Sequential Deliberation for Social Choice

In large scale collective decision making, social choice is a normative study of how one ought to design a protocol for reaching consensus. However, in instances where the underlying decision space is too large or complex for ordinal voting, standard voting methods of social choice may be impractical. How then can we design a mechanism - preferably decentralized, simple, scalable, and not requiring any special knowledge of the decision space - to reach consensus? We propose sequential deliberation as a natural solution to this problem. In this iterative method, successive pairs of agents bargain over the decision space using the previous decision as a disagreement alternative. We describe the general method and analyze the quality of its outcome when the space of preferences define a median graph. We show that sequential deliberation finds a 1.208- approximation to the optimal social cost on such graphs, coming very close to this value with only a small constant number of agents sampled from the population. We also show lower bounds on simpler classes of mechanisms to justify our design choices. We further show that sequential deliberation is ex-post Pareto efficient and has truthful reporting as an equilibrium of the induced extensive form game. We finally show that for general metric spaces, the second moment of of the distribution of social cost of the outcomes produced by sequential deliberation is also bounded

Tensor Norms and the Classical Communication Complexity of Nonlocal Quantum Measurement

We initiate the study of quantifying nonlocalness of a bipartite measurement by the minimum amount of classical communication required to simulate the measurement. We derive general upper bounds, which are expressed in terms of certain tensor norms of the measurement operator. As applications, we show that (a) If the amount of communication is constant, quantum and classical communication protocols with unlimited amount of shared entanglement or shared randomness compute the same set of functions; (b) A local hidden variable model needs only a constant amount of communication to create, within an arbitrarily small statistical distance, a distribution resulted from local measurements of an entangled quantum state, as long as the number of measurement outcomes is constant.Comment: A preliminary version of this paper appears as part of an article in Proceedings of the the 37th ACM Symposium on Theory of Computing (STOC 2005), 460--467, 200

On Selecting the Nonce Length in Distance-Bounding Protocols

Distance-bounding protocols form a family of challenge-response authentication protocols that have been introduced to thwart relay attacks. They enable a verifier to authenticate and to establish an upper bound on the physical distance to an untrusted prover. We provide a detailed security analysis of a family of such protocols. More precisely, we show that the secret key shared between the verifier and the prover can be leaked after a number of nonce repetitions. The leakage probability, while exponentially decreasing with the nonce length, is only weakly dependent on the key length. Our main contribution is a high probability bound on the number of sessions required for the attacker to discover the secret, and an experimental analysis of the attack under noisy conditions. Both of these show that the attack's success probability mainly depends on the length of the used nonces rather than the length of the shared secret key. The theoretical bound could be used by practitioners to appropriately select their security parameters. While longer nonces can guard against this type of attack, we provide a possible countermeasure which successfully combats these attacks even when short nonces are use

On selecting the nonce length in distance bounding protocols

Distance-bounding protocols form a family of challenge–response authentication protocols that have been introduced to thwart relay attacks. They enable a verifier to authenticate and to establish an upper bound on the physical distance to an untrusted prover.We provide a detailed security analysis of a family of such protocols. More precisely, we show that the secret key shared between the verifier and the prover can be leaked after a number of nonce repetitions. The leakage probability, while exponentially decreasing with the nonce length, is only weakly dependent on the key length. Our main contribution is a high probability bound on the number of sessions required for the attacker to discover the secret, and an experimental analysis of the attack under noisy conditions. Both of these show that the attack’s success probability mainly depends on the length of the used nonces rather than the length of the shared secret key. The theoretical bound could be used by practitioners to appropriately select their security parameters. While longer nonces can guard against this type of attack, we provide a possible countermeasure which successfully combats these attacks even when short nonces are use

Enhancing Energy Minimization Framework for Scene Text Recognition with Top-Down Cues

Recognizing scene text is a challenging problem, even more so than the recognition of scanned documents. This problem has gained significant attention from the computer vision community in recent years, and several methods based on energy minimization frameworks and deep learning approaches have been proposed. In this work, we focus on the energy minimization framework and propose a model that exploits both bottom-up and top-down cues for recognizing cropped words extracted from street images. The bottom-up cues are derived from individual character detections from an image. We build a conditional random field model on these detections to jointly model the strength of the detections and the interactions between them. These interactions are top-down cues obtained from a lexicon-based prior, i.e., language statistics. The optimal word represented by the text image is obtained by minimizing the energy function corresponding to the random field model. We evaluate our proposed algorithm extensively on a number of cropped scene text benchmark datasets, namely Street View Text, ICDAR 2003, 2011 and 2013 datasets, and IIIT 5K-word, and show better performance than comparable methods. We perform a rigorous analysis of all the steps in our approach and analyze the results. We also show that state-of-the-art convolutional neural network features can be integrated in our framework to further improve the recognition performance

Crowdsourcing in Computer Vision

Computer vision systems require large amounts of manually annotated data to properly learn challenging visual concepts. Crowdsourcing platforms offer an inexpensive method to capture human knowledge and understanding, for a vast number of visual perception tasks. In this survey, we describe the types of annotations computer vision researchers have collected using crowdsourcing, and how they have ensured that this data is of high quality while annotation effort is minimized. We begin by discussing data collection on both classic (e.g., object recognition) and recent (e.g., visual story-telling) vision tasks. We then summarize key design decisions for creating effective data collection interfaces and workflows, and present strategies for intelligently selecting the most important data instances to annotate. Finally, we conclude with some thoughts on the future of crowdsourcing in computer vision.Comment: A 69-page meta review of the field, Foundations and Trends in Computer Graphics and Vision, 201