of approaches to systematic identification of theory and (ii) provides an external framework for reporting searches for theory (e.g. 'we followed Steps 1a-1c of the BeHEMoTh procedure').. Given that the BeHEMoTh procedure requires supplemental searches, alongside classic Population-Intervention effectiveness reviews, a decision on its use depends upon the role of theory and the complexity of the intervention under review. Nevertheless, even within the prohibitive timescales of a pre-specified commissioned review, there are potential dividends to be gained from a more nuanced understanding of how an intervention works, even from a truncated version of the procedure that targets 'quick wins'. Some reviews have examined whether interventions that are based on an underpinning theory are more likely to be effective. 42 In a systematic review, exploring use of the Internet to promote behaviour change, Webb et al. 43 found that more extensive use of theory was associated with statistically significant increases in effect size. Interventions based on the Theory of Planned Behavior tended to have substantial effects on behaviour. Such findings imply that it is important to identify theory and, in particular, to isolate specific named theories, when synthesising complex evidence. However, such investigations are subject to many potentially confounding factors.. As information specialists, we recognise that citing a theory/model imperfectly reflects whether researchers have used a theory in practice or, indeed, whether they have used it appropriately. As a profession, we are familiar with inappropriate and tokenistic citation. Clearly, a review team must examine the context in which theory is used and whether such use is appropriate. Appraisal of 'theory fidelity' lies outside the remit of the searcher. Nevertheless, information professionals should be aware that the review team needs to differentiate a 'plausible' explanation for the choice of intervention from close and faithful adherence to a specific named theory. 44 Michie and Prestwich have produced a Theory Coding Scheme for use once papers reporting theory have been identified. 45 We have confirmed the poor standard of reporting of theory. 46, 47 In this study, we propose a systematic and transparent strategy for identifying papers that use theories of behaviour change. By providing an auditable, transparent mechanism, the BeHEMoTh method allows systematic reviewers to explain how they have identified theories that underpin a particular intervention, its implementation or differential rates of success. We offer embryonic procedures to allow reviewers to exploit the potential of theory both procedurally, as in framework synthesis, and interpretively in generating theory for realist syntheses. In doing so, we acknowledge the ongoing debate as to whether it is practically useful to search for theories separately from studies for inclusion in a review and, subsequently, whether it is valid to engage with theory if not specifically referenced within an included study. More intuitive methods of identification of theory may appear to challenge the proceduralism of systematic review methods as well as commanding acknowledgement that systematic reviews require creativity and imagination.. We conceive the BeHEMoTh procedure as a 'supplementary search' for identifying papers that explicitly describe or use theory. Such a search extends a review beyond theories invoked by included papers or already known to members of the review team. The suggested procedures address the 'disconnect' between experimental and theoretical studies. Experimental studies frequently omit reference to underlying relevant theory. In turn, review teams commonly exclude theoretical studies when sifting titles and abstracts for inclusion in a review. Painter et al. 48 report that only 35.7% of 193 health behaviour change studies mention theory. Our systematic, transparent and auditable BeHEMoTh procedure extends the likelihood of identifying theory, from sibling studies, theoretical or empirical work or study antecedents. We look forward to hearing from other researchers and practitioners who are involved in searching for theory, especially to inform reviews of complex behavioural interventions. Systematic reviews are a resource-intensive endeavour. Any proposal to extend search procedures must be regarded with circumspection. Arguments on adding a theoretical element to an intervention review must be framed in terms of added value, not simply cost. Inconclusive reviews, where an intervention only works under certain circumstances, may be illuminated by an underpinning theory. Where heterogeneity of interventions precludes meta-analysis reviews may benefit from identifying a theoretically informed 'active ingredient'. Empty reviews, with no included studies, may benefit from theoretical principles when designing future studies. Initial 'leads' to theoretical articles may be present in a typical Population-Intervention result set. Subsequently supplementary searches looking for the occurrence of theory with either the Population or the Intervention separately may be required. However, additional searches may yield collateral benefits for other review elements -for example in identifying relevant qualitative research.. Our results found no single expansive search strategy used for searching for theory. Keyword only approaches are manifestly inadequate given that theory is often not identifiable from titles and abstract alone but requires examination of full text or retrieval from citations to theory in reference lists. Future research could investigate the relative yield of a structured question formulation approach, performance of search filter terms49 and the added value of time and effort versus yield of the formalised BeHEMoTh procedure.

