Computer attitude and classroom computers

Computers not only increase the speed and efficiency of our mental efforts, but in the process they also alter the problem-solving tasks we are faced with and, in so doing, they alter the cognitive processes we use to solve problems. Computers are fundamentally changing our forms of thinking (Colc & Griffin, 1980). Therefore, the computer should be seen as not only having the potential to amplify human mental capabilities, but also of providing a catalyst for intellectual development.

Towards continuous surveillance of fruit flies using sensor networks and machine vision

In Australia, the Queensland fruit fly (B. tryoni), is the most destructive insect pest of horticulture, attacking nearly all fruit and vegetable crops. This project has researched and prototyped a system for monitoring fruit flies so that authorities can be alerted when a fly enters a crop in a more efficient manner than is currently used. This paper presents the idea of our sensor platform design as well as the fruit fly detection and recognition algorithm by using machine vision techniques. Our experiments showed that the designed trap and sensor platform is capable to capture quality fly images, the invasive flies can be successfully detected and the average precision of the Queensland fruit fly recognition is 80% from our experiment.

Mid-level concept learning with visual contextual ontologies and probabilistic inference for image annotation

To date, automatic recognition of semantic information such as salient objects and mid-level concepts from images is a challenging task. Since real-world objects tend to exist in a context within their environment, the computer vision researchers have increasingly incorporated contextual information for improving object recognition. In this paper, we present a method to build a visual contextual ontology from salient objects descriptions for image annotation. The ontologies include not only partOf/kindOf relations, but also spatial and co-occurrence relations. A two-step image annotation algorithm is also proposed based on ontology relations and probabilistic inference. Different from most of the existing work, we specially exploit how to combine representation of ontology, contextual knowledge and probabilistic inference. The experiments show that image annotation results are improved in the LabelMe dataset.

Adaptive unsupervised learning of human actions

Automatic detection of suspicious activities in CCTV camera feeds is crucial to the success of video surveillance systems. Such a capability can help transform the dumb CCTV cameras into smart surveillance tools for fighting crime and terror. Learning and classification of basic human actions is a precursor to detecting suspicious activities. Most of the current approaches rely on a non-realistic assumption that a complete dataset of normal human actions is available. This paper presents a different approach to deal with the problem of understanding human actions in video when no prior information is available. This is achieved by working with an incomplete dataset of basic actions which are continuously updated. Initially, all video segments are represented by Bags-Of-Words (BOW) method using only Term Frequency-Inverse Document Frequency (TF-IDF) features. Then, a data-stream clustering algorithm is applied for updating the system's knowledge from the incoming video feeds. Finally, all the actions are classified into different sets. Experiments and comparisons are conducted on the well known Weizmann and KTH datasets to show the efficacy of the proposed approach.

Biomechanical force displacement analysis of strength of fixation of tracker holding devices to bone in computer aided joint replacement

Computer aided joint replacement surgery has become very popular during recent years and is being done in increasing numbers all over the world. The accuracy of the system depends to a major extent, on accurate registration and immobility of the tracker attachment devices to the bone. This study was designed to asses the forces needed to displace the tracker attachment devices in the bone simulators. Bone simulators were used to maintain the uniformity of the bone structure during the study. The fixation devices tested were 3mm diameter self drilling, self tapping threaded pin, 4mm diameter self tapping cortical threaded pin, 5mm diameter self tapping cancellous threaded pin and a triplanar fixation device ‘ortholock’ used with three 3mm pins. All the devices were tested for pull out, translational and rotational forces in unicortical and bicortical fixation modes. Also tested was the normal bang strength and forces generated by leaning on the devices. The forces required to produce translation increased with the increasing diameter of the pins. These were 105N, 185N, and 225N for the unicortical fixations and 130N, 200N, 225N for the bicortical fixations for 3mm, 4mm and 5mm diameter pins respectively. The forces required to pull out the pins were 1475N, 1650N, 2050N for the unicortical, 1020N, 3044N and 3042N for the bicortical fixated 3mm, 4mm and 5mm diameter pins. The ortholock translational and pull out strength was tested to 900N and 920N respectively and still it did not fail. Rotatory forces required to displace the tracker on pins was to the magnitude of 30N before failure. The ortholock device had rotational forces applied up to 135N and still did not fail. The manual leaning forces and the sudden bang forces generated were of the magnitude of 210N and 150N respectively. The strength of the fixation pins increases with increasing diameter from three to five mm for the translational forces. There is no significant difference in pull out forces of four mm and five mm diameter pins though it is more that the three mm diameter pins. This is because of the failure of material at that stage rather than the fixation device. The rotatory forces required to displace the tracker are very small and much less that that can be produced by the surgeon or assistants in single pins. Although the ortholock device was tested to 135N in rotation without failing, one has to be very careful not to put any forces during the operation on the tracker devices to ensure the accuracy of the procedure.

