Improving the Understanding and the Reliability of the Concept of "Sufficiency" in Friction Ridge Examination

The National Academies has stressed the need to develop quantifiable measures for methods that are currently qualitative in nature, such as the examination of fingerprints. Current protocols and procedures to perform these examinations rely heavily on a succession of subjective decisions, from the initial acceptance of evidence for probative value to the final assessment of forensic results. This project studied the concept of sufficiency associated with the decisions made by latent print examiners at the end of the various phases of the examination process. During this 2-year effort, a web‐based interface was designed to capture the observations of 146 latent print examiners and trainees on 15 pairs of latent/control prints. Two main findings resulted from the study: The concept of sufficiency is driven mainly by the number and spatial relationships between the minutiae observed on the latent and control prints. Data indicate that demographics (training, certification, years of experience) or non‐minutiae based features (such as level 3 features) do not play a major role in examiners' decisions; Significant variability was observed between detecting and interpreting friction ridge features and at all levels of details, as well as for factors that have the potential to influence the examination process, such as degradation, distortion, or influence of the background and the development technique.

NBAS mutations cause a multisystem disorder involving bone, connective tissue, liver, immune system, and retina.

We report two unrelated patients with a multisystem disease involving liver, eye, immune system, connective tissue, and bone, caused by biallelic mutations in the neuroblastoma amplified sequence (NBAS) gene. Both presented as infants with recurrent episodes triggered by fever with vomiting, dehydration, and elevated transaminases. They had frequent infections, hypogammaglobulinemia, reduced natural killer cells, and the Pelger-Huët anomaly of their granulocytes. Their facial features were similar with a pointed chin and proptosis; loose skin and reduced subcutaneous fat gave them a progeroid appearance. Skeletal features included short stature, slender bones, epiphyseal dysplasia with multiple phalangeal pseudo-epiphyses, and small C1-C2 vertebrae causing cervical instability and myelopathy. Retinal dystrophy and optic atrophy were present in one patient. NBAS is a component of the synthaxin-18 complex and is involved in nonsense-mediated mRNA decay control. Putative loss-of-function mutations in NBAS are already known to cause disease in humans. A specific founder mutation has been associated with short stature, optic nerve atrophy and Pelger-Huët anomaly of granulocytes (SOPH) in the Siberian Yakut population. A more recent report associates NBAS mutations with recurrent acute liver failure in infancy in a group of patients of European descent. Our observations indicate that the phenotypic spectrum of NBAS deficiency is wider than previously known and includes skeletal, hepatic, metabolic, and immunologic aspects. Early recognition of the skeletal phenotype is important for preventive management of cervical instability. © 2015 Wiley Periodicals, Inc.

Local Binary Patterns in Focal-Plane Processing. Analysis and Applications

Feature extraction is the part of pattern recognition, where the sensor data is transformed into a more suitable form for the machine to interpret. The purpose of this step is also to reduce the amount of information passed to the next stages of the system, and to preserve the essential information in the view of discriminating the data into different classes. For instance, in the case of image analysis the actual image intensities are vulnerable to various environmental effects, such as lighting changes and the feature extraction can be used as means for detecting features, which are invariant to certain types of illumination changes. Finally, classification tries to make decisions based on the previously transformed data. The main focus of this thesis is on developing new methods for the embedded feature extraction based on local non-parametric image descriptors. Also, feature analysis is carried out for the selected image features. Low-level Local Binary Pattern (LBP) based features are in a main role in the analysis. In the embedded domain, the pattern recognition system must usually meet strict performance constraints, such as high speed, compact size and low power consumption. The characteristics of the final system can be seen as a trade-off between these metrics, which is largely affected by the decisions made during the implementation phase. The implementation alternatives of the LBP based feature extraction are explored in the embedded domain in the context of focal-plane vision processors. In particular, the thesis demonstrates the LBP extraction with MIPA4k massively parallel focal-plane processor IC. Also higher level processing is incorporated to this framework, by means of a framework for implementing a single chip face recognition system. Furthermore, a new method for determining optical flow based on LBPs, designed in particular to the embedded domain is presented. Inspired by some of the principles observed through the feature analysis of the Local Binary Patterns, an extension to the well known non-parametric rank transform is proposed, and its performance is evaluated in face recognition experiments with a standard dataset. Finally, an a priori model where the LBPs are seen as combinations of n-tuples is also presented

