Exposure to foreign media and changes in cultural traits: Evidence from naming patterns in France

Free trade in audio-visual services has faced opposition on the grounds that foreign media undermine domestic culture, and ultimately, global diversity. We assess the media-culture link using name frequencies as a measure of tastes. Using a 47-year panel of French birth registries, we first show that names appearing on television shows, movies, or in songs are about five times more popular than other names. Most, but not all, of this relationship arises from endogeneity: song and script writers, as well as performers and their parents, select names that would be popular anyway. Using name attributes, fixed effects, and lagged popularity as controls, our regression results suggest that media affect choices by informing parents of unfamiliar names.Endogenous Tastes, Cultural transmission, Television, Cinema, Popular Music

Dissemination of Health Information within Social Networks

In this paper, we investigate, how information about a common food born health hazard, known as Campylobacter, spreads once it was delivered to a random sample of individuals in France. The central question addressed here is how individual characteristics and the various aspects of social network influence the spread of information. A key claim of our paper is that information diffusion processes occur in a patterned network of social ties of heterogeneous actors. Our percolation models show that the characteristics of the recipients of the information matter as much if not more than the characteristics of the sender of the information in deciding whether the information will be transmitted through a particular tie. We also found that at least for this particular advisory, it is not the perceived need of the recipients for the information that matters but their general interest in the topic

Video Categorization Using Data Mining

Video categorization using data mining is the area of the research that aims to propose adeveloped method based on Artificial Neural Network (ANN), which could be used to classify video files into different categories according to the content. In order to test this method, the classifications of video files are discussed. The applied system proposes that the video could be categorized in two classes. The first one is educational while is noneducational. The classification is conducted based on the motion using optical flow. Several experiments were conducted using Artificial Neural Network (ANN) model. The research facilitate access to the required educational video to the learners students, especially novice students. This research objective is to investigate how the effect of motion feature can be useful in such lassification. We believe that other effects such audio features, text features, and other factors can enhance accuracy, but this requires wider studies and need more time. The accuracy of results in video classification to educational and non-educational through technique 3 fold cross validation and using (ANN) model is 54%. This result may can be improved by introducing other factors mentioned above

Applications of a High-Altitude Powered Platform (HAPP)

A list of potential uses for the (HAPP) and conceptual system designs for a small subset of the most promising applications were investigated. The method was to postulate a scenario for each application specifying a user, a set of system requirements and the most likely competitor among conventional aircraft and satellite systems. As part of the study of remote sensing applications, a parametric cost comparison was done between aircraft and HAPPS. For most remote sensing applications, aircraft can supply the same data as HAPPs at substantially lower cost. The critical parameters in determining the relative costs of the two systems are the sensor field of view and the required frequency of the observations being made. The HAPP is only competitive with an airplane when sensors having a very wide field of view are appropriate and when the phenomenon being observed must be viewed at least once per day. This eliminates the majority of remote sensing applications from any further consideration

Validating Network Value of Influencers by means of Explanations

Recently, there has been significant interest in social influence analysis. One of the central problems in this area is the problem of identifying influencers, such that by convincing these users to perform a certain action (like buying a new product), a large number of other users get influenced to follow the action. The client of such an application is a marketer who would target these influencers for marketing a given new product, say by providing free samples or discounts. It is natural that before committing resources for targeting an influencer the marketer would be interested in validating the influence (or network value) of influencers returned. This requires digging deeper into such analytical questions as: who are their followers, on what actions (or products) they are influential, etc. However, the current approaches to identifying influencers largely work as a black box in this respect. The goal of this paper is to open up the black box, address these questions and provide informative and crisp explanations for validating the network value of influencers. We formulate the problem of providing explanations (called PROXI) as a discrete optimization problem of feature selection. We show that PROXI is not only NP-hard to solve exactly, it is NP-hard to approximate within any reasonable factor. Nevertheless, we show interesting properties of the objective function and develop an intuitive greedy heuristic. We perform detailed experimental analysis on two real world datasets - Twitter and Flixster, and show that our approach is useful in generating concise and insightful explanations of the influence distribution of users and that our greedy algorithm is effective and efficient with respect to several baselines