Automating human thought processes for a UAV forced landing

This paper describes the current status of a program to develop an automated forced landing system for a fixed-wing Unmanned Aerial Vehicle (UAV). This automated system seeks to emulate human pilot thought processes when planning for and conducting an engine-off emergency landing. Firstly, a path planning algorithm that extends Dubins curves to 3D space is presented. This planning element is then combined with a nonlinear guidance and control logic, and simulated test results demonstrate the robustness of this approach to strong winds during a glided descent. The average path deviation errors incurred are comparable to or even better than that of manned, powered aircraft. Secondly, a study into suitable multi-criteria decision making approaches and the problems that confront the decision-maker is presented. From this study, it is believed that decision processes that utilize human expert knowledge and fuzzy logic reasoning are most suited to the problem at hand, and further investigations will be conducted to identify the particular technique/s to be implemented in simulations and field tests. The automated UAV forced landing approach presented in this paper is promising, and will allow the progression of this technology from the development and simulation stages through to a prototype system

QoS-Based Web Service Scheduling Accommodating Inter-Service Dependencies Using Minimal-Conflict Hill-Climbing Repair Genetic Algorithm

In the filed of semantic grid, QoS-based Web service scheduling for workflow optimization is an important problem.However, in semantic and service rich environment like semantic grid, the emergence of context constraints on Web services is very common making the scheduling consider not only quality properties of Web services, but also inter service dependencies which are formed due to the context constraints imposed on Web services. In this paper, we present a repair genetic algorithm, namely minimal-conflict hill-climbing repair genetic algorithm, to address scheduling optimization problems in workflow applications in the presence of domain constraints and inter service dependencies. Experimental results demonstrate the scalability and effectiveness of the genetic algorithm.

Toward a fuzzy domain ontology extraction method for adaptive e-learning

With the widespread applications of electronic learning (e-Learning) technologies to education at all levels, increasing number of online educational resources and messages are generated from the corresponding e-Learning environments. Nevertheless, it is quite difficult, if not totally impossible, for instructors to read through and analyze the online messages to predict the progress of their students on the fly. The main contribution of this paper is the illustration of a novel concept map generation mechanism which is underpinned by a fuzzy domain ontology extraction algorithm. The proposed mechanism can automatically construct concept maps based on the messages posted to online discussion forums. By browsing the concept maps, instructors can quickly identify the progress of their students and adjust the pedagogical sequence on the fly. Our initial experimental results reveal that the accuracy and the quality of the automatically generated concept maps are promising. Our research work opens the door to the development and application of intelligent software tools to enhance e-Learning.

Merging business process models

This paper addresses the following problem: given two or more business process models, create a process model that is the union of the process models given as input. In other words, the behavior of the produced process model should encompass that of the input models. The paper describes an algorithm that produces a single configurable process model from an arbitrary collection of process models. The algorithm works by extracting the common parts of the input process models, creating a single copy of them, and appending the differences as branches of configurable connectors. This way, the merged process model is kept as small as possible, while still capturing all the behavior of the input models. Moreover, analysts are able to trace back from which original model(s) does a given element in the merged model come from. The algorithm has been prototyped and tested against process models taken from several application domains.

Towards automatic tree crown detection and delineation in spectral feature space using PCNN and morphological reconstruction

The application of object-based approaches to the problem of extracting vegetation information from images requires accurate delineation of individual tree crowns. This paper presents an automated method for individual tree crown detection and delineation by applying a simplified PCNN model in spectral feature space followed by post-processing using morphological reconstruction. The algorithm was tested on high resolution multi-spectral aerial images and the results are compared with two existing image segmentation algorithms. The results demonstrate that our algorithm outperforms the other two solutions with the average accuracy of 81.8%.

Prediction of adjacent vertebral fracture risk associated with vertebroplasty : a computer-based simulation study

Vertebrplasty involved injecting cement into a fractured vertebra to provide stabilisation. There is clinical evidence to suggest however that vertebroplasty may be assocated with a higher risk of adjacent vertebral fracture; which may be due to the change in material properties of the post-procedure vertebra modifying the transmission of mechanical stresses to adjacent vertebrae.

Mining Negative Relevance Feedback for Information Filtering

It is a big challenge to clearly identify the boundary between positive and negative streams. Several attempts have used negative feedback to solve this challenge; however, there are two issues for using negative relevance feedback to improve the effectiveness of information filtering. The first one is how to select constructive negative samples in order to reduce the space of negative documents. The second issue is how to decide noisy extracted features that should be updated based on the selected negative samples. This paper proposes a pattern mining based approach to select some offenders from the negative documents, where an offender can be used to reduce the side effects of noisy features. It also classifies extracted features (i.e., terms) into three categories: positive specific terms, general terms, and negative specific terms. In this way, multiple revising strategies can be used to update extracted features. An iterative learning algorithm is also proposed to implement this approach on RCV1, and substantial experiments show that the proposed approach achieves encouraging performance.

