Partially Supervised Neighbor Embedding for Example-Based Image Super-Resolution

Neighbor embedding algorithm has been widely used in example-based super-resolution reconstruction from a single frame, which makes the assumption that neighbor patches embedded are contained in a single manifold. However, it is not always true for complicated texture structure. In this paper, we believe that textures may be contained in multiple manifolds, corresponding to classes. Under this assumption, we present a novel example-based image super-resolution reconstruction algorithm with clustering and supervised neighbor embedding (CSNE). First, a class predictor for low-resolution (LR) patches is learnt by an unsupervised Gaussian mixture model. Then by utilizing class label information of each patch, a supervised neighbor embedding is used to estimate high-resolution (HR) patches corresponding to LR patches. The experimental results show that the proposed method can achieve a better recovery of LR comparing with other simple schemes using neighbor embedding.

Predictive gene lists for breast cancer prognosis: a topographic visualisation study

Background: The controversy surrounding the non-uniqueness of predictive gene lists (PGL) of small selected subsets of genes from very large potential candidates as available in DNA microarray experiments is now widely acknowledged 1. Many of these studies have focused on constructing discriminative semi-parametric models and as such are also subject to the issue of random correlations of sparse model selection in high dimensional spaces. In this work we outline a different approach based around an unsupervised patient-specific nonlinear topographic projection in predictive gene lists. Methods: We construct nonlinear topographic projection maps based on inter-patient gene-list relative dissimilarities. The Neuroscale, the Stochastic Neighbor Embedding(SNE) and the Locally Linear Embedding(LLE) techniques have been used to construct two-dimensional projective visualisation plots of 70 dimensional PGLs per patient, classifiers are also constructed to identify the prognosis indicator of each patient using the resulting projections from those visualisation techniques and investigate whether a-posteriori two prognosis groups are separable on the evidence of the gene lists. A literature-proposed predictive gene list for breast cancer is benchmarked against a separate gene list using the above methods. Generalisation ability is investigated by using the mapping capability of Neuroscale to visualise the follow-up study, but based on the projections derived from the original dataset. Results: The results indicate that small subsets of patient-specific PGLs have insufficient prognostic dissimilarity to permit a distinction between two prognosis patients. Uncertainty and diversity across multiple gene expressions prevents unambiguous or even confident patient grouping. Comparative projections across different PGLs provide similar results. Conclusion: The random correlation effect to an arbitrary outcome induced by small subset selection from very high dimensional interrelated gene expression profiles leads to an outcome with associated uncertainty. This continuum and uncertainty precludes any attempts at constructing discriminative classifiers. However a patient's gene expression profile could possibly be used in treatment planning, based on knowledge of other patients' responses. We conclude that many of the patients involved in such medical studies are intrinsically unclassifiable on the basis of provided PGL evidence. This additional category of 'unclassifiable' should be accommodated within medical decision support systems if serious errors and unnecessary adjuvant therapy are to be avoided.

Semi-supervised learning of WLAN radio maps

In this thesis a manifold learning method is applied to the problem of WLAN positioning and automatic radio map creation. Due to the nature of WLAN signal strength measurements, a signal map created from raw measurements results in non-linear distance relations between measurement points. These signal strength vectors reside in a high-dimensioned coordinate system. With the help of the so called Isomap-algorithm the dimensionality of this map can be reduced, and thus more easily processed. By embedding position-labeled strategic key points, we can automatically adjust the mapping to match the surveyed environment. The environment is thus learned in a semi-supervised way; gathering training points and embedding them in a two-dimensional manifold gives us a rough mapping of the measured environment. After a calibration phase, where the labeled key points in the training data are used to associate coordinates in the manifold representation with geographical locations, we can perform positioning using the adjusted map. This can be achieved through a traditional supervised learning process, which in our case is a simple nearest neighbors matching of a sampled signal strength vector. We deployed this system in two locations in the Kumpula campus in Helsinki, Finland. Results indicate that positioning based on the learned radio map can achieve good accuracy, especially in hallways or other areas in the environment where the WLAN signal is constrained by obstacles such as walls.

