Twofold Video Hashing with Automatic Synchronization

Video hashing finds a wide array of applications in content authentication, robust retrieval and anti-piracy search. While much of the existing research has focused on extracting robust and secure content descriptors, a significant open challenge still remains: Most existing video hashing methods are fallible to temporal desynchronization. That is, when the query video results by deleting or inserting some frames from the reference video, most existing methods assume the positions of the deleted (or inserted) frames are either perfectly known or reliably estimated. This assumption may be okay under typical transcoding and frame-rate changes but is highly inappropriate in adversarial scenarios such as anti-piracy video search. For example, an illegal uploader will try to bypass the 'piracy check' mechanism of YouTube/Dailymotion etc by performing a cleverly designed non-uniform resampling of the video. We present a new solution based on dynamic time warping (DTW), which can implement automatic synchronization and can be used together with existing video hashing methods. The second contribution of this paper is to propose a new robust feature extraction method called flow hashing (FH), based on frame averaging and optical flow descriptors. Finally, a fusion mechanism called distance boosting is proposed to combine the information extracted by DTW and FH. Experiments on real video collections show that such a hash extraction and comparison enables unprecedented robustness under both spatial and temporal attacks.Comment: submitted to Image Processing (ICIP), 2014 21st IEEE International Conference o

On quasi modules at infinity for vertex algebras

A theory of quasi modules at infinity for (weak) quantum vertex algebras including vertex algebras was previously developed in \cite{li-infinity}. In this current paper, quasi modules at infinity for vertex algebras are revisited. Among the main results, we extend some technical results, to fill in a gap in the proof of a theorem therein, and we obtain a commutator formula for general quasi modules at infinity and establish a version of the converse of the aforementioned theorem.Comment: 18 page

Coupling analysis in educational data

University of Technology Sydney. Faculty of Engineering and Information Technology.Educational data analysis refers to techniques, tools, and research designed to automatically extract meaning from large repositories of data generated by or related to people’s learning activities in educational environments. It is a research field which focus on helping policymakers and administrators understand how analytics and data mining can be applied for the purposes of educational improvement. Unfortunately, most research on educational data only by applying the existing machine learning or data mining algorithms, very few publications have discussed the character of the data itself. Traditional data mining algorithms have disadvantages, in that most of them assume the independent and identically distributed (IID) of data objects, attributes, and values. However, real world data usually contains strong couplings among values, attributes and data objects, and this represents a considerable challenge to existing methods and tools. This thesis focuses on utilizing coupling analysis in educational data analysis tasks. In particular, it focuses on two educational data analysis tasks: student performance prediction, and student social media sentiment analysis. The student performance prediction task is firstly examined. This thesis begins with the most straightforward method which integrates coupling similarities as the distance for a weighted k-nearest centroid classifier. This method considers not only the intra-coupled similarity within an attribute but also the inter-coupled similarity between attributes. Computational cost is high for coupling analysis. Hence, a more efficient method is proposed that selects the centroid objects instead of all objects in the nearest neighbor search process. Furthermore, integrating support vector machines with coupled similarity. The original SVMs is designed for numerical data. This thesis develops a novel pairwise SVMs that use the coupled similarity metric as a kernel between data objects with nominal attributes. The experiment result shows the two proposed methods outperform the traditional SVMs and other popular classification methods on various public data sets, and the student performance prediction task. Secondly, the student social media sentiment analysis is examined. Unlike linguistic methods, this thesis learns how to classify student sentiment by applying data mining on the labeled historical data. Most previous research employs the vector-space model for text representation and analysis, however, the vector-space model does not utilize the information about the term to term relationships. In other words, the traditional text mining techniques assume the relations between term to term are independent and identically distributed (IID). This thesis introduces a novel term representation by involving coupling relations between neighbors. This coupling representation provide much richer information which enables us to create a coupled similarity metric from document to document, and a coupling document similarity based k-nearest centroid classifier applied to the classification task. Experiments verify that the proposed approach outperforms the classic vector-space based classifier and displays distinct advantages and richness in terms of student social media sentiment analysis tasks. Finally, due to the complexity of the proposed algorithm and the enormous amount of the educational related data source, a scalable educational data mining platform is in great demand. Hence, with the help of the Spark cluster, a novel coupling similarity based learning approach has been proposed to cater for the big data learning problem by parallelizing the coupled similarity calculation process. Further, the parallel k-NN for classification and k-Means for the clustering task has been proposed. Compared to the original algorithms, the experimental results show that the proposed methods not only outperforms the clustering and classification performance of the baselines, but also represent a huge improvement on the data scale in terms of the time efficiency. Accordingly, the proposed framework has already been implemented, a scalable educational data analysis platform with coupling analysis will serve to meet a host of future challenges