An evaluation of different features and learning models for anomalous event detection

The huge amount of CCTV footage available makes it very burdensome to process these videos manually through human operators. This has made automated processing of video footage through computer vision technologies necessary. During the past several years, there has been a large effort to detect abnormal activities through computer vision techniques. Typically, the problem is formulated as a novelty detection task where the system is trained on normal data and is required to detect events which do not fit the learned ‘normal’ model. There is no precise and exact definition for an abnormal activity; it is dependent on the context of the scene. Hence there is a requirement for different feature sets to detect different kinds of abnormal activities. In this work we evaluate the performance of different state of the art features to detect the presence of the abnormal objects in the scene. These include optical flow vectors to detect motion related anomalies, textures of optical flow and image textures to detect the presence of abnormal objects. These extracted features in different combinations are modeled using different state of the art models such as Gaussian mixture model(GMM) and Semi- 2D Hidden Markov model(HMM) to analyse the performances. Further we apply perspective normalization to the extracted features to compensate for perspective distortion due to the distance between the camera and objects of consideration. The proposed approach is evaluated using the publicly available UCSD datasets and we demonstrate improved performance compared to other state of the art methods.

A survey of fingerprint classification Part I: taxonomies on feature extraction methods and learning models

This paper reviews the fingerprint classification literature looking at the problem from a double perspective. We first deal with feature extraction methods, including the different models considered for singular point detection and for orientation map extraction. Then, we focus on the different learning models considered to build the classifiers used to label new fingerprints. Taxonomies and classifications for the feature extraction, singular point detection, orientation extraction and learning methods are presented. A critical view of the existing literature have led us to present a discussion on the existing methods and their drawbacks such as difficulty in their reimplementation, lack of details or major differences in their evaluations procedures. On this account, an experimental analysis of the most relevant methods is carried out in the second part of this paper, and a new method based on their combination is presented.

Prediction Of Hydrological Models’ Uncertainty By A Committee Of Machine Learning-Models

This study presents an approach to combine uncertainties of the hydrological model outputs predicted from a number of machine learning models. The machine learning based uncertainty prediction approach is very useful for estimation of hydrological models' uncertainty in particular hydro-metrological situation in real-time application [1]. In this approach the hydrological model realizations from Monte Carlo simulations are used to build different machine learning uncertainty models to predict uncertainty (quantiles of pdf) of the a deterministic output from hydrological model . Uncertainty models are trained using antecedent precipitation and streamflows as inputs. The trained models are then employed to predict the model output uncertainty which is specific for the new input data. We used three machine learning models namely artificial neural networks, model tree, locally weighted regression to predict output uncertainties. These three models produce similar verification results, which can be improved by merging their outputs dynamically. We propose an approach to form a committee of the three models to combine their outputs. The approach is applied to estimate uncertainty of streamflows simulation from a conceptual hydrological model in the Brue catchment in UK and the Bagmati catchment in Nepal. The verification results show that merged output is better than an individual model output. [1] D. L. Shrestha, N. Kayastha, and D. P. Solomatine, and R. Price. Encapsulation of parameteric uncertainty statistics by various predictive machine learning models: MLUE method, Journal of Hydroinformatic, in press, 2013.

Learning Models of Gene Expression from Synthetic DNA Sequences

Thesis (Ph.D.)--University of Washington, 2016-06

Improving learning in undergraduate control engineering courses using context-based learning models

Control Engineering is an essential part of university electrical engineering education. Normally, a control course requires considerable mathematical as well as engineering knowledge and is consequently regarded as a difficult course by many undergraduate students. From the academic point of view, how to help the students to improve their learning of the control engineering knowledge is therefore an important task which requires careful planning and innovative teaching methods. Traditionally, the didactic teaching approach has been used to teach the students the concepts needed to solve control problems. This approach is commonly adopted in many mathematics intensive courses; however it generally lacks reflection from the students to improve their learning. This paper addresses the practice of action learning and context-based learning models in teaching university control courses. This context-based approach has been practised in teaching several control engineering courses in a university with promising results, particularly in view of student learning performances.

Datasets, features, learning, and models in visual recognition

Visual recognition is a fundamental research topic in computer vision. This dissertation explores datasets, features, learning, and models used for visual recognition. In order to train visual models and evaluate different recognition algorithms, this dissertation develops an approach to collect object image datasets on web pages using an analysis of text around the image and of image appearance. This method exploits established online knowledge resources (Wikipedia pages for text; Flickr and Caltech data sets for images). The resources provide rich text and object appearance information. This dissertation describes results on two datasets. The first is Berg’s collection of 10 animal categories; on this dataset, we significantly outperform previous approaches. On an additional set of 5 categories, experimental results show the effectiveness of the method. Images are represented as features for visual recognition. This dissertation introduces a text-based image feature and demonstrates that it consistently improves performance on hard object classification problems. The feature is built using an auxiliary dataset of images annotated with tags, downloaded from the Internet. Image tags are noisy. The method obtains the text features of an unannotated image from the tags of its k-nearest neighbors in this auxiliary collection. A visual classifier presented with an object viewed under novel circumstances (say, a new viewing direction) must rely on its visual examples. This text feature may not change, because the auxiliary dataset likely contains a similar picture. While the tags associated with images are noisy, they are more stable when appearance changes. The performance of this feature is tested using PASCAL VOC 2006 and 2007 datasets. This feature performs well; it consistently improves the performance of visual object classifiers, and is particularly effective when the training dataset is small. With more and more collected training data, computational cost becomes a bottleneck, especially when training sophisticated classifiers such as kernelized SVM. This dissertation proposes a fast training algorithm called Stochastic Intersection Kernel Machine (SIKMA). This proposed training method will be useful for many vision problems, as it can produce a kernel classifier that is more accurate than a linear classifier, and can be trained on tens of thousands of examples in two minutes. It processes training examples one by one in a sequence, so memory cost is no longer the bottleneck to process large scale datasets. This dissertation applies this approach to train classifiers of Flickr groups with many group training examples. The resulting Flickr group prediction scores can be used to measure image similarity between two images. Experimental results on the Corel dataset and a PASCAL VOC dataset show the learned Flickr features perform better on image matching, retrieval, and classification than conventional visual features. Visual models are usually trained to best separate positive and negative training examples. However, when recognizing a large number of object categories, there may not be enough training examples for most objects, due to the intrinsic long-tailed distribution of objects in the real world. This dissertation proposes an approach to use comparative object similarity. The key insight is that, given a set of object categories which are similar and a set of categories which are dissimilar, a good object model should respond more strongly to examples from similar categories than to examples from dissimilar categories. This dissertation develops a regularized kernel machine algorithm to use this category dependent similarity regularization. Experiments on hundreds of categories show that our method can make significant improvement for categories with few or even no positive examples.

