Learning Temporal Alignment Uncertainty for Efficient Event Detection

In this paper we tackle the problem of efficient video event detection. We argue that linear detection functions should be preferred in this regard due to their scalability and efficiency during estimation and evaluation. A popular approach in this regard is to represent a sequence using a bag of words (BOW) representation due to its: (i) fixed dimensionality irrespective of the sequence length, and (ii) its ability to compactly model the statistics in the sequence. A drawback to the BOW representation, however, is the intrinsic destruction of the temporal ordering information. In this paper we propose a new representation that leverages the uncertainty in relative temporal alignments between pairs of sequences while not destroying temporal ordering. Our representation, like BOW, is of a fixed dimensionality making it easily integrated with a linear detection function. Extensive experiments on CK+, 6DMG, and UvA-NEMO databases show significant performance improvements across both isolated and continuous event detection tasks.Comment: Appeared in DICTA 2015, 8 page

An Automata Based Text Analysis System

This report describes and implements an automata based text analysis system. We have collected some of the writing samples. Each sample establishes a tree, and uses the ALERGIA algorithm to merge all compatible nodes in order to get a merged stochastic finite automaton. We store these automatons which demonstrate writing style of the sample texts in the hard drive. For a new testing piece, we can test if it has similar writing style compared to those sample texts

Technical Debt Prioritization: State of the Art. A Systematic Literature Review

Background. Software companies need to manage and refactor Technical Debt issues. Therefore, it is necessary to understand if and when refactoring Technical Debt should be prioritized with respect to developing features or fixing bugs. Objective. The goal of this study is to investigate the existing body of knowledge in software engineering to understand what Technical Debt prioritization approaches have been proposed in research and industry. Method. We conducted a Systematic Literature Review among 384 unique papers published until 2018, following a consolidated methodology applied in Software Engineering. We included 38 primary studies. Results. Different approaches have been proposed for Technical Debt prioritization, all having different goals and optimizing on different criteria. The proposed measures capture only a small part of the plethora of factors used to prioritize Technical Debt qualitatively in practice. We report an impact map of such factors. However, there is a lack of empirical and validated set of tools. Conclusion. We observed that technical Debt prioritization research is preliminary and there is no consensus on what are the important factors and how to measure them. Consequently, we cannot consider current research conclusive and in this paper, we outline different directions for necessary future investigations

ALIA LIS research environmental scan report

Executive summary: An environmental scan of Australian Library and Information Studies (LIS) research was undertaken focusing on the period 2005–2013. This was in response to a brief from ALIA that sought such an analysis to inform its decisions in relation to content of a future research agenda, support, advocacy, and future funding. The investigation was expected to include research priorities of other library and information organisations, topics of research undertaken in Australia, types of research, persons/organisations undertaking research, and how research activities are funded, communicated and applied. The report took into account: research priorities of LIS professional associations both within and outside Australia production of higher degree theses over the period publication by practitioners and academics in both Australian and international publications and grant or other support for research or investigatory projects. METHODOLOGY AND LIMITATIONS: Methodologies employed included: Website analysis for research priorities of LIS organisations Database searching using Trove for higher degree theses Database searching using multiple databases for publications In the case of research in progress and resourcing via grants, methods employed were database searching, consultation and by survey methods The limitations in these approaches are explained in each related Section or Appendix. However, the major limitations were: Poor response to the online survey despite its wide dissemination through ALIA and other associations Inconsistent responses to individual surveys directed specifically at academic departments Coverage of publications by databases, particularly of material outside periodicals Difficulties in categorising document