The influence of physical attractiveness and gender on perceived competence of sportscasters

The media tends to represent female athletes as women first and athletes second (Koivula, 1 999). The present study investigated whether this same trend was present for female sportscasters, using a self-presentational framework. Self-presentation is the process by which people try to control how others see them (Leary, 1995). One factor that may influence the type of image they try to project is their roles held in society, including gender roles. The gender roles for a man include dominance, assertiveness, and masculinity, while the gender roles for a woman include nurturer, femininity, and attractiveness (Deaux & Major, 1 987). By contrast, sports broadcasters are expected to be knowledgeable, assertive, and competent. Research suggests that female sports broadcasters are seen as less competent and less persuasive than male sports broadcasters (Mitrook & Dorr, 2001; Ordman & Zillmann, 1994, Toro, 2005). One reason for this difference may be that the gender roles for a man are much more similar to those of a sportscaster, compared to those of a woman. Thus, there may be a conflict between the two roles for women. The present study investigated whether the gender and perceived attractiveness of sportscasters influenced the audience's perceptions of the level of competence that a sportscaster demonstrates. Two hundred and four male (n =75) and female (n =129) undergraduate students were recruited from a southern Ontario university to participate in the study. The average age of the male participants was 21 .23 years {SD =1 .60), and the average age for female participants was 20.67 years {SD = 1 .31). The age range for all participants was from 19 to 30 years {M = 20.87 years, SD = 1 .45). Af^er providing informed consent, participants randomly received one of four possible questionnaire packages. The participants answered the demographic questionnaire, and then proceeded to view the picture and read the script of a sports newscast. Next, based on the picture and script, the participants answered the competence questionnaire, assessing the general, sport specific, and overall competence of the sportscaster. Once participants had finished, they returned the package to the researcher and were thanked for their time. Data was analyzed using an ANOVA to determine if general sport competence differs with respect to gender and attractiveness of the sportscaster. Overall, the ANOVA was non-significant (p > .05), indicating no differences on the dependent variable based on gender (F (3, 194) = .631, p = .426), attractiveness (F (3, 194) = .070, p = .791), or the interaction of the two {F (3, 194) = .043,/? = .836). Although none of the study hypotheses were supported, the study provided some insight to the perceived competence of female sportscasters. It is possible that female sportscasters are now seen as competent in the area of sports. Sample characteristics could also have influenced these results; the participants in the current study were primarily physical education and kinesiology students, who had experience participating in physical activity with both men and women. Future research should investigate this issue further by using a video sportscast. It is possible that delivery characteristics such as voice quality or eye contact may also impact perceptions of sportscasters.

Level Identification of Brain MR Images using Histogram of a LBP variant

Axial brain slices containing similar anatomical structures are retrieved using features derived from the histogram of Local binary pattern (LBP). A rotation invariant description of texture in terms of texture patterns and their strength is obtained with the incorporation of local variance to the LBP, called Modified LBP (MOD-LBP). In this paper, we compare Histogram based Features of LBP (HF/LBP), against Histogram based Features of MOD-LBP (HF/MOD-LBP) in retrieving similar axial brain images. We show that replacing local histogram with a local distance transform based similarity metric further improves the performance of MOD-LBP based image retrieval

People Recognition in Image Sequences by Supervised Learning

We describe a system that learns from examples to recognize people in images taken indoors. Images of people are represented by color-based and shape-based features. Recognition is carried out through combinations of Support Vector Machine classifiers (SVMs). Different types of multiclass strategies based on SVMs are explored and compared to k-Nearest Neighbors classifiers (kNNs). The system works in real time and shows high performance rates for people recognition throughout one day.