WP 93 - An overview of women's work and employment in Kazakhstan

*Management summary* This report provides information on Kazakhstan on behalf of the implementation of the DECISIONS FOR LIFE project in that country. The DECISIONS FOR LIFE project aims to raise awareness amongst young female workers about their employment opportunities and career possibilities, family building and the work-family balance. This report is part of the Inventories, to be made by the University of Amsterdam, for all 14 countries involved. It focuses on a gender analysis of work and employment. _History (2.1.1)._ Under the Soviet regime, the Kazahs had a hard time, initially not improving with the collapse of the Soviet Union. In the 2000s, based on its mineral wealth and high oil prices, the economy boomed, followed by a nosedive in 2008. Governance (2.1.2). Kazakhstan is a republic with a parliamentary system dominated by president Nazarbayev and his party. Recently the government’s human rights record remained poor. Though constitution and law provide for equal rights and freedoms for men and women, enforcement of human and women’s rights is weak. Women’s participation in politics and governance structures is low. _Prospects (2.1.3)._ The global economic crisis has a considerable impact on Kazakhstan’s economic and maybe social prospects. The government had to massively support the banking system. Though official (un)employment and wage fi gures for 2009 do not yet point at serious consequences for the population, projections until 2015 stick to low growth rates, which among other things may endanger the government’s ambituous diversifi cation program. _Communication (2.2)._ Though the coverage of fi xed telephone connections has recently increased, this is dwarfed by the expansion of the incidence of cell phones, to about one per inhabitant in 2008. By that year, 146 per 1,000 were Internet users. Nearly all households have a TV set. The government uses a variety of means to control the media and limit freedom of expression. _The sectoral labour market structure – Population and employment (2.3.1)._ Between 2001 and 2008 a growing ”formalisation” of the of the labour market took place, lifting the share of employees to about two-third. In particular women’s employment witnessed strong growth. Reaching 75% in 2008, women’s Labour Participation Rate (LPR) was rather high and 92% of men’s. _The sectoral labour market structure – Unemployment (2.3.2)_ In the 2000s unemployment fell from over 10% to below 7%, with female unemployment rates remaining one third above male. Youth employment is rather low, the highest unemoplyment rates are among the female 25-29 aged. _Legislation (2.4.1)._ Kazakhstan has ratifi ed the eight core ILO Labour Conventions. The Constitution provides for the freedom of association and the right to strike, though notably the latter right is subject to numerous legal limitations. In the informal economy the government did not enforce contracts or labour legislation. _Labour relations and wage-setting (2.4.2)._ The union movement of Kazakhstan consists of both “traditional” and, after independence newly created, “independent” trade unions. In the 1990s membership of in particular the traditional confederation fell heavily. In 2008, union density may have been about 50% (paid employees). Based on formally tripartite structures, the yearly General Agreement is the basis for national, regional and sectoral collective agreements. The statutory minimum wage (2.5.1). In 2009 the national monthly minimum wage, set by law, was 13,717 Tenge, or 23% of the country’s average monthly wage. Since 2004, the gap between the minimum and average wage has slightly decreased. Poverty (2.5.2). The country’s growth pattern has been pro-poor, with in the (early) 2000s poverty falling according to all yardsticks. For 2004, it was estimated that 16% of the population lived below the national poverty line. Income inequality is relatively limited. Nevertheless, an in-depth study revealed considerable housing poverty and poor quality of basic infrastructure services. _Population and fertility (2.6.1)._ Kazakstan’s population showed a sharp downward trend from 1989 to 2002, followed by a modest growth of on average 0.9% yearly. The total fertility rate, about 1.9 children per woman, and the adolescent fertility rate (29 per 1,000) are both rather low and stable. Early marriage and early pregnancy do occur, but seem to remain rather limited. _Health (2.6.2)._ In 2007, the number of people in Kazakhstan living with HIV was estimated at 12,000, or 0.7 per 1,000, low in comparison with the rest of the region. The levels of public awareness of HIV/AIDS are low, as is the case for knowledge on contraceptive prevalence among women. General health indicators are still low by international standards. In particular in urban areas, access to essential infrastructure services is limited. _Women’s labour market share (2.6.3)._ Women make up nearly half of the country’s labour force. In 2008 seven of 15 industries showed a female share above this average as well as a female majority. Women are clearly over-represented in four occupational groups at the higher and middle levels, each time with more than a two to one parity; even at the level of legislators, senior officials and managers, the female share of 38% is in international perspective rather high. _Literacy (2.7.1)._ The adult literacy rate –those age 15 and over that can read and write—in 1999-2006 was 97.9%, with a small gender gap: 99.0% for men and 96.7% for women. In 2007 the literacy rate for 15-24-year-olds stood at 99.8%; the young females scored 99.9%. _Education of girls (2.7.2)._ In 2006, the combined gross enrollment rate in education was 91.8%, divided in 88.5% for females and 95.1% for males. Net enrollment in primary education was for 2007 set at 99.4% for girls and 98.6% for boys. Women to men parity in secondary education increased to 97% in 2007. Income differences play a major role in further education after secondary school, though much more young women than young men enroll in universities and colleges. _Female skill levels (2.7.3)._ Women in the employed population have on average a higher educational level than their male colleagues. In contrast, women’s opportunities in work and employment are severely limited by the segmentation of the country’s labour market along regional and gender dimensions. We estimate the current size of the target group of DECISIONS FOR LIFE for Kazakhstan at about 230,000 girls and young women 15-29 of age working in urban areas in commercial services. _Wages (2.8.1)._ We found for 2008 a large gender pay gap, totaling 36%. Further, fi tting in the picture of a highly segmented labour market, wages in Kazakhstan vary largely across sectors, occupational categories, the urban – rural divide, and across regions. _Working conditions (2.8.2)._ Official statistical information concerning working conditions is quite limited. As far as can be traced, gender differences in hours worked are small.