Improved detection and tracking of objects in surveillance video

Surveillance networks are typically monitored by a few people, viewing several monitors displaying the camera feeds. It is then very difficult for a human operator to effectively detect events as they happen. Recently, computer vision research has begun to address ways to automatically process some of this data, to assist human operators. Object tracking, event recognition, crowd analysis and human identification at a distance are being pursued as a means to aid human operators and improve the security of areas such as transport hubs. The task of object tracking is key to the effective use of more advanced technologies. To recognize an event people and objects must be tracked. Tracking also enhances the performance of tasks such as crowd analysis or human identification. Before an object can be tracked, it must be detected. Motion segmentation techniques, widely employed in tracking systems, produce a binary image in which objects can be located. However, these techniques are prone to errors caused by shadows and lighting changes. Detection routines often fail, either due to erroneous motion caused by noise and lighting effects, or due to the detection routines being unable to split occluded regions into their component objects. Particle filters can be used as a self contained tracking system, and make it unnecessary for the task of detection to be carried out separately except for an initial (often manual) detection to initialise the filter. Particle filters use one or more extracted features to evaluate the likelihood of an object existing at a given point each frame. Such systems however do not easily allow for multiple objects to be tracked robustly, and do not explicitly maintain the identity of tracked objects. This dissertation investigates improvements to the performance of object tracking algorithms through improved motion segmentation and the use of a particle filter. A novel hybrid motion segmentation / optical flow algorithm, capable of simultaneously extracting multiple layers of foreground and optical flow in surveillance video frames is proposed. The algorithm is shown to perform well in the presence of adverse lighting conditions, and the optical flow is capable of extracting a moving object. The proposed algorithm is integrated within a tracking system and evaluated using the ETISEO (Evaluation du Traitement et de lInterpretation de Sequences vidEO - Evaluation for video understanding) database, and significant improvement in detection and tracking performance is demonstrated when compared to a baseline system. A Scalable Condensation Filter (SCF), a particle filter designed to work within an existing tracking system, is also developed. The creation and deletion of modes and maintenance of identity is handled by the underlying tracking system; and the tracking system is able to benefit from the improved performance in uncertain conditions arising from occlusion and noise provided by a particle filter. The system is evaluated using the ETISEO database. The dissertation then investigates fusion schemes for multi-spectral tracking systems. Four fusion schemes for combining a thermal and visual colour modality are evaluated using the OTCBVS (Object Tracking and Classification in and Beyond the Visible Spectrum) database. It is shown that a middle fusion scheme yields the best results and demonstrates a significant improvement in performance when compared to a system using either mode individually. Findings from the thesis contribute to improve the performance of semi-automated video processing and therefore improve security in areas under surveillance.

Primary students' group metacognitive processes in a computer supported collaborative learning environment

The current understanding of students’ group metacognition is limited. The research on metacognition has focused mainly on the individual student. The aim of this study was to address the void by developing a conceptual model to inform the use of scaffolds to facilitate group metacognition during mathematical problem solving in computer supported collaborative learning (CSCL) environments. An initial conceptual framework based on the literature from metacognition, cooperative learning, cooperative group metacognition, and computer supported collaborative learning was used to inform the study. In order to achieve the study aim, a design research methodology incorporating two cycles was used. The first cycle focused on the within-group metacognition for sixteen groups of primary school students working together around the computer; the second cycle included between-group metacognition for six groups of primary school students working together on the Knowledge Forum® CSCL environment. The study found that providing groups with group metacognitive scaffolds resulted in groups planning, monitoring, and evaluating the task and team aspects of their group work. The metacognitive scaffolds allowed students to focus on how their group was completing the problem-solving task and working together as a team. From these findings, a revised conceptual model to inform the use of scaffolds to facilitate group metacognition during mathematical problem solving in computer supported collaborative learning (CSCL) environments was generated.

Measuring Visual Consistency in 3D Rendering Systems

One of the major challenges facing a present day game development company is the removal of bugs from such complex virtual environments. This work presents an approach for measuring the correctness of synthetic scenes generated by a rendering system of a 3D application, such as a computer game. Our approach builds a database of labelled point clouds representing the spatiotemporal colour distribution for the objects present in a sequence of bug-free frames. This is done by converting the position that the pixels take over time into the 3D equivalent points with associated colours. Once the space of labelled points is built, each new image produced from the same game by any rendering system can be analysed by measuring its visual inconsistency in terms of distance from the database. Objects within the scene can be relocated (manually or by the application engine); yet the algorithm is able to perform the image analysis in terms of the 3D structure and colour distribution of samples on the surface of the object. We applied our framework to the publicly available game RacingGame developed for Microsoft(R) Xna(R). Preliminary results show how this approach can be used to detect a variety of visual artifacts generated by the rendering system in a professional quality game engine.