Efficient Nearest Neighbor Classification Using a Cascade of Approximate Similarity Measures

Nearest neighbor classification using shape context can yield highly accurate results in a number of recognition problems. Unfortunately, the approach can be too slow for practical applications, and thus approximation strategies are needed to make shape context practical. This paper proposes a method for efficient and accurate nearest neighbor classification in non-Euclidean spaces, such as the space induced by the shape context measure. First, a method is introduced for constructing a Euclidean embedding that is optimized for nearest neighbor classification accuracy. Using that embedding, multiple approximations of the underlying non-Euclidean similarity measure are obtained, at different levels of accuracy and efficiency. The approximations are automatically combined to form a cascade classifier, which applies the slower approximations only to the hardest cases. Unlike typical cascade-of-classifiers approaches, that are applied to binary classification problems, our method constructs a cascade for a multiclass problem. Experiments with a standard shape data set indicate that a two-to-three order of magnitude speed up is gained over the standard shape context classifier, with minimal losses in classification accuracy.

Supervised Aggregative Feature Extraction for Big Data Time Series Regression

In many applications, and especially those where batch processes are involved, a target scalar output of interest is often dependent on one or more time series of data. With the exponential growth in data logging in modern industries such time series are increasingly available for statistical modeling in soft sensing applications. In order to exploit time series data for predictive modelling, it is necessary to summarise the information they contain as a set of features to use as model regressors. Typically this is done in an unsupervised fashion using simple techniques such as computing statistical moments, principal components or wavelet decompositions, often leading to significant information loss and hence suboptimal predictive models. In this paper, a functional learning paradigm is exploited in a supervised fashion to derive continuous, smooth estimates of time series data (yielding aggregated local information), while simultaneously estimating a continuous shape function yielding optimal predictions. The proposed Supervised Aggregative Feature Extraction (SAFE) methodology can be extended to support nonlinear predictive models by embedding the functional learning framework in a Reproducing Kernel Hilbert Spaces setting. SAFE has a number of attractive features including closed form solution and the ability to explicitly incorporate first and second order derivative information. Using simulation studies and a practical semiconductor manufacturing case study we highlight the strengths of the new methodology with respect to standard unsupervised feature extraction approaches.

Zero-day malware detection based on supervised learning algorithms of API call signatures

Zero-day or unknown malware are created using code obfuscation techniques that can modify the parent code to produce offspring copies which have the same functionality but with different signatures. Current techniques reported in literature lack the capability of detecting zero-day malware with the required accuracy and efficiency. In this paper, we have proposed and evaluated a novel method of employing several data mining techniques to detect and classify zero-day malware with high levels of accuracy and efficiency based on the frequency of Windows API calls. This paper describes the methodology employed for the collection of large data sets to train the classifiers, and analyses the performance results of the various data mining algorithms adopted for the study using a fully automated tool developed in this research to conduct the various experimental investigations and evaluation. Through the performance results of these algorithms from our experimental analysis, we are able to evaluate and discuss the advantages of one data mining algorithm over the other for accurately detecting zero-day malware successfully. The data mining framework employed in this research learns through analysing the behavior of existing malicious and benign codes in large datasets. We have employed robust classifiers, namely Naïve Bayes (NB) Algorithm, k−Nearest Neighbor (kNN) Algorithm, Sequential Minimal Optimization (SMO) Algorithm with 4 differents kernels (SMO - Normalized PolyKernel, SMO – PolyKernel, SMO – Puk, and SMO- Radial Basis Function (RBF)), Backpropagation Neural Networks Algorithm, and J48 decision tree and have evaluated their performance. Overall, the automated data mining system implemented for this study has achieved high true positive (TP) rate of more than 98.5%, and low false positive (FP) rate of less than 0.025, which has not been achieved in literature so far. This is much higher than the required commercial acceptance level indicating that our novel technique is a major leap forward in detecting zero-day malware. This paper also offers future directions for researchers in exploring different aspects of obfuscations that are affecting the IT world today.

Graph-based Semi-Supervised Learning in Acoustic Modeling for Automatic Speech Recognition

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

Embedding university perspectives in university teaching and learning : lessons learnt and possibilities for reforming/decolonising curriculum

Embedding Indigenous knowledge in the curriculum continues to challenge traditional western perspectives on Indigenous epistemologies and cultures. This paper will initially discuss experiences of embedding Indigenous perspectives in the curriculum at an Australian university. The project was inspired by the Reconciliation Statement which ensured funding through Teaching and Learning Large Grants. Its successful outcomes included the creation of identified positions for Indigenous academics within faculties, creation of a resource hub of relevant teaching materials and consistent documentation and awareness of Indigenous perspectives through interviews and workshops. The paper concludes by critically interrogating the methodology used to conceptualise Indigenous knowledge in embedding Indigenous perspectives in a university curriculum. This paper argues for a thorough curriculum reform if a degree of decolonisation of the western constructed Indigenous knowledge and its living systems are desired.

