62,185 research outputs found

    More than just friends? Facebook, disclosive ethics and the morality of technology

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    Social networking sites have become increasingly popular destinations for people wishing to chat, play games, make new friends or simply stay in touch. Furthermore, many organizations have been quick to grasp the potential they offer for marketing, recruitment and economic activities. Nevertheless, counterclaims depict such spaces as arenas where deception, social grooming and the posting of defamatory content flourish. Much research in this area has focused on the ends to which people deploy the technology, and the consequences arising, with a view to making policy recommendations and ethical interventions. In this paper, we argue that tracing where morality lies is more complex than these efforts suggest. Using the case of a popular social networking site, and concepts about the morality of technology, we disclose the ethics of Facebook as diffuse and multiple. In our conclusions we provide some reflections on the possibilities for action in light of this disclosure

    VIP: Incorporating Human Cognitive Biases in a Probabilistic Model of Retweeting

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    Information spread in social media depends on a number of factors, including how the site displays information, how users navigate it to find items of interest, users' tastes, and the `virality' of information, i.e., its propensity to be adopted, or retweeted, upon exposure. Probabilistic models can learn users' tastes from the history of their item adoptions and recommend new items to users. However, current models ignore cognitive biases that are known to affect behavior. Specifically, people pay more attention to items at the top of a list than those in lower positions. As a consequence, items near the top of a user's social media stream have higher visibility, and are more likely to be seen and adopted, than those appearing below. Another bias is due to the item's fitness: some items have a high propensity to spread upon exposure regardless of the interests of adopting users. We propose a probabilistic model that incorporates human cognitive biases and personal relevance in the generative model of information spread. We use the model to predict how messages containing URLs spread on Twitter. Our work shows that models of user behavior that account for cognitive factors can better describe and predict user behavior in social media.Comment: SBP 201

    Fiber Orientation Estimation Guided by a Deep Network

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    Diffusion magnetic resonance imaging (dMRI) is currently the only tool for noninvasively imaging the brain's white matter tracts. The fiber orientation (FO) is a key feature computed from dMRI for fiber tract reconstruction. Because the number of FOs in a voxel is usually small, dictionary-based sparse reconstruction has been used to estimate FOs with a relatively small number of diffusion gradients. However, accurate FO estimation in regions with complex FO configurations in the presence of noise can still be challenging. In this work we explore the use of a deep network for FO estimation in a dictionary-based framework and propose an algorithm named Fiber Orientation Reconstruction guided by a Deep Network (FORDN). FORDN consists of two steps. First, we use a smaller dictionary encoding coarse basis FOs to represent the diffusion signals. To estimate the mixture fractions of the dictionary atoms (and thus coarse FOs), a deep network is designed specifically for solving the sparse reconstruction problem. Here, the smaller dictionary is used to reduce the computational cost of training. Second, the coarse FOs inform the final FO estimation, where a larger dictionary encoding dense basis FOs is used and a weighted l1-norm regularized least squares problem is solved to encourage FOs that are consistent with the network output. FORDN was evaluated and compared with state-of-the-art algorithms that estimate FOs using sparse reconstruction on simulated and real dMRI data, and the results demonstrate the benefit of using a deep network for FO estimation.Comment: A shorter version is accepted by MICCAI 201

    On the energy momentum dispersion in the lattice regularization

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    For a free scalar boson field and for U(1) gauge theory finite volume (infrared) and other corrections to the energy-momentum dispersion in the lattice regularization are investigated calculating energy eigenstates from the fall off behavior of two-point correlation functions. For small lattices the squared dispersion energy defined by Edis2=Ekāƒ—2āˆ’E02āˆ’4āˆ‘i=1dāˆ’1sinā”(ki/2)2E_{\rm dis}^2=E_{\vec{k}}^2-E_0^2-4\sum_{i=1}^{d-1}\sin(k_i/2)^2 is in both cases negative (dd is the Euclidean space-time dimension and Ekāƒ—E_{\vec{k}} the energy of momentum kāƒ—\vec{k} eigenstates). Observation of Edis2=0E_{\rm dis}^2=0 has been an accepted method to demonstrate the existence of a massless photon (E0=0E_0=0) in 4D lattice gauge theory, which we supplement here by a study of its finite size corrections. A surprise from the lattice regularization of the free field is that infrared corrections do {\it not} eliminate a difference between the groundstate energy E0E_0 and the mass parameter MM of the free scalar lattice action. Instead, the relation E0=coshā”āˆ’1(1+M2/2)E_0=\cosh^{-1} (1+M^2/2) is derived independently of the spatial lattice size.Comment: 9 pages, 2 figures. Parts of the paper have been rewritten and expanded to clarify the result

    Monte Carlo Simulation of the Three-dimensional Ising Spin Glass

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    We study the 3D Edwards-Anderson model with binary interactions by Monte Carlo simulations. Direct evidence of finite-size scaling is provided, and the universal finite-size scaling functions are determined. Using an iterative extrapolation procedure, Monte Carlo data are extrapolated to infinite volume up to correlation length \xi = 140. The infinite volume data are consistent with both a continuous phase transition at finite temperature and an essential singularity at finite temperature. An essential singularity at zero temperature is excluded.Comment: 5 pages, 6 figures. Proceedings of the Workshop "Computer Simulation Studies in Condensed Matter Physics XII", Eds. D.P. Landau, S.P. Lewis, and H.B. Schuettler, (Springer Verlag, Heidelberg, Berlin, 1999

    Collaborative Epistemic Discourse in Classroom Information Seeking Tasks

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    We discuss the relationship between information seeking, and epistemic beliefs ā€“ beliefs about the source, structure, complexity, and stability of knowledge ā€“ in the context of collaborative information seeking discourses. We further suggest that both information seeking, and epistemic cognition research agendas have suffered from a lack of attention to how information seeking as a collaborative activity is mediated by talk between partners ā€“ an area we seek to address in this paper. A small-scale observational study using sociocultural discourse analysis was conducted with eight eleven year old pupils who carried out search engine tasks in small groups. Qualitative and quantitative analysis were performed on their discussions using sociocultural discourse analytic techniques. Extracts of the dialogue are reported, informed by concordance analysis and quantitative coding of dialogue duration. We find that 1) discourse which could be characterised as ā€˜epistemicā€™ is identifiable in student talk, 2) that it is possible to identify talk which is more or less productive, and 3) that epistemic talk is associated with positive learning outcomes
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