Effect of signal intensity normalization on the multivariate analysis of spectral data in complex 'real-world' datasets

Spectral signal intensities, especially in 'real-world' applications with nonstandardized sample presentation due to uncontrolled variables/factors, commonly require additional spectral processing to normalize signal intensity in an effective way. In this study, we have demonstrated the complexity of choosing a normalization routine in the presence of multiple spectrally distinct constituents by probing a dataset of Raman spectra. Variation in absolute signal intensity (90.1% of total variance) of the Raman spectra of these complex biological samples swamps the variation in useful signals (9.4% of total variance), degrading its diagnostic and evaluative potential.

Tools for Finding Inconsistencies in Real-world Logic-based Systems

Currently there is extensive theoretical work on inconsistencies in logic-based systems. Recently, algorithms for identifying inconsistent clauses in a single conjunctive formula have demonstrated that practical application of this work is possible. However, these algorithms have not been extended for full knowledge base systems and have not been applied to real-world knowledge. To address these issues, we propose a new algorithm for finding the inconsistencies in a knowledge base using existing algorithms for finding inconsistent clauses in a formula. An implementation of this algorithm is then presented as an automated tool for finding inconsistencies in a knowledge base and measuring the inconsistency of formulae. Finally, we look at a case study of a network security rule set for exploit detection (QRadar) and suggest how these automated tools can be applied.

Energy compensation in the real world: Good compensation for small portions of chocolate and biscuits over short time periods in complicit consumers using commercially available foods

While investigations using covert food manipulations tend to suggest that individuals are poor at adjusting for previous energy intake, in the real world adults rarely consume foods of which they are ill-informed. This study investigated the impact in fully complicit consumers of consuming commercially available dark chocolate, milk chocolate, sweet biscuits and fruit bars on subsequent appetite. Using a repeated measures design, participants received four small portions (4 × 10-11 g) of either dark chocolate, milk chocolate, sweet biscuits, fruit bars or no food throughout five separate study days (counterbalanced in order), and test meal intake, hunger, liking and acceptability were measured. Participants consumed significantly less at lunch following dark chocolate, milk chocolate and sweet biscuits compared to no food (smallest t(19) = 2.47, p = 0.02), demonstrating very good energy compensation (269-334%). No effects were found for fruit bars (t(19) = 1.76, p = 0.09), in evening meal intakes (F(4,72) = 0.62, p = 0.65) or in total intake (lunch + evening meal + food portions) (F(4,72) = 0.40, p = 0.69). No differences between conditions were found in measures of hunger (largest F(4,76) = 1.26, p = 0.29), but fruit bars were significantly less familiar than all other foods (smallest t(19) = 3.14, p = 0.01). These findings demonstrate good compensation over the short term for small portions of familiar foods in complicit consumers. Findings are most plausibly explained as a result of participant awareness and cognitions, although the nature of these cognitions cannot be discerned from this study. These findings however, also suggest that covert manipulations may have limited transfer to real world scenarios.

Multi-bubble sonoluminescence: Laboratory curiosity, or real world application?

Sonoluminescence (SL) involves the conversion of mechanical [ultra]sound energy into light. Whilst the phenomenon is invariably inefficient, typically converting just 10^-4 of the incident acoustic energy into photons, it is nonetheless extraordinary, as the resultant energy density of the emergent photons exceeds that of the ultrasonic driving field by a factor of some 10 ¹². Sonoluminescence has specific [as yet untapped] advantages in that it can be effected at remote locations in an essentially wireless format. The only [usual] requirement is energy transduction via the violent oscillation of microscopic bubbles within the propagating medium. The dependence of sonoluminescent output on the generating sound field's parameters, such as pulse duration, duty cycle, and position within the field, have been observed and measured previously, and several relevant aspects are discussed presently. We also extrapolate the logic from a recently published analysis relating to the ensuing dynamics of bubble 'clouds' that have been stimulated by ultrasound. Here, the intention was to develop a relevant [yet computationally simplistic] model that captured the essential physical qualities expected from real sonoluminescent microbubble clouds. We focused on the inferred temporal characteristics of SL light output from a population of such bubbles, subjected to intermediate [0.5-2MPa] ultrasonic pressures. Finally, whilst direct applications for sonoluminescent light output are thought unlikely in the main, we proceed to frame the state-of-the- art against several presently existing technologies that could form adjunct approaches with distinct potential for enhancing present sonoluminescent light output that may prove useful in real world [biomedical] applications.

Investigating Behaviour and Face Encoding in a Hypothetical Real-World Social Contract: Handwashing in Hazardous Health Settings

Investigations into the evolutionary origins of human cognition has shown that individuals’ memory for others is influenced by the latter’s behaviour in social contracts. Such research is primarily based on hypothetical or more abstract forms of social contracts, whereas an application of this knowledge to everyday health behaviours can be of great value. To address this, the current study investigated whether participants who were asked to imagine themselves in a hypothetical hazardous health scenario showed differential response sensitivity (d’) and latency (RT) to faces of hospital staff tagged with contrasting hand hygiene before touching patients: clean hands, dirty hands, or unknown hand-washing behaviour (control). The test used a two alternative forced-choice (2AFC: “old/new”) face recognition paradigm. The findings showed that d’ to dirty and clean hands was similar, but higher than for controls. Moreover, d’ was not affected by the occupation of hospital staff (nurses vs porters). The absence of memory gains towards clean or dirty hands points to the need for new strategies to remind patients to observe (and remember) the hand hygiene of others when exposed to hazardous health environments.

