Robust abandoned object detection integrating wide area visual surveillance and social context

This paper presents a video surveillance framework that robustly and efficiently detects abandoned objects in surveillance scenes. The framework is based on a novel threat assessment algorithm which combines the concept of ownership with automatic understanding of social relations in order to infer abandonment of objects. Implementation is achieved through development of a logic-based inference engine based on Prolog. Threat detection performance is conducted by testing against a range of datasets describing realistic situations and demonstrates a reduction in the number of false alarms generated. The proposed system represents the approach employed in the EU SUBITO project (Surveillance of Unattended Baggage and the Identification and Tracking of the Owner).

Constructing summary statistics for approximate Bayesian computation: semi-automatic approximate Bayesian computation

Many modern statistical applications involve inference for complex stochastic models, where it is easy to simulate from the models, but impossible to calculate likelihoods. Approximate Bayesian computation (ABC) is a method of inference for such models. It replaces calculation of the likelihood by a step which involves simulating artificial data for different parameter values, and comparing summary statistics of the simulated data with summary statistics of the observed data. Here we show how to construct appropriate summary statistics for ABC in a semi-automatic manner. We aim for summary statistics which will enable inference about certain parameters of interest to be as accurate as possible. Theoretical results show that optimal summary statistics are the posterior means of the parameters. Although these cannot be calculated analytically, we use an extra stage of simulation to estimate how the posterior means vary as a function of the data; and we then use these estimates of our summary statistics within ABC. Empirical results show that our approach is a robust method for choosing summary statistics that can result in substantially more accurate ABC analyses than the ad hoc choices of summary statistics that have been proposed in the literature. We also demonstrate advantages over two alternative methods of simulation-based inference.

Effects of 3-nitrooxypropanol on methane emission,digestion, and energy and nitrogen balance of lactating dairy cows

The objective was to measure effects of 3-nitrooxypropanol (3NP) on methane production of lactating dairy cows and any associated changes in digestion and energy and nitrogen metabolism. Six Holstein-Friesian dairy cows in mid-lactation were fed twice daily a total mixed ration with maize silage as the primary forage source. Cows received 1 of 3 treatments using an experimental design based on two 3 × 3 Latin squares with 5-wk periods. Treatments were a control placebo or 500 or 2,500 mg/d of 3NP delivered directly into the rumen, via the rumen fistula, in equal doses before each feeding. Measurements of methane production and energy and nitrogen balance were obtained during wk 5 of each period using respiration calorimeters and digestion trials. Measurements of rumen pH (48 h) and postprandial volatile fatty acid and ammonia concentrations were made at the end of wk 4. Daily methane production was reduced by 3NP, but the effects were not dose dependent (reductions of 6.6 and 9.8% for 500 and 2,500 mg/d, respectively). Dosing 3NP had a transitory inhibitory effect on methane production, which may have been due to the product leaving the rumen in liquid outflow or through absorption or metabolism. Changes in rumen concentrations of volatile fatty acids indicated that the pattern of rumen fermentation was affected by both doses of the product, with a decrease in acetate:propionate ratio observed, but that acetate production was inhibited by the higher dose. Dry matter, organic matter, acid detergent fiber, N, and energy digestibility were reduced at the higher dose of the product. The decrease in digestible energy supply was not completely countered by the decrease in methane excretion such that metabolizable energy supply, metabolizable energy concentration of the diet, and net energy balance (milk plus tissue energy) were reduced by the highest dose of 3NP. Similarly, the decrease in nitrogen digestibility at the higher dose of the product was associated with a decrease in body nitrogen balance that was not observed for the lower dose. Milk yield and milk fat concentration and fatty acid composition were not affected but milk protein concentration was greater for the higher dose of 3NP. Twice-daily rumen dosing of 3NP reduced methane production by lactating dairy cows, but the dose of 2,500 mg/d reduced rumen acetate concentration, diet digestibility, and energy supply. Further research is warranted to determine the optimal dose and delivery method of the product. Key words: 3-nitrooxypropanol, methane, digestion, rumen, dairy cow

Role conflict and role ambiguity in construction projects

Role conflict happens when a person faces different and incompatible expectations regarding a particular social status which they occupy. The literature on role conflict is reviewed for a better understanding of project dynamics in construction teams. The discussion focuses on issues surrounding the miscommunication of role expectations and tensions owing to differences in expectations of the same role. This ongoing doctoral study involves a qualitative research design, based on interviews with practicing professionals. Analysis will focus on the relation between formal expectations, as evidenced in contracts and other types of written communication, and informal expectations as observed from the interviews. Insights from the literature review suggest: 1. that the differences between formal and informal expectations is a major sources of role conflict in construction teams and 2. that this effect is exacerbated by the failure of team members to recognise it and take it into account.

