Capturing Hate: Eyewitness Videos Provide New Source of Data on Prevalence of Transphobic Violence - Executive Summary

"Capturing Hate" is a new report from the WITNESS Media Lab which collected and analyzed eyewitness videos of transphobic violence, primarily viewed for entertainment purposes, in order to illustrate the extent of hate and violence faced by the LGBTQ community in the United States.The data, and the stories and voices which contextualize this data, aims to equip advocacy groups and the media with the tools to effectively and ethically use eyewitness videos to document and report on violence affecting the LGBTQ community.

The UK geography of the E-Society: a national classification

It is simplistic to think of the impacts of new information and communication technologies (ICTs) in terms of a single, or even small number of, 'digital divides'. As developments in what has been termed the ?e-society? reach wider and more generalisedaudiences, so it becomes appropriate to think of digital media as having wider-ranging but differentiated impacts upon consumer transactions, information gathering and citizen participation. This paper describes the development of a detailed, nationwide household classification based on levels of awareness of different ICTs; levels of use of ICTs; andtheir perceived impacts upon human capital formation and the quality of life. It discusses how geodemographic classification makes it possible to provide context for detailed case studies, and hence identify how policy might best improve both the quality and degree ofsociety?s access to ICTs. The primary focus of the paper is methodological, but it alsoillustrates how the classification may be used to investigate a range of regional and subregional policy issues. This paper illustrates the potential contribution of bespoke classifications to evidence-based policy, and the likely benefits of combining the most appropriate methods, techniques, datasets and practices that are used in the public and private sectors. It is simplistic to think of the impacts of new information and communication technologies (ICTs) in terms of a single, or even small number of, 'digital divides'. As developments in what has been termed the ?e-society? reach wider and more generalisedaudiences, so it becomes appropriate to think of digital media as having wider-rangingbut differentiated impacts upon consumer transactions, information gathering and citizen participation. This paper describes the development of a detailed, nationwide household classification based on levels of awareness of different ICTs; levels of use of ICTs; and their perceived impacts upon human capital formation and the quality of life. It discusses how geodemographic classification makes it possible to provide context for detailed case studies, and hence identify how policy might best improve both the quality and degree of society?s access to ICTs. The primary focus of the paper is methodological, but it also illustrates how the classification may be used to investigate a range of regional and subregional policy issues. This paper illustrates the potential contribution of bespoke classifications to evidence-based policy, and the likely benefits of combining the most appropriate methods, techniques, datasets and practices that are used in the public and private sectors

Where Art Thou? Regional Distribution of Culture Workers in Finland

This study seeks to shed light on the regional distribution of culture workers in Finland. What factors – if any - make the location decisions of culture workers different from that of others? This study uses a rich micro level data for an application of multinomial logit model. The data is from the Finnish Longitudinal Census File and it contains information e.g. on individual's personal charactersitics, family characteristics and working life characteristics. The estimation results show that being a culture worker is an important factor in locational choice: the coefficient of living in a metropolitan area compared to rural areas is highly positive. According to the estimated marginal effects, the likelihood of living in a metropolitan region increases as much as 22 percentage points if the person is a culture worker. Another interesting notion is that the residential choices of cultural entrepreneurs seem to differ from that of other entrepreneurs.

Speech Processing in Computer Vision Applications

Deep learning has been recently proven to be a viable asset in determining features in the field of Speech Analysis. Deep learning methods like Convolutional Neural Networks facilitate the expansion of specific feature information in waveforms, allowing networks to create more feature dense representations of data. Our work attempts to address the problem of re-creating a face given a speaker\u27s voice and speaker identification using deep learning methods. In this work, we first review the fundamental background in speech processing and its related applications. Then we introduce novel deep learning-based methods to speech feature analysis. Finally, we will present our deep learning approaches to speaker identification and speech to face synthesis. The presented method can convert a speaker audio sample to an image of their predicted face. This framework is composed of several chained together networks, each with an essential step in the conversion process. These include Audio embedding, encoding, and face generation networks, respectively. Our experiments show that certain features can map to the face and that with a speaker\u27s voice, DNNs can create their face and that a GUI could be used in conjunction to display a speaker recognition network\u27s data

Finding Street Gang Members on Twitter

Most street gang members use Twitter to intimidate others, to present outrageous images and statements to the world, and to share recent illegal activities. Their tweets may thus be useful to law enforcement agencies to discover clues about recent crimes or to anticipate ones that may occur. Finding these posts, however, requires a method to discover gang member Twitter profiles. This is a challenging task since gang members represent a very small population of the 320 million Twitter users. This paper studies the problem of automatically finding gang members on Twitter. It outlines a process to curate one of the largest sets of verifiable gang member profiles that have ever been studied. A review of these profiles establishes differences in the language, images, YouTube links, and emojis gang members use compared to the rest of the Twitter population. Features from this review are used to train a series of supervised classifiers. Our classifier achieves a promising F1 score with a low false positive rate.Comment: 8 pages, 9 figures, 2 tables, Published as a full paper at 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2016