Robustness of mutual information to inter-subject variability for automatic artefact removal from EEG

The externally recorded electroencephalogram (EEG) is contaminated with signals that do not originate from the brain, collectively known as artefacts. Thus, EEG signals must be cleaned prior to any further analysis. In particular, if the EEG is to be used in online applications such as Brain-Computer Interfaces (BCIs) the removal of artefacts must be performed in an automatic manner. This paper investigates the robustness of Mutual Information based features to inter-subject variability for use in an automatic artefact removal system. The system is based on the separation of EEG recordings into independent components using a temporal ICA method, RADICAL, and the utilisation of a Support Vector Machine for classification of the components into EEG and artefact signals. High accuracy and robustness to inter-subject variability is achieved.

Learning enhanced 3D models for vehicle tracking

This paper presents an enhanced hypothesis verification strategy for 3D object recognition. A new learning methodology is presented which integrates the traditional dichotomic object-centred and appearance-based representations in computer vision giving improved hypothesis verification under iconic matching. The "appearance" of a 3D object is learnt using an eigenspace representation obtained as it is tracked through a scene. The feature representation implicitly models the background and the objects observed enabling the segmentation of the objects from the background. The method is shown to enhance model-based tracking, particularly in the presence of clutter and occlusion, and to provide a basis for identification. The unified approach is discussed in the context of the traffic surveillance domain. The approach is demonstrated on real-world image sequences and compared to previous (edge-based) iconic evaluation techniques.

A framework for graphic description in book design

This project is concerned with the way that illustrations, photographs, diagrams and graphs, and typographic elements interact to convey ideas on the book page. A framework for graphic description is proposed to elucidate this graphic language of ‘complex texts’. The model is built up from three main areas of study, with reference to a corpus of contemporary children’s science books. First, a historical survey puts the subjects for study in context. Then a multidisciplinary discussion of graphic communication provides a theoretical underpinning for the model; this leads to various proposals, such as the central importance of ratios and relationships among parts in creating meaning in graphic communication. Lastly a series of trials in description contribute to the structure of the model itself. At the heart of the framework is an organising principle that integrates descriptive models from fields of design, literary criticism, art history, and linguistics, among others, as well as novel categories designed specifically for book design. Broadly, design features are described in terms of elemental component parts (micro-level), larger groupings of these (macro-level), and finally in terms of overarching, ‘whole book’ qualities (meta-level). Various features of book design emerge at different levels; for instance, the presence of nested discursive structures, a form of graphic recursion in editorial design, is proposed at the macro-level. Across these three levels are the intersecting categories of ‘rule’ and ‘context’, offering different perspectives with which to describe graphic characteristics. Contextbased features are contingent on social and cultural environment, the reader’s previous knowledge, and the actual conditions of reading; rule-based features relate to the systematic or codified aspects of graphic language. The model aims to be a frame of reference for graphic description, of use in different forms of qualitative or quantitative research and as a heuristic tool in practice and teaching.

Content validation for level of use of feature rich systems: a Delphi study of electronic medical records systems

Introduction. Feature usage is a pre-requisite to realising the benefits of investments in feature rich systems. We propose that conceptualising the dependent variable 'system use' as 'level of use' and specifying it as a formative construct has greater value for measuring the post-adoption use of feature rich systems. We then validate the content of the construct as a first step in developing a research instrument to measure it. The context of our study is the post-adoption use of electronic medical records (EMR) by primary care physicians. Method. Initially, a literature review of the empirical context defines the scope based on prior studies. Having identified core features from the literature, they are further refined with the help of experts in a consensus seeking process that follows the Delphi technique. Results.The methodology was successfully applied to EMRs, which were selected as an example of feature rich systems. A review of EMR usage and regulatory standards provided the feature input for the first round of the Delphi process. A panel of experts then reached consensus after four rounds, identifying ten task-based features that would be indicators of level of use. Conclusions. To study why some users deploy more advanced features than others, theories of post-adoption require a rich formative dependent variable that measures level of use. We have demonstrated that a context sensitive literature review followed by refinement through a consensus seeking process is a suitable methodology to validate the content of this dependent variable. This is the first step of instrument development prior to statistical confirmation with a larger sample.

