7,159 research outputs found
State of Alaska Election Security Project Phase 2 Report
A laska’s election system is among the most secure in the country,
and it has a number of safeguards other states are now adopting. But
the technology Alaska uses to record and count votes could be improved—
and the state’s huge size, limited road system, and scattered communities
also create special challenges for insuring the integrity of the vote.
In this second phase of an ongoing study of Alaska’s election
security, we recommend ways of strengthening the system—not only the
technology but also the election procedures. The lieutenant governor
and the Division of Elections asked the University of Alaska Anchorage to
do this evaluation, which began in September 2007.Lieutenant Governor Sean Parnell.
State of Alaska Division of Elections.List of Appendices / Glossary / Study Team / Acknowledgments / Introduction / Summary of Recommendations / Part 1 Defense in Depth / Part 2 Fortification of Systems / Part 3 Confidence in Outcomes / Conclusions / Proposed Statement of Work for Phase 3: Implementation / Reference
Deep Learning and Music Adversaries
OA Monitor ExerciseOA Monitor ExerciseAn {\em adversary} is essentially an algorithm intent on making a classification system perform in some particular way given an input, e.g., increase the probability of a false negative. Recent work builds adversaries for deep learning systems applied to image object recognition, which exploits the parameters of the system to find the minimal perturbation of the input image such that the network misclassifies it with high confidence. We adapt this approach to construct and deploy an adversary of deep learning systems applied to music content analysis. In our case, however, the input to the systems is magnitude spectral frames, which requires special care in order to produce valid input audio signals from network-derived perturbations. For two different train-test partitionings of two benchmark datasets, and two different deep architectures, we find that this adversary is very effective in defeating the resulting systems. We find the convolutional networks are more robust, however, compared with systems based on a majority vote over individually classified audio frames. Furthermore, we integrate the adversary into the training of new deep systems, but do not find that this improves their resilience against the same adversary
Just Say No? Shareholder Voting on Securities Class Actions
The U.S. securities laws allow security-holders to bring a class action suit against a public company and its officers who make materially misleading statements to the market. The class action mechanism allows individual claimants to aggregate their claims. This procedure mitigates the collective action problem among claimants, and also creates potential economies of scale. Despite these efficiencies, the class action mechanism has been criticized for being driven by attorneys and also encouraging nuisance suits. Although various statutory and doctrinal solutions have been proposed and implemented over the years, the concerns over the agency problem and nuisance suits persist. This paper proposes and examines a novel mechanism that attempts to preserve the benefits of the class action system while curtailing its costs: allowing a company’s shareholders to vote on securities class actions. The shareholders can vote on the structural dimensions of securities class actions, e.g., whether to allow class actions at all, limit discovery, impose fee-shifting, etc., before any class action suit has been filed (ex ante voting) or vote to determine the course of a specific class action suit, e.g., whether to terminate or settle a class action (ex post voting). The paper analyzes the conditions under which allowing shareholders to manage and control securities class actions can benefit the shareholders across the board and its potential limitations
Evorus: A Crowd-powered Conversational Assistant Built to Automate Itself Over Time
Crowd-powered conversational assistants have been shown to be more robust
than automated systems, but do so at the cost of higher response latency and
monetary costs. A promising direction is to combine the two approaches for high
quality, low latency, and low cost solutions. In this paper, we introduce
Evorus, a crowd-powered conversational assistant built to automate itself over
time by (i) allowing new chatbots to be easily integrated to automate more
scenarios, (ii) reusing prior crowd answers, and (iii) learning to
automatically approve response candidates. Our 5-month-long deployment with 80
participants and 281 conversations shows that Evorus can automate itself
without compromising conversation quality. Crowd-AI architectures have long
been proposed as a way to reduce cost and latency for crowd-powered systems;
Evorus demonstrates how automation can be introduced successfully in a deployed
system. Its architecture allows future researchers to make further innovation
on the underlying automated components in the context of a deployed open domain
dialog system.Comment: 10 pages. To appear in the Proceedings of the Conference on Human
Factors in Computing Systems 2018 (CHI'18
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