A Spinning Wheel for YARN: User Interface for a Crowdsourced Thesaurus

YARN (Yet Another RussNet) project started in 2013 aims at creating a large open thesaurus for Russian using crowdsourcing. This paper describes synset assembly interface developed within the project — motivation behind it, design, usage scenarios, implementation details, and first experimental results

Discovering Exclusive Patterns in Frequent Sequences

This paper presents a new concept for pattern discovery in frequent sequences with potentially interesting applications. Based on data mining, the approach aims to discover exclusive sequential patterns (ESP) by checking the relative exclusion of patterns across data sequences. ESP mining pursues the post-processing of sequential patterns and augments existing work on structural relations patterns mining. A three phase ESP mining method is proposed together with component algorithms, where a running worked example explains the process. Experiments are performed on real-world and synthetic datasets which showcase the results of ESP mining and demonstrate its effectiveness, illuminating the theories developed. An outline case study in workflow modelling gives some insight into future applicability

Power Management Techniques for Data Centers: A Survey

With growing use of internet and exponential growth in amount of data to be stored and processed (known as 'big data'), the size of data centers has greatly increased. This, however, has resulted in significant increase in the power consumption of the data centers. For this reason, managing power consumption of data centers has become essential. In this paper, we highlight the need of achieving energy efficiency in data centers and survey several recent architectural techniques designed for power management of data centers. We also present a classification of these techniques based on their characteristics. This paper aims to provide insights into the techniques for improving energy efficiency of data centers and encourage the designers to invent novel solutions for managing the large power dissipation of data centers.Comment: Keywords: Data Centers, Power Management, Low-power Design, Energy Efficiency, Green Computing, DVFS, Server Consolidatio

Public Participation GIS and Neighbourhood Recovery: Using Community Mapping for Economic Development

In 2005, New Orleans, Louisiana experienced an interruption in its neighborhood life cycle due to Hurricane Katrina. While federal, state and local administrative policies have tried to manage the process of recovery, the non-profit sector has been a key to the recovery. This paper will examine the case study of the Beacon of Hope Resource Centre (BOH) whose ability to collect data, expand citizen engagement and influence policy made a positive impact upon economic development through public participation geographic information systems (PPGIS) with the Regional Planning Commission and the Department of Planning and Urban Studies, University of New Orleans. This successful neighbourhood planning model provides an understanding of how PPGIS partnerships can support and encourage community engagement and economic development pre- and post-disaster

Fractional norms and quasinorms do not help to overcome the curse of dimensionality

The curse of dimensionality causes the well-known and widely discussed problems for machine learning methods. There is a hypothesis that using of the Manhattan distance and even fractional quasinorms lp (for p less than 1) can help to overcome the curse of dimensionality in classification problems. In this study, we systematically test this hypothesis. We confirm that fractional quasinorms have a greater relative contrast or coefficient of variation than the Euclidean norm l2, but we also demonstrate that the distance concentration shows qualitatively the same behaviour for all tested norms and quasinorms and the difference between them decays as dimension tends to infinity. Estimation of classification quality for kNN based on different norms and quasinorms shows that a greater relative contrast does not mean better classifier performance and the worst performance for different databases was shown by different norms (quasinorms). A systematic comparison shows that the difference of the performance of kNN based on lp for p=2, 1, and 0.5 is statistically insignificant