AXES at TRECVID 2012: KIS, INS, and MED

The AXES project participated in the interactive instance search task (INS), the known-item search task (KIS), and the multimedia event detection task (MED) for TRECVid 2012. As in our TRECVid 2011 system, we used nearly identical search systems and user interfaces for both INS and KIS. Our interactive INS and KIS systems focused this year on using classifiers trained at query time with positive examples collected from external search engines. Participants in our KIS experiments were media professionals from the BBC; our INS experiments were carried out by students and researchers at Dublin City University. We performed comparatively well in both experiments. Our best KIS run found 13 of the 25 topics, and our best INS runs outperformed all other submitted runs in terms of P@100. For MED, the system presented was based on a minimal number of low-level descriptors, which we chose to be as large as computationally feasible. These descriptors are aggregated to produce high-dimensional video-level signatures, which are used to train a set of linear classifiers. Our MED system achieved the second-best score of all submitted runs in the main track, and best score in the ad-hoc track, suggesting that a simple system based on state-of-the-art low-level descriptors can give relatively high performance. This paper describes in detail our KIS, INS, and MED systems and the results and findings of our experiments

An investigation into machine learning approaches for forecasting spatio-temporal demand in ride-hailing service

In this paper, we present machine learning approaches for characterizing and forecasting the short-term demand for on-demand ride-hailing services. We propose the spatio-temporal estimation of the demand that is a function of variable effects related to traffic, pricing and weather conditions. With respect to the methodology, a single decision tree, bootstrap-aggregated (bagged) decision trees, random forest, boosted decision trees, and artificial neural network for regression have been adapted and systematically compared using various statistics, e.g. R-square, Root Mean Square Error (RMSE), and slope. To better assess the quality of the models, they have been tested on a real case study using the data of DiDi Chuxing, the main on-demand ride hailing service provider in China. In the current study, 199,584 time-slots describing the spatio-temporal ride-hailing demand has been extracted with an aggregated-time interval of 10 mins. All the methods are trained and validated on the basis of two independent samples from this dataset. The results revealed that boosted decision trees provide the best prediction accuracy (RMSE=16.41), while avoiding the risk of over-fitting, followed by artificial neural network (20.09), random forest (23.50), bagged decision trees (24.29) and single decision tree (33.55).Comment: Currently under review for journal publicatio

Commonwealth place-based service delivery initiatives: key learnings project

Overview: Place-based initiatives are programs designed and delivered with the intention of targeting a specific geographical location and particular population group in order to respond to complex social problems. Typically, they focus on areas and communities with entrenched disadvantage or deprivation. This report investigates the key factors and characteristics associated with successful outcomes with such programs, drawing on the international literature and evaluation studies of Australian government and overseas programs. Key messages: Many Commonwealth place-based initiatives reviewed had features that accord with those of international place-based initiatives, including common program elements such as design, delivery, implementation and evaluation. Evaluation of all these common elements is rarely done by either international or Commonwealth place-based initiatives. In particular, evaluations of Commonwealth place-based initiatives lacked sufficient evidence to establish the causal effects of initiatives, their cost-effectiveness and an understanding of how these initiatives work to achieve their goals. This was more pronounced among the evaluation of Commonwealth initiatives than in international evaluations

Delivering on Open Government: The Obama Administration's Unfinished Legacy

This report examines progress made during President Obama's first term toward open government goals outlined in a comprehensive set of recommendations that the open government community issued in November 2008, titled Moving Toward a 21st Century Right-to-Know Agenda. We examine activity in the three main areas of the 2008 report: creating an environment within government that is supportive of transparency, improving public use of government information, and reducing the secrecy related to national security issues

Pension Reform in Germany:

The paper examines the long-term implications of various reform options on retirement entry decisions and the actual retirement age of older workers. It focuses on the changes in pension legislation since 1992 and the reform options discussed by the German Social Security Reform Commission installed in 2002 (�Rürup Commission�). Our simulations show that the early-retirement adjustment factors introduced by the 1992 pension reform will raise the average effective retirement age for men by almost two years. The two-year increase in all relevant age limits proposed by the �Rürup Commission� would raise retirement age of men by another eight months.

Pension Reform in Germany: The Impact on Retirement Decisions

The paper examines the long-term implications of various reform options on retirement entry decisions and the actual retirement age of older workers. It focuses on the changes in pension legislation since 1992 and the reform options discussed by the German Social Security Reform Commission installed in 2002 (“Rürup Commission”). Our simulations show that the early-retirement adjustment factors introduced by the 1992 pension reform will raise the average effective retirement age for men by almost two years. The two-year increase in all relevant age limits proposed by the “Rürup Commission” would raise retirement age of men by another eight months.

Toward a Model of Intercultural Warrant: A Case of the Korean Decimal Classification\u27s Cross-cultural Adaptation of the Dewey Decimal Classification

I examined the Korean Decimal Classification (KDC)\u27s adaptation of the Dewey Decimal Classification (DDC) by comparing the two systems. This case manifests the sociocultural influences on KOSs in a cross-cultural context. I focused my analysis on the changes resulting from the meeting of the two cultures, answering the main research question: “How does KDC adapt DDC in terms of underlying sociocultural perspectives in a classificatory form?” I took a comparative approach and address the main research question in two phases. In Phase 1, quantities of class numbers were analyzed by edition and discipline. The main class with the most consistently high number of class numbers in DDC was the social sciences, while the main class with the most consistently high number of class numbers in KDC was technology. The two main classes are expected to differ in semantic contents or specificities. In Phase 2, patterns of adaptations were analyzed by examining the class numbers, captions, and hierarchical relations within the developed adaptation taxonomy. Implementing the taxonomy as a coding scheme brings two comparative features of classifications: 1) semantic contents determined by captions and quantity of subordinate numbers; and 2) structural arrangement determined by ranks, the broader category, presence and the order of subordinate numbers. Surveying proper forms of adaptation resulted in the development of an adaptation taxonomy that will serve as a framework to account for the conflicts between and harmonization of multiple social and cultural influences in knowledge structures. This study has ramifications in theoretical and empirical foundations for the development of “intercultural warrant” in KOSs

Investigating data mining techniques for extracting information from Alzheimer\u27s disease data

Data mining techniques have been used widely in many areas such as business, science, engineering and more recently in clinical medicine. These techniques allow an enormous amount of high dimensional data to be analysed for extraction of interesting information as well as the construction of models for prediction. One of the foci in health related research is Alzheimer\u27s disease which is currently a non-curable disease where diagnosis can only be confirmed after death via an autopsy. Using multi-dimensional data and the applications of data mining techniques, researchers hope to find biomarkers that will diagnose Alzheimer\u27s disease as early as possible. The primary purpose of this research project is to investigate the application of data mining techniques for finding interesting biomarkers from a set of Alzheimer\u27s disease related data. The findings from this project will help to analyse the data more effectively and contribute to methods of providing earlier diagnosis of the disease