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I B I B I B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Prescribed Learning of R.E. Classes Abstract. This work extends studies of Angluin, Lange and Zeugmann on the dependence of learning on the hypotheses space chosen for the class. In subsequent investigations, uniformly recursively enumerable hypotheses spaces have been considered. In the present work, the following four types of learning are distinguished: class-comprising (where the learner can choose a uniformly recursively enumerable superclass as hypotheses space), class-preserving (where the learner has to choose a uniformly recursively enumerable hypotheses space of the same class), prescribed (where there must be a learner for every uniformly recursively enumerable hypotheses space of the same class) and uniform (like prescribed, but the learner has to be synthesized effectively from an index of the hypothesis space). While for explanatory learning, these four types of learnability coincide, some or all are different for other learning criteria. For example, for conservative learning, all four types are different. Several results are obtained for vacillatory and behaviourally correct learning; three of the four types can be separated, however the relation between prescribed and uniform learning remains open. It is also shown that every (not necessarily uniformly recursively enumerable) behaviourally correct learnable class has a prudent learner, that is, a learner using a hypotheses space such that it learns every set in the hypotheses space. Moreover the prudent learner can be effectively built from any learner for the class. The intuition behind learning in inductive inference [10] is that a learner sees more and more data and while reading the data produces conjectures about the concept to be learned which eventually stabilize on a correct description. The learning task is not arbitrary, but stems from a given class of concepts. Angluin [1] considered the important case that such a class is given by an indexed family, that is, the class is uniformly recursive. She has given a characterization when such a class is explanatorily learnable and introduced also important variants such as conservative learning. In the present work, the more general case of uniformly r.e. classes is addressed. Previously learnability of uniformly r.e. classes had been considered by de Jongh, Kanazawa [7] and Zilles [26, 27] . Remark 1. First some basic notation. Let W 0 , W 1 , W 2 , . . . be an acceptable enumeration of all r.e. subsets of the set of natural numbers N. A language is a r.e. subset of natural numbers. Let ϕ e denote the e-th partial recursive function, again from an acceptable numbering. For more information on recursion theory, the reader is referred to standard text books like the ones of Odifreddi [18] and Soare [21] . The function e, x = 1 2 · (e + x)(e + x + 1) + x is Cantor's pairing function. A family L 0 , L 1 , L 2 , . . . is uniformly recursively enumerable iff { e, x : x ∈ L e } is a recursively enumerable set. For ease of notation, uniformly r.e. classes are just called r.e. classes. Note that in this paper, notations like {L 0 , L 1 , L 2 , . . .} are used as a short-hand for both, the family as well as for the class of the sets; so set-theoretic comparisons like {L 0 , L 1 , L 2 , . . .} ⊆ {H 0 , H 1 , H 2 , . . .} and {L 0 , L 1 , L 2 , . . .} = {H 0 , H 1 , H 2 , . . .} ignore the ordering of the sets inside the class. Furthermore, let W e,s , L e,s , H e,s be the elements enumerated within time s into W e , L e , H e , respectively. Without loss of generality, W e,s , L e,s , H e,s are subsets of {0, 1, . . . , s}.. Let σ, τ range over (N ∪ {#}) * . Furthermore, let σ ⊆ τ denote that τ is an extension of σ as a string. content(σ) denotes the set of natural numbers in the range of σ. T is a text if T maps N to N ∪ {#} and T is a text for L a iff the numbers occurring in T are exactly those in L a . content(T ) denotes the set of natural numbers in the range of T . T [n] denotes the string consisting of the first n elements of the text T , so T [0] is the empty string and T [2] = T (0)T (1).

