Data mining on urban sound sensor networks

ICA 2016, 22nd International Congress on Acoustics, BUENOS AIRES, ARGENTINE, 05-/09/2016 - 09/09/2016Urban sound sensor networks deliver megabytes of data on a daily basis so the question on how to extract useful knowledge from this overwhelming dataset is eminent. This paper presents and compares two extremely different approaches. The first approach uses as much as possible expert knowledge on how people perceive the sonic environment, the second approach simply considers the spectra obtained every time step as meaningless numbers yet tries to structure them in a meaningful way. The approach based on expert knowledge starts by extracting features that a human listener might use to detect salient sounds and to recognize these sounds. These features are then fed to a recurrent neural network that learns in an unsupervised way to structure and group these features based on co-occurrence and typical sequences. The network is constructed to mimic human auditory processing and includes inhibition and adaptation processes. The outcome of this network is the activation of a set of several hundred neurons. The second approach collects a sequence of one minute of sound spectra (1/8 second time step) and summarizes it using Gaussian mixture models in the frequency-amplitude space. Mean and standard deviation of the set of Gaussians are used for further analysis. In both cases, the outcome is clustered to analyze similarities over space and time as well as to detect outliers. Both approaches are applied on a dataset obtained from 25 measurement nodes during approximately one and a half year in Paris, France. Although the approach based on human listening models is expected to be much more precise when it comes to analyzing and clustering soundscapes, it is also much slower than the blind data analysis

Water resources, chapter 2, part B

Various applications and projected applications of active microwave instruments for studying water resources. Most applications involve use of an imaging system operating primarily at wavelengths of less than 30 cm (i.e., K-, X-, and L-bands). Discussion is also included concerning longer wavelength nonimaging systems for use in sounding polar glaciers and icecaps (e.g., Greenland and the Antarctic). The section is divided into six topics: (1) stream runoff, drainage basin analysis, and floods, (2) lake detection and fluctuating levels, (3) coastal processes and wetlands, (4) seasonally and permanently frozen (permafrost) ground, (5) solid water resources (snow, ice, and glaciers), and (6) water pollution

Learning-assisted Theorem Proving with Millions of Lemmas

Large formal mathematical libraries consist of millions of atomic inference steps that give rise to a corresponding number of proved statements (lemmas). Analogously to the informal mathematical practice, only a tiny fraction of such statements is named and re-used in later proofs by formal mathematicians. In this work, we suggest and implement criteria defining the estimated usefulness of the HOL Light lemmas for proving further theorems. We use these criteria to mine the large inference graph of the lemmas in the HOL Light and Flyspeck libraries, adding up to millions of the best lemmas to the pool of statements that can be re-used in later proofs. We show that in combination with learning-based relevance filtering, such methods significantly strengthen automated theorem proving of new conjectures over large formal mathematical libraries such as Flyspeck.Comment: journal version of arXiv:1310.2797 (which was submitted to LPAR conference

The Political Implications of Norway’s Sovereign Wealth Fund investments in Eastern and Central Europe

Although there has been vivid academic debate as to what extent Sovereign Wealth Funds (SWFs) are motivated by political reasons, it is rather clear that countries can use state-owned investment funds as a tool of their foreign policy. Even Barack Obama, during his initial presidential campaign in 2008 commented: “I am obviously concerned if these… sovereign wealth funds are motivated by more than just market consideration and that’s obviously a possibility”. This book looks at SWF activities in Central and Eastern Europe (CEE) to determine the main motives for SWF presence in CEE. Are the potential financial gains the only reason behind their investments? Are SWF activities in the region dangerous for the stability and security of the CEE countries? The book is pioneering analyses of SWFs behaviour in the region, based on empirical data collected from the Sovereign Wealth Fund Institute Transaction Database, arguably the most comprehensive and authoritative resource tracking SWF investment behaviour globally.Rozdział pochodzi z książki: Political Players? Sovereign Wealth Funds’ Investments in Central and Eastern Europe, T. Kamiński (ed.), Wydawnictwo Uniwersytetu Łódzkiego, Łódź 2017.The main goal of this chapter is to analyze investment policy of this state-controlled entity and provide the picture of its portfolio investment in Eastern and Central Europe Countries. For this purpose, the remainder of the chapter is organized in the following manner: in the next part facts and figures about the fund are presented, than the investment policy of the fund is described, after that the holdings of GPFG in Bulgaria, the Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, Slovakia are analyzed, and finally the political implications of these investments are discussed with particular emphasis on the issue of risks. The chapter ends with conclusions.This book was published in frames of project “Political significance of the Sovereign Wealth Funds’ investments in the Central and Eastern Europe”. The project was financed by the Polish National Science Centre (Decision no. DEC-2012/07/B/HS5/03797)