Characterization of Common Videos with Signatures Extracted from Frame Transition Profiles

People have access to a tremendous amount of video nowadays, both on television and Internet. The amount of video that a viewer has to choose from is so large that it is infeasible for a human to go through it all to find a video of interest. Organizing video into categories will make the process of large number of videos much faster and improves the ease of access. A profile created by observing the rate at which the contents of video frame changes helps in categorization of videos in different types. The experiments we conducted on three types of videos (News, Sports, and Music) show that a profile built on a set of frame transition parameter measurements could be applied to automatically distinguish the types of these videos. We have researched a way to automatically characterize videos into their respected video type, such as a news, music, or sports video clips, by comparing the content value transitions among the video frames. The objective of this research is to see if some measurements extracted from frame transitions are used to show the differences between different categories of videos. In other words, we want to see if such kind of values and measurements can be used to tell different kind of videos or the genre of videos, e.g., with respect to the authors. Our program extracts the statistical data from the video frames based on the histograms of the grayscale pixel intensity changes in the frame transitions. A variety of videos were tested to categorize them using the extracted signatures from these frame transition profiles. The signatures extracted presents a problem of classification that can be addressed using the machine learning algorithms. Time complexity of the evaluation is decreased when compared to other methods in video classification as the video is processed in a single step where all the features are extracted and analysis is performed on the obtained signatures. This provides a simple approach in classifying the videos, additional signatures will be extracted to create a more efficient profiling system to better reveal the nature and characteristics of the video categorization

Annual Report of Undergraduate Research Fellows, August 2008 to May 2009

Annual Report of Undergraduate Research Fellows from August 2008 to May 2009