B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Networks of neuroinjury semantic predications to identify biomarkers for mild traumatic brain injury Objective: Mild traumatic brain injury (mTBI) has high prevalence in the military, among athletes, and in the general population worldwide (largely due to falls). Consequences can include a range of neuropsychological disorders. Unfortunately, such neural injury often goes undiagnosed due to the difficulty in identifying symptoms, so the discovery of an effective biomarker would greatly assist diagnosis; however, no single biomarker has been identified. We identify several body substances as potential components of a panel of biomarkers to support the diagnosis of mild traumatic brain injury. Methods: Our approach to diagnostic biomarker discovery combines ideas and techniques from systems medicine, natural language processing, and graph theory. We create a molecular interaction network that represents neural injury and is composed of relationships automatically extracted from the literature. We retrieve citations related to neurological injury and extract relationships (semantic predications) that contain potential biomarkers. After linking all relationships together to create a network representing neural injury, we filter the network by relationship frequency and concept connectivity to reduce the set to a manageable size of higher interest substances. Results: 99,437 relevant citations yielded 26,441 unique relations. 18,085 of these contained a potential biomarker as subject or object with a total of 6246 unique concepts. After filtering by graph metrics, the set was reduced to 1021 relationships with 49 unique concepts, including 17 potential biomarkers. Conclusion: We created a network of relationships containing substances derived from 99,437 citations and filtered using graph metrics to provide a set of 17 potential biomarkers. We discuss the interaction of several of these (glutamate, glucose, and lactate) as the basis for more effective diagnosis than is currently possible. This method provides an opportunity to focus the effort of wet bench research on those substances with the highest potential as biomarkers for mTBI. The diagnosis and treatment of traumatic brain injury (TBI) has received considerable attention. The military community may provide the biggest contribution to this interest because the signature injury of the wars in Iraq and Afghanistan is mild TBI (mTBI) [1] . mTBI is sometimes referred to as concussion, although the latter term is becoming less common in clinical and research contexts. The athletic community is also concerned with this condition, especially football and fighting sports, but also rugby, hockey, and soccer [2] [3] [4] [5] [6] [7] [8] . Although less newsworthy, falls cause the majority of head injuries in the US, with nearly 1.7 million TBI cases annually [9] . Worldwide, the annual incidence of mild TBI is estimated to be above 600 per 100,000 and, in addition to falls, motorcycle and bicycle accidents are also major causes [10] . As important as improvements in care are for veterans and athletes, such improvements can have a much broader impact on the health of communities around the world.. Although there is a need to improve the treatment of brain injury, perhaps the most significant hurdle is diagnosing mTBI. Current diagnostic standards are adequate for moderate and severe TBI because their signs and symptoms are more easily identifiable, but about 70-90% of TBI is mild, also known as concussion, and still difficult to recognize [10] . Additionally, the World Health Organization estimates that many mild injuries are not even seen by a health care practitioner because this lack of obvious and urgent symptoms fails to motivate patients to seek care [10] . Unfortunately, this does not mean that there are no long-term sequelae resulting from mTBI. According to current clinical research, mTBI sequelae include cognitive dysfunction, post-traumatic stress disorder, depression, anxiety, and dementia [2, 11] .. However, there are no currently accepted markers for clinical diagnosis of mTBI. Different organizations have created schematic tools for diagnosis, but these are subjective and the organizations do not completely agree on what constitutes a concussion [12] . For the greatest impact for military applications and throughout the world, as well as to minimize costs, a blood-based test would be ideal. Thus far such a test has not been found. There have been several candidate substances (S100B, neuron-specific enolase, glial acidic fibrillary protein, etc.), but none have succeeded for effective diagnosis of mild injury [13] . Because the search for a single biomarker has not succeeded, a composite panel may be an effective alternative. We present a method to help facilitate the identification of substances that have potential as biomarkers, which can then be validated experimentally.. As demonstrated with systems biology [14] , the molecular interactions that occur after neurological injury are complex. There may be no serum value for any of the individual components of this complicated interplay that are specific to neural injury. However, some specific combination of these values included in a panel has much greater potential for diagnostic accuracy. The first step in investigating which substances belong in such a panel is to identify the potential candidates for inclusion. In this paper, we describe a methodology to provide a list of substances that is intended to establish a base of current knowledge and provide insight into the development of a biomarker panel for mTBI diagnosis. We apply natural language processing to MEDLINE citations to extract semantic predications, which we represent as a network of potentially relevant substances interacting with