Internationalisation and international students : creating an internationalised learning context for international students’ study preparation via CMC

Australian universities have been phenomenally internationalised because of significant numbers of international students in their student cohorts. The international students make up 17.3 percent (OECD 2007) of all the university enrolment, and some universities have much more international student enrolments than the average. From a truly internationalisation perspective, however, there is far more demand of integration with Australian students and international students, the internationalising learning content and context. There have not been much discussion and effort of understanding and practicing of internationalising the learning context from international students’ cultural background and internationalised learning environment. There are many factors which interfere with internationalisation in the learning context such as English proficiency, culture difference and academic staff unawareness. This paper argues the concepts of cultural dimensions and the characteristics of CMC (Computer-Mediated Communication) in a multicultural learning context of Australian higher education. This paper aims to develop a framework of international students’ preparation program for their Western university study based on technology-driven learning models, especially targeting those students who have an Asian cultural background. The program is expected to help international students bridge the gap of cultural differences and better preparation for their active participation and engagement in a new learning environment in order to realise truly internationalisation in Australian higher education

What are students' understandings of how digital tools contribute to learning in design disciplines?

Building Information Modelling (BIM) is evolving in the Construction Industry as a successor to CAD. CAD is mostly a technical tool that conforms to existing industry practices, however BIM has the capacity to revolutionise industry practice. Rather than producing representations of design intent, BIM produces an exact Virtual Prototype of any building that in an ideal situation is centrally stored and freely exchanged between the project team, facilitating collaboration and allowing experimentation in design. Exposing design students to this technology through their formal studies allows them to engage with cutting edge industry practices and to help shape the industry upon their graduation. Since this technology is relatively new to the construction industry, there are no accepted models for how to “teach” BIM effectively at university level. Developing learning models to enable students to make the most out of their learning with BIM presents significant challenges to those teaching in the field of design. To date there are also no studies of students experiences of using this technology. This research reports on the introduction of Building Information Modeling (BIM) software into a second year Bachelor of Design course. This software has the potential to change industry standards through its ability to revolutionise the work practices of those involved in large scale design projects. Students’ understandings and experiences of using the software in order to complete design projects as part of their assessment are reported here. In depth semi-structured interviews with 6 students revealed that students had views that ranged from novice to sophisticate about the software. They had variations in understanding of how the software could be used to complete course requirements, to assist with the design process and in the workplace. They had engaged in limited exploration of the collaborative potential of the software as a design tool. Their understanding of the significance of BIM for the workplace was also variable. The results indicate that students are beginning to develop an appreciation for how BIM could aid or constrain the work of designers, but that this appreciation is highly varied and likely to be dependent on the students’ previous experiences of working in a design studio environment. Their range of understandings of the significance of the technology is a reflection of their level of development as designers (they are “novice” designers). The results also indicate that there is a need for subjects in later years of the course that allow students to specialise in the area of digital design and to develop more sophisticated views of the role of technology in the design process. There is also a need to capitalise on the collaborative potential inherent in the software in order to realise its capability to streamline some aspects of the design process. As students become more sophisticated designers we should explore their understanding of the role of technology as a design tool in more depth in order to make recommendations for improvements to teaching and learning practice related to BIM and other digital design tools.

Revisiting 'how we learn' in academia : practice-based learning exchanges in three Australian universities

Ideas of 'how we learn' in formal academic settings have changed markedly in recent decades. The primary position that universities once held on shaping what constitutes learning has come into question from a range of experience-led and situated learning models. Drawing on findings from a study conducted across three Australian universities, the article focuses on the multifarious learning experiences indicative of practice-based learning exchanges such as student placements. Building on both experiential and situated learning theories, the authors found that students can experience transformative and emotional elucidations of learning, that can challenge tacit assumptions and transform the ways they understand the world. It was found that all participants (hosts, students, academics) both teach and learn in these educative scenarios and that, contrary to common (mis)perceptions that academics live in 'ivory towers', they play a crucial role in contributing to learning that takes place in the so-called 'real world'.

An introduction to the constraints-led approach to learning in outdoor education

Participation in outdoor education is underpinned by a learner's ability to acquire skills in activities such as canoeing, bushwalking and skiing and consequently the outdoor leader is often required to facilitate skill acquisition and motor learning. As such, outdoor leaders might benefit from an appropriate and tested model on how the learner acquires skills in order to design appropriate learning contexts. This paper introduces an approach to skill acquisition based on ecological psychology and dynamical systems theory called the constraints-led approach to skills acquisition. We propose that this student-centred approach is an ideal perspective for the outdoor leader to design effective learning settings. Furthermore, this open style of facilitation is also congruent with learning models that focus on other concepts such as teamwork and leadership.