Evaluation and revision of inferential comprehension in narrative texts: an eye movement study

We investigated the processes of how adult readers evaluate and revise their situation model during reading by monitoring their eye movements as they read narrative texts and subsequent critical sentences. In each narrative text, a short introduction primed a knowledge-based inference, followed by a target concept that was either expected (e.g., “oven”) or unexpected (e.g., “grill”) in relation to the inferred concept. Eye movements showed that readers detected a mismatch between the new unexpected information and their prior interpretation, confirming their ability to evaluate inferential information. Just below the narrative text, a critical sentence included a target word that was either congruent (e.g., “roasted”) or incongruent (e.g., “barbecued”) with the expected but not the unexpected concept. Readers spent less time reading the congruent than the incongruent target word, reflecting the facilitation of prior information. In addition, when the unexpected (but not expected) concept had been presented, participants with lower verbal (but not visuospatial) working memory span exhibited longer reading times and made more regressions (from the critical sentence to previous information) on encountering congruent information, indicating difficulty in inhibiting their initial incorrect interpretation and revising their situation model

Long-term positive effects of repeating a year in school: Six-year longitudinal study of self-beliefs, anxiety, social relations, school grades, and test scores

Consistently with a priori predictions, school retention (repeating a year in school) had largely positive effects for a diverse range of 10 outcomes (e.g., math self-concept, self-efficacy, anxiety, relations with teachers, parents and peers, school grades, and standardized achievement test scores). The design, based on a large, representative sample of German students (N = 1,325, M age = 11.75 years) measured each year during the first five years of secondary school, was particularly strong. It featured four independent retention groups (different groups of students, each repeating one of the four first years of secondary school, total N = 103), with multiple post-test waves to evaluate short- and long-term effects, controlling for covariates (gender, age, SES, primary school grades, IQ) and one or more sets of 10 outcomes realised prior to retention. Tests of developmental invariance demonstrated that the effects of retention (controlling for covariates and pre-retention outcomes) were highly consistent across this potentially volatile early-to-middle adolescent period; largely positive effects in the first year following retention were maintained in subsequent school years following retention. Particularly considering that these results are contrary to at least some of the accepted wisdom about school retention, the findings have important implications for educational researchers, policymakers and parents.

Context-based interaction design for smart homes

This paper presents the notion of Context-based Activity Design (CoBAD) that represents context with its dynamic changes and normative activities in an interactive system design. The development of CoBAD requires an appropriate context ontology model and inference mechanisms. The incorporation of norms and information field theory into Context State Transition Model, and the implementation of new conflict resolution strategies based on the specific situation are discussed. A demonstration of CoBAD using a human agent scenario in a smart home is also presented. Finally, a method of treating conflicting norms in multiple information fields is proposed.

Design and optimisation of a large-area process-based model for annual crops

The formulation of a new process-based crop model, the general large-area model (GLAM) for annual crops is presented. The model has been designed to operate on spatial scales commensurate with those of global and regional climate models. It aims to simulate the impact of climate on crop yield. Procedures for model parameter determination and optimisation are described, and demonstrated for the prediction of groundnut (i.e. peanut; Arachis hypogaea L.) yields across India for the period 1966-1989. Optimal parameters (e.g. extinction coefficient, transpiration efficiency, rate of change of harvest index) were stable over space and time, provided the estimate of the yield technology trend was based on the full 24-year period. The model has two location-specific parameters, the planting date, and the yield gap parameter. The latter varies spatially and is determined by calibration. The optimal value varies slightly when different input data are used. The model was tested using a historical data set on a 2.5degrees x 2.5degrees grid to simulate yields. Three sites are examined in detail-grid cells from Gujarat in the west, Andhra Pradesh towards the south, and Uttar Pradesh in the north. Agreement between observed and modelled yield was variable, with correlation coefficients of 0.74, 0.42 and 0, respectively. Skill was highest where the climate signal was greatest, and correlations were comparable to or greater than correlations with seasonal mean rainfall. Yields from all 35 cells were aggregated to simulate all-India yield. The correlation coefficient between observed and simulated yields was 0.76, and the root mean square error was 8.4% of the mean yield. The model can be easily extended to any annual crop for the investigation of the impacts of climate variability (or change) on crop yield over large areas. (C) 2004 Elsevier B.V. All rights reserved.