their physiological environment. These semantic predications are subjectrelation-object triples, where the subjects and objects are UMLS concepts and the relation is derived from the UMLS Semantic Network as appropriate for a given concept pair. We then use network analysis techniques to identify a list that is focused on highly significant substances. We have explored the creation of a molecular interaction network that represents neural injury and is composed of semantic predications automatically extracted from the literature. We achieved our goal of providing substances with potential as biomarkers to support the diagnosis of mTBI. The methodology is based on a network of semantic predications representing the interaction of substances observed subsequent to neural insult. Combining semantic predications of TBI substance interactions into a network in this way correlates well with systems biology (and by extension, systems medicine), which is concerned with the complex network interplay of a biological unit and represents injury and illness as a perturbation to the network.. Predications were extracted by SemRep and the component subject or object concepts were mapped to nodes and their relationships (predicates) mapped to edges, creating a network of relations. This network represents a summary of the physiological and pharmacogenomic space of neurological injury, as presented in the literature included in MEDLINE. To identify clinically significant candidates for mTBI biomarkers, the network was then filtered by degree centrality and frequency, greatly reducing the density of concepts and relationships. The resulting network produced 17 compounds to be considered as mTBI biomarkers, both previously investigated and novel as TBI biomarker candidates. The interaction of several of these is discussed as the basis for a panel of biomarkers to more effectively diagnose mTBI than is currently possible. Most substances identified in this study as worthy of consideration as mTBI biomarkers fall into four general categories: previously studied biomarkers (amyloid beta-protein precursor, brain-derived neurotrophic factor, fibroblast growth factor 2, glial fibrillary acidic protein, neuron-specific enolase, S100b); neurotransmitters (glutamate, dopamine, norepinephrine); inflammation and cell injury markers (interleukin-6, calpain breakdown products, malondialdehyde, superoxide dismutase); and ubiquitous substances (glucose, lactate, calcium). Although all of the resulting substances were reviewed in depth during the methodology, the following illustrate the information contained in the resulting mTBI biomarker network and the information retrieved during the validation process. These examples suggest possible implications for clinical practice retrieved directly from the research literature. Several manual reviews have been undertaken to survey potential biomarkers for TBI [39, 40] and more specifically mTBI [41] [42] [43] . These authors search for citations specifically detailing clinical research of mTBI biomarkers and therefore contain only potential biomarkers that have already been investigated. Another limitation of the studies is the small number of citations reviewed (ranging from 26 [42] to 107 [43] ) due to the limitations of human review. Although no automated detection of potential TBI biomarkers exists in the literature, there are automatic systems to help diagnose other disorders, for example diabetes and obesity [44] . Although not related to mTBI, there is research related to the literaturebased discovery of other types of interaction networks Figure 1 Network of glutamate predications. Subject and object concepts are represented as nodes and predicates are represented as edges. Glutamate is common to all three predications and is, therefore, the most highly connected node in the network.. (though not specifically for biomarkers). One automatically generates an interaction network detailing gene involvement in vaccine-related fever using 170,000 citations from a PubMed search and a vaccine-specific ontology [45] . Another used citations containing the PubMed MeSH term human and containing sentences related to interferon-gamma, from which relationships were extracted and ranked using graph metrics [46] . Jordan et al. [47] present a keyword search method for identifying putative biomarkers for breast and lung cancer by searching for genes and proteins associated with a biological fluid keyword and either cancer. However, none of this work has made use of semantic predications, as we have, in the formation of an interaction network. There is a large body of work on literature-based discovery approaches many of which use SemRep semantic predications [26, [48] [49] [50] [51] [52] [53] [54] . These approaches may generate systems for discovery [55] [56] [57] [58] or are specific applications to predict various phenomena such as interactions between genes and proteins [46, 59] , cancer treatments [60, 61] , adverse drug reactions [49] , drug-drug interactions [50] , drug repurposing [51, 62] , asthma gene associations [63] , treatments for neovascularization in diabetic retinopathy [52] , relations between psychiatric and somatic diseases [64] , genes related to reactive oxygen species and diabetes [65] , and mechanisms for sleep disturbance [25] and the obesity paradox [53] . As shown in Figure 2 , citations related to nervous system trauma were retrieved from PubMed. From these, predications were extracted that contain a substance as the subject or object. These predications were organized into a single network which is then filtered to select for the most highly connected and frequent components. The substances included in this summary network serve as the list of potential mTBI biomarkers.

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