Empowerment for lifelong learning : embedding information literacy into the business curriculum

Success in modern business demands effective information literacy to address the ever-changing business context. This context includes changes in Government policy reflected through legislation and regulations, developments in case law and expectations of professional associations and the public. Students require the skills to continue their own learning beyond the completion of their degree, since learning the subject content of a course alone sufficient. This paper considers the methods utilised to embed information literacy, in the context of generic skills and graduate attributes, into a Business degree’s curriculum. The paper describes how information literacy has been embedded in two sequential third-year Taxation Law courses, allowing for the explicit development of information literacy. Through the development of legal reasoning and research skills, students are empowered to continue their lifelong learning, which successful professional practice demands. The study will draw upon the experience of the course convener in designing, teaching and evaluating the courses, and on students’ experiences as illustrated through evaluation questionnaire responses and interviews. The findings of this study could be relevant to other business courses, especially company law and auditing.

Low fat loss response after medium-term supervised exercise in obese is associated with exercise-induced increase in food reward

Objective: To examine exercise-induced changes in the reward value of food during medium-term supervised exercise in obese individuals. ---------- Subjects/Methods: The study was a 12-week supervised exercise intervention prescribed to expend 500 kcal/day, 5 d/week. 34 sedentary obese males and females were identified as responders (R) or non-responders (NR) to the intervention according to changes in body composition relative to measured energy expended during exercise. Food reward (ratings of liking and wanting, and relative preference by forced choice pairs) for an array of food images was assessed before and after an acute exercise bout. ---------- Results. 20 responders and 14 non-responders were identified. R lost 5.2 kg±2.4 of total fat mass and NR lost 1.7 kg±1.4. After acute exercise, liking for all foods increased in NR compared to no change in R. Furthermore, NR showed an increase in wanting and relative preference for high-fat sweet foods. These differences were independent of 12-weeks regular exercise and weight loss. ---------- Conclusion. Individuals who showed an immediate post-exercise increase in liking and increased wanting and preference for high-fat sweet foods displayed a smaller reduction in fat mass with exercise. For some individuals, exercise increases the reward value of food and diminishes the impact of exercise on fat loss.

Embedding 'philosophy in the classroom' in pre-service teacher education

The holistic conception of the troika, as described in the first chapter, centres on the relationship between the implicit and explicit teaching of values the nurturing of the specific dimensions of quality teaching and the opportunity to ‘walk the talk’ of the values education program through aspects such as practical citizenship (Lovat, Toomey, Clement, Crotty & Nielsen, 2009). It is proposed in this chapter that the conception can be realized through the embedding of Philosophy in the Classroom within pre-service teaching programs. The troika, a Russian sleigh with three horses, only function well when there is complete synergy and balance between all Classroom is a scaffold for ensuring that all three elements of the troika, namely, quality teaching, values education and service learning in the form of education for citizenship, exist within the classroom to achieve an optimal learning, growth and wellbeing for all students. For this to be more widely accomplished Philosophy in the Classroom and discusses how it constitutes a successful synergy and balance of the troika for effective teaching. It then proposes how it might be embedded into pre-service teacher education.

Critical thinking and organisation theory : embedding a process to encourage graduate capabilities

The need for the development of effective business curricula that meets the needs of the marketplace has created an increase in the adoption of core competencies lists identifying appropriate graduate skills. Many organisations and tertiary institutions have individual graduate capabilities lists including skills deemed essential for success. Skills recognised as ‘critical thinking’ are popular inclusions on core competencies and graduate capability lists. While there is literature outlining ‘critical thinking’ frameworks, methods of teaching it and calls for its integration into business curricula, few studies actually identify quantifiable improvements achieved in this area. This project sought to address the development of ‘critical thinking’ skills in a management degree program by embedding a process for critical thinking within a theory unit undertaken by students early in the program. Focus groups and a student survey were used to identify issues of both content and implementation and to develop a student perspective on their needs in thinking critically. A process utilising a framework of critical thinking was integrated through a workbook of weekly case studies for group analysis, discussions and experiential exercises. The experience included formative and summative assessment. Initial results indicate a greater valuation by students of their experience in the organisation theory unit; better marks for mid semester essay assignments and higher evaluations on the university administered survey of students’ satisfaction.