Využití asociačních pravidel při analýze alarmových trendů

This article deals with acquired alarm logs analyses, the alarm logs from control systems, and explains the reasons for its collecting and implication of the analyses. Several existing platform solutions can be used to analyze the alarms from the historical trends, and solutions, which offer the filtering of events based on time units to obtain the data about the maximum and average number of logs. This paper contributes to this area by developing a graphical interface for a system that makes it possible to use the analysis by association rules and brings the possibility of detecting frequent and repeating patterns in acquired alarm data files.Tento článek se zabývá sběrem a analýzami alarmových logů, alarmových hlášení z řídicích systémů, a vysvětluje důvody pro jejich sběr a důsledky analýz. Několik stávajících řešení na softwarových platformách může být k analýze alarmů použito z ukládaných historických trendů a řešení, která nabízejí filtrování událostí na základě časových období a údajů o maximálním a průměrném počtu logů. Tento příspěvek přispívá k této oblasti vytvořením grafického rozhraní pro systém, který umožňuje použití analýzy pomocí asociačních pravidel a přináší možnost detekce častých a opakujících se vzorů v získaných datových souborech s alarmy

Assessing the impact of anthropogenic activities on groundwater quality in Maiduguri, Nigeria

This study investigates the impact of anthropogenic activities on groundwater quality; this was achieved by determining the concentration of potential anthropogenic contaminant indicator parameters such as nitrate, chloride, phosphate, and sulphate in the groundwater samples of the study area. A total of 30groundwater samples,15 each from the northern and southern partsof Maiduguri where obtained across a period of 2 months. Results ofthe groundwater analysesshowed that nitrate (NO3-) has mean concentration of 13.7mg/l in the northern part (site A), and 15.53 mg/l in the southern part (site B).Chloride (Cl-)has a mean concentration of 10.62 and 13.33 mg/l respectively in sites A and B. Sulphate (SO4-) has mean concentration of 3.52 mg/l in site A and 1.46 mg/l in site B. Lastly,phosphate (PO4-) has mean concentration of 1.39 and 1.52 mg/l in sites A and B respectively. The Mean concentrations were tested for their significant difference (p <0.05) across the boreholes of the two sites.Water quality results indicate that the impact of anthropogenic activities in the study area is low to moderate currently. The outcome of this paper will be useful in planning for sustainable groundwater management strategy

Measuring the Circle: Emerging Trends in Philanthropy for First Nations

The Circle had the opportunity to undertake a multi-part research project to gain a more robust understanding of non-governmental funding to Aboriginal beneficiaries and causes in Canada over the past few years. The year-long knowledge gathering process included three inter-related activities: (a) mining Canada Revenue Agency data to map the Aboriginal funding economy in Canada from 2005 to 2011; (b) a set of Key Informant interviews with representatives from a sample of grantmakers surfaced through the mapping activity; and (c) a series of case studies to showcase some leading funders in the Aboriginal funding sphere or initiatives dedicated to building community capacity as well as supporting Aboriginal beneficiaries and causes. This report contains the key findings from the three-part research initiative

Mining Social Media and Structured Data in Urban Environmental Management to Develop Smart Cities

This research presented the deployment of data mining on social media and structured data in urban studies. We analyzed urban relocation, air quality and traffic parameters on multicity data as early work. We applied the data mining techniques of association rules, clustering and classification on urban legislative history. Results showed that data mining could produce meaningful knowledge to support urban management. We treated ordinances (local laws) and the tweets about them as indicators to assess urban policy and public opinion. Hence, we conducted ordinance and tweet mining including sentiment analysis of tweets. This part of the study focused on NYC with a goal of assessing how well it heads towards a smart city. We built domain-specific knowledge bases according to widely accepted smart city characteristics, incorporating commonsense knowledge sources for ordinance-tweet mapping. We developed decision support tools on multiple platforms using the knowledge discovered to guide urban management. Our research is a concrete step in harnessing the power of data mining in urban studies to enhance smart city development