AT(1) receptor ligands: virtual-screening-based design with TOPP descriptors, synthesis, and biological evaluation of pyrrolidine derivatives

As a continuing effort to establish the structure-activity relationships (SARs) within the series of the angiotensin II antagonists (sartans), a pharmacophoric model was built by using novel TOPP 3D descriptors. Statistical values were satisfactory (PC4: r(2)=0.96, q(2) ((5) (random) (groups))=0.84; SDEP=0.26) and encouraged the synthesis and consequent biological evaluation of a series of new pyrrolidine derivatives. SAR together with a combined 3D quantitative SAR and high-throughput virtual screening showed that the newly synthesized 1-acyl-N-(biphenyl-4-ylmethyl)pyrrolidine-2-carboxamides may represent an interesting starting point for the design of new antihypertensive agents. In particular, biological tests performed on CHO-hAT(1) cells stably expressing the human AT(1) receptor showed that the length of the acyl chain is crucial for the receptor interaction and that the valeric chain is the optimal one.

Design of mucoadhesive polymeric films based on blends of poly(acrylic acid) and (hydroxypropyl)cellulose

Mixing of aqueous solutions of poly(acrylic acid) and (hydroxypropyl) cellulose results in formation of hydrogen-bonded interpolymer complexes, which precipitate and do not allow preparation of homogeneous polymeric films by casting. In the present work the effect of pH on the complexation between poly(acrylic acid) and (hydroxypropyl)cellulose in solutions and miscibility of these polymers in solid state has been studied. The pH-induced complexation-miscibility-immiscibility transitions in the polymer mixtures have been observed. The optimal conditions for preparation of homogeneous polymeric films based on blends of these polymers have been found, and the possibility of radiation cross-linking of these materials has been demonstrated. Although the gamma-radiation treatment of solid polymeric blends was found to be inefficient, successful cross-linking was achieved by addition of N, N'- methylenebis(acrylamide). The mucoadhesive potential of both soluble and cross-linked films toward porcine buccal mucosa is evaluated. Soluble films adhered to mucosal tissues undergo dissolution within 30-110 min depending on the polymer ratio in the blend. Cross-linked films are retained on the mucosal surface for 10-40 min and then detach.

Design and evaluation of intelligent classifier based intrusion detection system

We have discovered a novel approach of intrusion detection system using an intelligent data classifier based on a self organizing map (SOM). We have surveyed all other unsupervised intrusion detection methods, different alternative SOM based techniques and KDD winner IDS methods. This paper provides a robust designed and implemented intelligent data classifier technique based on a single large size (30x30) self organizing map (SOM) having the capability to detect all types of attacks given in the DARPA Archive 1999 the lowest false positive rate being 0.04 % and higher detection rate being 99.73% tested using full KDD data sets and 89.54% comparable detection rate and 0.18% lowest false positive rate tested using corrected data sets.

Sparse kernel density estimator using orthogonal regression based on D-optimality experimental design

A novel sparse kernel density estimator is derived based on a regression approach, which selects a very small subset of significant kernels by means of the D-optimality experimental design criterion using an orthogonal forward selection procedure. The weights of the resulting sparse kernel model are calculated using the multiplicative nonnegative quadratic programming algorithm. The proposed method is computationally attractive, in comparison with many existing kernel density estimation algorithms. Our numerical results also show that the proposed method compares favourably with other existing methods, in terms of both test accuracy and model sparsity, for constructing kernel density estimates.