Youth multilingualism in South Africa's hip-hop culture: a metapragmatic analysis

This paper describes the practice of youth multilingualism in South Africa's hip-hop culture, in an online social media space and an advertising space. Based on a multi-sited ethnographic fieldwork study of youth multilingual practices, comprising of the following data sets - multilingual interviews, observations, multilingual interactions and performances, documents and online social networking interactions - the paper reports on how young multilingual speakers active in the hip-hop culture of the country talk and write about the intermixing of racial and ethnic speech forms, as well as use registers in the practice of gendered identities. The argument I put forth in the paper is that the examples of youth multilingualism suggest a complex picture of youth multilingual contact in postcolonial South Africa, and one that require a sociocultural linguistic response that accounts for the cultural influence of youth multilingualisms in local hip-hop culture. To such an end, I suggest that multilingual policy planning in the country should be readjusted to the complex sociocultural changes we see emerge with youth multilingual practices.IBS

The Duplicity of Online Behavior

People commonly believe that any form of deception, no matter how innocuous it is and no matter whether the deceiving person intended it otherwise, is always morally wrong. In this paper, I will argue that deceiving in real-time is morally distinguishable from deceiving on-line because online actions aren’t as fine-grained as actions occurring in real-time. Our failure to detect the fine-grained characteristics of another avatar leads us to believe that that avatar intended to do a moral harm. Openly deceiving someone on Facebook or Twitter is not a way to build wholesome virtual friendships but to destroy them. This paper will show how the traditional understanding of the doing / allowing distinction fails to apply in cyberspace

Counter-intuitive throughput behaviors in networks under end-to-end control

It has been shown that as long as traffic sources adapt their rates to aggregate congestion measure in their paths, they implicitly maximize certain utility. In this paper we study some counter-intuitive throughput behaviors in such networks, pertaining to whether a fair allocation is always inefficient and whether increasing capacity always raises aggregate throughput. A bandwidth allocation policy can be defined in terms of a class of utility functions parameterized by a scalar a that can be interpreted as a quantitative measure of fairness. An allocation is fair if alpha is large and efficient if aggregate throughput is large. All examples in the literature suggest that a fair allocation is necessarily inefficient. We characterize exactly the tradeoff between fairness and throughput in general networks. The characterization allows us both to produce the first counter-example and trivially explain all the previous supporting examples. Surprisingly, our counter-example has the property that a fairer allocation is always more efficient. In particular it implies that maxmin fairness can achieve a higher throughput than proportional fairness. Intuitively, we might expect that increasing link capacities always raises aggregate throughput. We show that not only can throughput be reduced when some link increases its capacity, more strikingly, it can also be reduced when all links increase their capacities by the same amount. If all links increase their capacities proportionally, however, throughput will indeed increase. These examples demonstrate the intricate interactions among sources in a network setting that are missing in a single-link topology

Ensuring an Impartial Jury in the Age of Social Media

The explosive growth of social networking has placed enormous pressure on one of the most fundamental of American institutions—the impartial jury. Through social networking services like Facebook and Twitter, jurors have committed significant and often high-profile acts of misconduct. Just recently, the Arkansas Supreme Court reversed a death sentence because a juror Tweeted about the case during deliberations. In light of the significant risks to a fair trial that arise when jurors communicate through social media during trial, judges must be vigilant in monitoring for potential outside influences and in deterring misconduct. In this Article, we present informal survey data from actual jurors on their use of social networking during trial. We discuss the rise of web-based social networks like Facebook and Twitter, and the concerns that arise when jurors communicate about a case through social media before returning a verdict. After surveying how courts have responded to jurors’ social media use, we describe the results of the informal survey. The results support a growing consensus in the legal profession that courts should frequently, as a matter of course, instruct jurors not to use social media to communicate about trial. Although others have stressed the importance of jury instructions in this area, we hope that the informal survey data will further the dialogue by providing an important perspective—that of actual jurors

An LSPI based reinforcement learning approach to enable network cooperation in cognitive wireless sensor networks

The number of wirelessly communicating devices increases every day, along with the number of communication standards and technologies that they use to exchange data. A relatively new form of research is trying to find a way to make all these co-located devices not only capable of detecting each other's presence, but to go one step further - to make them cooperate. One recently proposed way to tackle this problem is to engage into cooperation by activating 'network services' (such as internet sharing, interference avoidance, etc.) that offer benefits for other co-located networks. This approach reduces the problem to the following research topic: how to determine which network services would be beneficial for all the cooperating networks. In this paper we analyze and propose a conceptual solution for this problem using the reinforcement learning technique known as the Least Square Policy Iteration (LSPI). The proposes solution uses a self-learning entity that negotiates between different independent and co-located networks. First, the reasoning entity uses self-learning techniques to determine which service configuration should be used to optimize the network performance of each single network. Afterwards, this performance is used as a reference point and LSPI is used to deduce if cooperating with other co-located networks can lead to even further performance improvements

Active networks: an evolution of the internet

Active Networks can be seen as an evolution of the classical model of packet-switched networks. The traditional and ”passive” network model is based on a static definition of the network node behaviour. Active Networks propose an “active” model where the intermediate nodes (switches and routers) can load and execute user code contained in the data units (packets). Active Networks are a programmable network model, where bandwidth and computation are both considered shared network resources. This approach opens up new interesting research fields. This paper gives a short introduction of Active Networks, discusses the advantages they introduce and presents the research advances in this field

A duality model of TCP and queue management algorithms

We propose a duality model of end-to-end congestion control and apply it to understanding the equilibrium properties of TCP and active queue management schemes. The basic idea is to regard source rates as primal variables and congestion measures as dual variables, and congestion control as a distributed primal-dual algorithm over the Internet to maximize aggregate utility subject to capacity constraints. The primal iteration is carried out by TCP algorithms such as Reno or Vegas, and the dual iteration is carried out by queue management algorithms such as DropTail, RED or REM. We present these algorithms and their generalizations, derive their utility functions, and study their interaction

XMILE:An XML-based approach for programmable networks

In this paper we describe an XML-based platform for dynamic active node policy updates. XML supports the definitionof specific policy languages, their extension to satisfy new needs and the management of deployed policies on differentactive nodes. We show an example of the management of router packet forwarding policies where the XML policiesthat drive the packet routing are updated at run-time on the active nodes depending on the network status. The platformdecouples policy management, which is handled through XML interpretation, from packet forwarding that, forperformance reasons has to be implemented in more efficient languages

Optimization flow control -- I: Basic algorithm and convergence

We propose an optimization approach to flow control where the objective is to maximize the aggregate source utility over their transmission rates. We view network links and sources as processors of a distributed computation system to solve the dual problem using a gradient projection algorithm. In this system, sources select transmission rates that maximize their own benefits, utility minus bandwidth cost, and network links adjust bandwidth prices to coordinate the sources' decisions. We allow feedback delays to be different, substantial, and time varying, and links and sources to update at different times and with different frequencies. We provide asynchronous distributed algorithms and prove their convergence in a static environment. We present measurements obtained from a preliminary prototype to illustrate the convergence of the algorithm in a slowly time-varying environment. We discuss its fairness property