Infrared face recogniton by optimum-path forest

This paper presents a novel, fast and accurate appearance-based method for infrared face recognition. By introducing the Optimum-Path Forest classifier, our objective is to get good recognition rates and effectively reduce the computational effort. The feature extraction procedure is carried out by PCA, and the results are compared to two other well known supervised learning classifiers; Artificial Neural Networks and Support Vector Machines. The achieved performance asserts the promise of the proposed framework. ©2009 IEEE.

Predicting the connectivity of primate cortical networks from topological and spatial node properties

Abstract Background The organization of the connectivity between mammalian cortical areas has become a major subject of study, because of its important role in scaffolding the macroscopic aspects of animal behavior and intelligence. In this study we present a computational reconstruction approach to the problem of network organization, by considering the topological and spatial features of each area in the primate cerebral cortex as subsidy for the reconstruction of the global cortical network connectivity. Starting with all areas being disconnected, pairs of areas with similar sets of features are linked together, in an attempt to recover the original network structure. Results Inferring primate cortical connectivity from the properties of the nodes, remarkably good reconstructions of the global network organization could be obtained, with the topological features allowing slightly superior accuracy to the spatial ones. Analogous reconstruction attempts for the C. elegans neuronal network resulted in substantially poorer recovery, indicating that cortical area interconnections are relatively stronger related to the considered topological and spatial properties than neuronal projections in the nematode. Conclusion The close relationship between area-based features and global connectivity may hint on developmental rules and constraints for cortical networks. Particularly, differences between the predictions from topological and spatial properties, together with the poorer recovery resulting from spatial properties, indicate that the organization of cortical networks is not entirely determined by spatial constraints.

Link prediction using a probabilistic description logic

Due to the growing interest in social networks, link prediction has received significant attention. Link prediction is mostly based on graph-based features, with some recent approaches focusing on domain semantics. We propose algorithms for link prediction that use a probabilistic ontology to enhance the analysis of the domain and the unavoidable uncertainty in the task (the ontology is specified in the probabilistic description logic crALC). The scalability of the approach is investigated, through a combination of semantic assumptions and graph-based features. We evaluate empirically our proposal, and compare it with standard solutions in the literature.

Towards Automatic Identification of Discourse Markers in Dialogs: the Case of 'Like'

This article discusses the detection of discourse markers (DM) in dialog transcriptions, by human annotators and by automated means. After a theoretical discussion of the definition of DMs and their relevance to natural language processing, we focus on the role of like as a DM. Results from experiments with human annotators show that detection of DMs is a difficult but reliable task, which requires prosodic information from soundtracks. Then, several types of features are defined for automatic disambiguation of like: collocations, part-of-speech tags and duration-based features. Decision-tree learning shows that for like, nearly 70% precision can be reached, with near 100% recall, mainly using collocation filters. Similar results hold for well, with about 91% precision at 100% recall.

Improvement for detection of microcalcifications through clustering algorithms and artificial neural networks

A new method for detecting microcalcifications in regions of interest (ROIs) extracted from digitized mammograms is proposed. The top-hat transform is a technique based on mathematical morphology operations and, in this paper, is used to perform contrast enhancement of the mi-crocalcifications. To improve microcalcification detection, a novel image sub-segmentation approach based on the possibilistic fuzzy c-means algorithm is used. From the original ROIs, window-based features, such as the mean and standard deviation, were extracted; these features were used as an input vector in a classifier. The classifier is based on an artificial neural network to identify patterns belonging to microcalcifications and healthy tissue. Our results show that the proposed method is a good alternative for automatically detecting microcalcifications, because this stage is an important part of early breast cancer detection