Cognitive intervention in autism using multimedia stimulus

We demonstrate an open multimedia-based system for delivering early intervention therapy for autism. Using exible multi-touch interfaces together with principled ways to access rich content and tasks, we show how a syllabus can be translated into stimulus sets for early intervention. Media stimuli are able to be presented agnostic to language and media modality due to a semantic network of concepts and relations that are fundamental to language and cognitive development, which enable stimulus complexity to be adjusted to child performance. Being open, the system is able to assemble enough media stimuli to avoid children over-learning, and is able to be customised to a specific child which aids with engagement. Computer-based delivery enables automation of session logging and reporting, a fundamental and time-consuming part of therapy.

The Tablet PC: A Complete Teaching Studio

The Tablet PC is a flexible teaching tool. It can be used to increase the lecturer’s productivity in note taking and in assignment marking. It can be used in the lecture room with increased interaction. With a few minor accessories it can be used to record all aspects of a lecture or presentation. It can also be used to record short topic segments that can be used as references or summaries by students. Containing the abilities of both a tablet device with multi touch, a pen interface for accurate drawing and handwriting and with the power of a full PC, it is a complete teaching studio.

Robust modular artmap for multi-class shape recognition

This paper presents a fuzzy ARTMAP (FAM) based modular architecture for multi-class pattern recognition known as modular adaptive resonance theory map (MARTMAP). The prediction of class membership is made collectively by combining outputs from multiple novelty detectors. Distance-based familiarity discrimination is introduced to improve the robustness of MARTMAP in the presence of noise. The effectiveness of the proposed architecture is analyzed and compared with ARTMAP-FD network, FAM network, and One-Against-One Support Vector Machine (OAO-SVM). Experimental results show that MARTMAP is able to retain effective familiarity discrimination in noisy environment, and yet less sensitive to class imbalance problem as compared to its counterparts.

Optimal metric selection for improved multi-pose face recognition with group information

We address the limitation of sparse representation based classification with group information for multi-pose face recognition. First, we observe that the key issue of such classification problem lies in the choice of the metric norm of the residual vectors, which represent the fitness of each class. Then we point out that limitation of the current sparse representation classification algorithms is the wrong choice of the ℓ2 norm, which does not match with data statistics as these residual values may be considerably non-Gaussian. We propose an explicit but effective solution using ℓp norm and explain theoretically and numerically why such metric norm would be able to suppress outliers and thus can significantly improve classification performance comparable to the state-of-arts algorithms on some challenging datasets

Dementia in older people admitted to hospital: a regional multi-hospital observational study of prevalence, associations and case recognition

BACKGROUND: previous studies have indicated a prevalence of dementia in older admissions of ∼42% in a single London teaching hospital, and 21% in four Queensland hospitals. However, there is a lack of published data from any European country on the prevalence of dementia across hospitals and between patient groups. OBJECTIVE: to determine the prevalence and associations of dementia in older patients admitted to acute hospitals in Ireland. METHODS: six hundred and six patients aged ≥70 years were recruited on admission to six hospitals in Cork County. Screening consisted of Standardised Mini-Mental State Examination (SMMSE); patients with scores <27/30 had further assessment with the Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE). Final expert diagnosis was based on SMMSE, IQCODE and relevant medical and demographic history. Patients were screened for delirium and depression, and assessed for co-morbidity, functional ability and nutritional status. RESULTS: of 598 older patients admitted to acute hospitals, 25% overall had dementia; with 29% in public hospitals. Prevalence varied between hospitals (P < 0.001); most common in rural hospitals and acute medical admissions. Only 35.6% of patients with dementia had a previous diagnosis. Patients with dementia were older and frailer, with higher co-morbidity, malnutrition and lower functional status (P < 0.001). Delirium was commonly superimposed on dementia (57%) on admission. CONCLUSION: dementia is common in older people admitted to acute hospitals, particularly in acute medical admissions, and rural hospitals, where services may be less available. Most dementia is not previously diagnosed, emphasising the necessity for cognitive assessment in older people on presentation to hospital.

Mixed-norm sparse representation for multi view face recognition

Face recognition with multiple views is a challenging research problem. Most of the existing works have focused on extracting shared information among multiple views to improve recognition. However, when the pose variation is too large or missing, 'shared information' may not be properly extracted, leading to poor recognition results. In this paper, we propose a novel method for face recognition with multiple view images to overcome the large pose variation and missing pose issue. By introducing a novel mixed norm, the proposed method automatically selects candidates from the gallery to best represent a group of highly correlated face images in a query set to improve classification accuracy. This mixed norm combines the advantages of both sparse representation based classification (SRC) and joint sparse representation based classification (JSRC). A trade off between the ℓ1-norm from SRC and ℓ2,1-norm from JSRC is introduced to achieve this goal. Due to this property, the proposed method decreases the influence when a face image is unseen and has large pose variation in the recognition process. And when some face images with a certain degree of unseen pose variation appear, this mixed norm will find an optimal representation for these query images based on the shared information induced from multiple views. Moreover, we also address an open problem in robust sparse representation and classification which is using ℓ1-norm on the loss function to achieve a robust solution. To solve this formulation, we derive a simple, yet provably convergent algorithm based on the powerful alternative directions method of multipliers (ADMM) framework. We provide extensive comparisons which demonstrate that our method outperforms other state-of-the-arts algorithms on CMU-PIE, Yale B and Multi-PIE databases for multi-view face recognition.