Introduction to the special issue on real world applications of intelligent tutoring systems

The idea behind creating this special issue on real world applications of intelligent tutoring systems was to bring together in a single publication some of the most important examples of success in the use of ITS technology. This will serve as a reference to all researchers working in the area. It will also be an important resource for the industry, showing the maturity of ITS technology and creating an atmosphere for funding new ITS projects. Simultaneously, it will be valuable to academic groups, motivating students for new ideas of ITS and promoting new academic research work in the area.

Beliefs and Conflicts in a Real World Multi-Agent System

In a real world multiagent system, where the agents are faced with partial, incomplete and intrinsically dynamic knowledge, conflicts are inevitable. Frequently, different agents have goals or beliefs that cannot hold simultaneously. Conflict resolution methodologies have to be adopted to overcome such undesirable occurrences. In this paper we investigate the application of distributed belief revision techniques as the support for conflict resolution in the analysis of the validity of the candidate beams to be produced in the CERN particle accelerators. This CERN multiagent system contains a higher hierarchy agent, the Specialist agent, which makes use of meta-knowledge (on how the con- flicting beliefs have been produced by the other agents) in order to detect which beliefs should be abandoned. Upon solving a conflict, the Specialist instructs the involved agents to revise their beliefs accordingly. Conflicts in the problem domain are mapped into conflicting beliefs of the distributed belief revision system, where they can be handled by proven formal methods. This technique builds on well established concepts and combines them in a new way to solve important problems. We find this approach generally applicable in several domains.

Spatial relational learning and memory abilities do not differ between men and women in a real-world, open-field environment

This study assesses gender differences in spatial and non-spatial relational learning and memory in adult humans behaving freely in a real-world, open-field environment. In Experiment 1, we tested the use of proximal landmarks as conditional cues allowing subjects to predict the location of rewards hidden in one of two sets of three distinct locations. Subjects were tested in two different conditions: (1) when local visual cues marked the potentially-rewarded locations, and (2) when no local visual cues marked the potentially-rewarded locations. We found that only 17 of 20 adults (8 males, 9 females) used the proximal landmarks to predict the locations of the rewards. Although females exhibited higher exploratory behavior at the beginning of testing, males and females discriminated the potentially-rewarded locations similarly when local visual cues were present. Interestingly, when the spatial and local information conflicted in predicting the reward locations, males considered both spatial and local information, whereas females ignored the spatial information. However, in the absence of local visual cues females discriminated the potentially-rewarded locations as well as males. In Experiment 2, subjects (9 males, 9 females) were tested with three asymmetrically-arranged rewarded locations, which were marked by local cues on alternate trials. Again, females discriminated the rewarded locations as well as males in the presence or absence of local cues. In sum, although particular aspects of task performance might differ between genders, we found no evidence that women have poorer allocentric spatial relational learning and memory abilities than men in a real-world, open-field environment.

Designing in the real world is complex anyway - so what?

Designing is a heterogeneous, fuzzily defined, floating field of various activities and chunks of ideas and knowledge. Available theories about the foundations of designing as presented in "the basic PARADOX" (Jonas and Meyer-Veden 2004) have evoked the impression of Babylonian confusion. We located the reasons for this "mess" in the "non-fit", which is the problematic relation of theories and subject field. There seems to be a comparable interface problem in theory-building as in designing itself. "Complexity" sounds promising, but turns out to be a problematic and not really helpful concept. I will argue for a more precise application of systemic and evolutionary concepts instead, which - in my view - are able to model the underlying generative structures and processes that produce the visible phenomenon of complexity. It does not make sense to introduce a new fashionable meta-concept and to hope for a panacea before having clarified the more basic and still equally problematic older meta-concepts. This paper will take one step away from "theories of what" towards practice and doing and try to have a closer look at existing process models or "theories of how" to design instead. Doing this from a systemic perspective leads to an evolutionary view of the process, which finally allows to specify more clearly the "knowledge gaps" inherent in the design process. This aspect has to be taken into account as constitutive of any attempt at theory-building in design, which can be characterized as a "practice of not-knowing". I conclude, that comprehensive "unified" theories, or methods, or process models run aground on the identified knowledge gaps, which allow neither reliable models of the present, nor reliable projections into the future. Consolation may be found in performing a shift from the effort of adaptation towards strategies of exaptation, which means the development of stocks of alternatives for coping with unpredictable situations in the future.

Surface Reflectance Recognition and Real-World Illumination Statistics

Humans distinguish materials such as metal, plastic, and paper effortlessly at a glance. Traditional computer vision systems cannot solve this problem at all. Recognizing surface reflectance properties from a single photograph is difficult because the observed image depends heavily on the amount of light incident from every direction. A mirrored sphere, for example, produces a different image in every environment. To make matters worse, two surfaces with different reflectance properties could produce identical images. The mirrored sphere simply reflects its surroundings, so in the right artificial setting, it could mimic the appearance of a matte ping-pong ball. Yet, humans possess an intuitive sense of what materials typically "look like" in the real world. This thesis develops computational algorithms with a similar ability to recognize reflectance properties from photographs under unknown, real-world illumination conditions. Real-world illumination is complex, with light typically incident on a surface from every direction. We find, however, that real-world illumination patterns are not arbitrary. They exhibit highly predictable spatial structure, which we describe largely in the wavelet domain. Although they differ in several respects from the typical photographs, illumination patterns share much of the regularity described in the natural image statistics literature. These properties of real-world illumination lead to predictable image statistics for a surface with given reflectance properties. We construct a system that classifies a surface according to its reflectance from a single photograph under unknown illuminination. Our algorithm learns relationships between surface reflectance and certain statistics computed from the observed image. Like the human visual system, we solve the otherwise underconstrained inverse problem of reflectance estimation by taking advantage of the statistical regularity of illumination. For surfaces with homogeneous reflectance properties and known geometry, our system rivals human performance.