JAG: Reliable and Predictable Wireless Agreement under External Radio Interference

Wireless low-power transceivers used in sensor networks typically operate in unlicensed frequency bands that are subject to external radio interference caused by devices transmitting at much higher power.communication protocols should therefore be designed to be robust against such interference. A critical building block of many protocols at all layers is agreement on a piece of information among a set of nodes. At the MAC layer, nodes may need to agree on a new time slot or frequency channel, at the application layer nodes may need to agree on handing over a leader role from one node to another. Message loss caused by interference may break agreement in two different ways: none of the nodes uses the new information (time slot, channel, leader) and sticks with the previous assignment, or-even worse-some nodes use the new information and some do not. This may lead to reduced performance or failures. In this paper, we investigate the problem of agreement under external radio interference and point out the limitations of traditional message-based approaches. We propose JAG, a novel protocol that uses jamming instead of message transmissions to make sure that two neighbouring nodes agree, and show that it outperforms message-based approaches in terms of agreement probability, energy consumption, and time-to-completion. We further show that JAG can be used to obtain performance guarantees and meet the requirements of applications with real-time constraints.CONETReSens

Local Guarantees in Graph Cuts and Clustering

Correlation Clustering is an elegant model that captures fundamental graph cut problems such as Min $s-t$ Cut, Multiway Cut, and Multicut, extensively studied in combinatorial optimization. Here, we are given a graph with edges labeled $+$ or $-$ and the goal is to produce a clustering that agrees with the labels as much as possible: $+$ edges within clusters and $-$ edges across clusters. The classical approach towards Correlation Clustering (and other graph cut problems) is to optimize a global objective. We depart from this and study local objectives: minimizing the maximum number of disagreements for edges incident on a single node, and the analogous max min agreements objective. This naturally gives rise to a family of basic min-max graph cut problems. A prototypical representative is Min Max $s-t$ Cut: find an $s-t$ cut minimizing the largest number of cut edges incident on any node. We present the following results: $(1)$ an $O(\sqrt{n})$-approximation for the problem of minimizing the maximum total weight of disagreement edges incident on any node (thus providing the first known approximation for the above family of min-max graph cut problems), $(2)$ a remarkably simple $7$-approximation for minimizing local disagreements in complete graphs (improving upon the previous best known approximation of $48$), and $(3)$ a $1/(2+\varepsilon)$-approximation for maximizing the minimum total weight of agreement edges incident on any node, hence improving upon the $1/(4+\varepsilon)$-approximation that follows from the study of approximate pure Nash equilibria in cut and party affiliation games

Internet Governance: the State of Play

The Global Forum on Internet Governance held by the UNICT Task Force in New York on 25-26 March concluded that Internet governance issues were many and complex. The Secretary-General's Working Group on Internet Governance will have to map out and navigate this complex terrain as it makes recommendations to the World Summit on an Information Society in 2005. To assist in this process, the Forum recommended, in the words of the Deputy Secretary-General of the United Nations at the closing session, that a matrix be developed "of all issues of Internet governance addressed by multilateral institutions, including gaps and concerns, to assist the Secretary-General in moving forward the agenda on these issues." This paper takes up the Deputy Secretary-General's challenge. It is an analysis of the state of play in Internet governance in different forums, with a view to showing: (1) what issues are being addressed (2) by whom, (3) what are the types of consideration that these issues receive and (4) what issues are not adequately addressed

Arrogance and deep disagreement

I intend to bring recent work applying virtue theory to the study of argument to bear on a much older problem, that of disagreements that resist rational resolution, sometimes termed "deep disagreements". Just as some virtue epistemologists have lately shifted focus onto epistemic vices, I shall argue that a renewed focus on the vices of argument can help to illuminate deep disagreements. In particular, I address the role of arrogance, both as a factor in the diagnosis of deep disagreements and as an obstacle to their mutually acceptable resolution. Arrogant arguers are likely to make any disagreements to which they are party seem deeper than they really are and arrogance impedes the strategies that we might adopt to resolve deep disagreements. As a case in point, since arrogant or otherwise vicious arguers cannot be trusted not to exploit such strategies for untoward ends, any policy for deep disagreement amelioration must require particularly close attention to the vices of argument, lest they be exploited by the unscrupulous

Information, fairness, and efficiency in bargaining

Economic theory assumes people strive for efficient agreements that benefit all consenting parties. The frequency of mutually destructive conflicts such as strikes, litigation, and military conflict, therefore, poses an important challenge to the field

Exploiting `Subjective' Annotations

Many interesting phenomena in conversation can only be annotated as a subjective task, requiring interpretative judgements from annotators. This leads to data which is annotated with lower levels of agreement not only due to errors in the annotation, but also due to the differences in how annotators interpret conversations. This paper constitutes an attempt to find out how subjective annotations with a low level of agreement can profitably be used for machine learning purposes. We analyse the (dis)agreements between annotators for two different cases in a multimodal annotated corpus and explicitly relate the results to the way machine-learning algorithms perform on the annotated data. Finally we present two new concepts, namely `subjective entity' classifiers resp. `consensus objective' classifiers, and give recommendations for using subjective data in machine-learning applications.\u

Transparency in Land-Based Investment: Key Questions and Next Steps

Large-scale investments in agriculture and forestry are often shrouded in secrecy. In many cases, they are negotiated without the involvement of affected communities, approved through opaque decision-making procedures, and governed by legal agreements that are difficult both to access and to understand. This systemic lack of transparency impedes accountability and exacerbates ongoing disagreements about the real costs and benefits for investors, host countries, and their citizens. Jointly authored by CCSI and the Open Contracting Partnership, this briefing note examines why contract disclosure and a contracting process that is open, accessible, and inclusive are important; what such transparency entails; and how various stakeholders can work towards achieving it