Multi-jurisdictional merger review procedures - a better way

Corporate mergers whose effects transcend national borders have faced increasing regulation over the past few decades as more jurisdictions have developed merger laws and imposed pre-merger notification requirements. The level of regulatory response to multi-jurisdictional mergers is likely to continue to increase as even more jurisdictions contemplate the introduction of competition laws. This level of regulation now goes beyond that required to protect national economies from potentially harmful mergers and has seen burgeoning costs to business, regulators and, ultimately, the public. In recognition of this, the relatively newly formed International Competition Network has placed merger regulation at the forefront of its agenda for greater harmonisation and cooperation in competition law. This has seen, over the past three years, the development of a set of guiding principles and recommended practices for merger notification procedures designed to reduce the regulatory burden. This article evaluates these recommendations and discusses areas for further reform.

Dynamic biometrics fusion at feature level for video-based human recognition

This paper proposes a novel human recognition method in video, which combines human face and gait traits
using a dynamic multi-modal biometrics fusion scheme. The Fisherface approach is adopted to extract face
features, while for gait features, Locality Preserving Projection (LPP) is used to achieve low-dimensional
manifold embedding of the temporal silhouette data derived from image sequences. Face and gait features are
fused dynamically at feature level based on a distance-driven fusion method. Encouraging experimental results
are achieved on the video sequences containing 20 people, which show that dynamically fused features produce
a more discriminating power than any individual biometric as well as integrated features built on common static
fusion schemes.

Driver verification based on handgrip recognition on steering wheel

This paper presents a novel driver verification algorithm based on the recognition of handgrip patterns on steering wheel. A pressure sensitive mat mounted on a steering wheel is employed to collect a series of pressure images exerted by the hands of the drivers who intend to start the vehicle. Then, feature extraction from those images is carried out through two major steps: Quad-Tree-based multi-resolution decomposition on the images and Principle Component Analysis (PCA)-based dimension reduction, followed by implementing a likelihood-ratio classifier to distinguish drivers into known or unknown ones. The experimental results obtained in this study show that the mean acceptance rates of 78.15% and 78.22% for the trained subjects and the mean rejection rates of 93.92% and 90.93% to the un-trained ones are achieved in two trials, respectively. It can be concluded that the driver verification approach based on the handgrip recognition on steering wheel is promising and will be further explored in the near future.

The water harvesting landscape of Budj Bim and Lake Condah : whither world heritage recognition

In July 2004 the Budj Bim National Heritage Landscape was inscribed onto the National Heritage List. The place accorded with the criterion of A. Events, Processes (in demonstrating a place of Indigenous-European colonization conflict), B. Rarity (in demonstrating the context, historical and philosophy of benevolence of Governments to Indigenous people), F. Creative or technical achievement (in demonstrating technical accomplishment in construction the system), and, I. Indigenous tradition (in demonstrating longevity and continuity of cultural practices). Such affords Budj Bim, that hosts a unique Indigenous water harvesting and aquaculture infrastructure system dating some 7,000-10,000 years within a country that the Gunditjmara have managed for some 20,000-50,000 years, national standing. Within the lands gazetted is a complex and multi-faceted system that would today be categorised as a major integrated landscape planning and catchment management scheme that includes demonstrable major site engineering, hydraulic engineering, and aquaculture and water management scientific evidence and process knowledge and application.

Use of log polar space for foveation and feature recognition

The finding and maintaining of high accuracy foveation points for several types of recognised feature in log polar space such as a line, circular or elliptical arc is considered. Log polar space is preferred over cartesian space as it provides a high resolution and a wide viewing angle; feature invariance in the fovea simplifies foveation; it allows multi-resolution analysis; and rotation and scale are linear translations in log polar space.

Face recognition using kernel ridge regression

In this paper, we present novel ridge regression (RR) and kernel ridge regression (KRR) techniques for multivariate labels and apply the methods to the problem of face recognition. Motivated by the fact that the regular simplex vertices are separate points with highest degree of symmetry, we choose such vertices as the targets for the distinct individuals in recognition and apply RR or KRR to map the training face images into a face subspace where the training images from each individual will locate near their individual targets. We identify the new face image by mapping it into this face subspace and comparing its distance to all individual targets. An efficient cross-validation algorithm is also provided for selecting the regularization and kernel parameters. Experiments were conducted on two face databases and the results demonstrate that the proposed algorithm significantly outperforms the three popular linear face recognition techniques (Eigenfaces, Fisherfaces and Laplacianfaces) and also performs comparably with the recently developed Orthogonal Laplacianfaces with the advantage of computational speed. Experimental results also demonstrate that KRR outperforms RR as expected since KRR can utilize the nonlinear structure of the face images. Although we concentrate on face recognition in this paper, the proposed method is general and may be applied for general multi-category classification problems.

Augmented optometry training simulator with multi-point haptics

Training of optometrists is traditionally achieved under close supervision of peers and superiors. With the rapid advancement in technology, medical procedures are performed more efficiently and effectively, resulting in faster recovery times and less trauma to the patient. However, application of this technology has made it difficult to effectively demonstrate and teach these manual skills as the education is now a combination of not only the medical procedure but also the use of the technology. In this paper we propose to increase the training capabilities of optometry students through haptically-enabled single-point and multi-point training tools as well as augmented reality techniques. Haptics technology allows a human to touch and feel virtual computer models as though they are real. Through physical connection to the operator, haptic devices are considered to be personal robots that are capable of improving the human-computer interaction with a virtual environment. These devices have played an increasing role in developing expertise, reducing instances of medical error and reducing training costs. A haptically-enabled virtual training environment, integrated with an optometry slit lamp instrument can be used to teach cognitive and manual skills while the system tracks the performance of each individual. These interactions would ideally replicate every aspect of the real procedure, consequently preparing the trainee for every possible scenario, without risking the health of a real patient.