I was lucky enough to represent ACM and ACM-W at the recent Ada Lovelace Symposium at Oxford University, celebrating her 200th birthday. I participated in a panel "Enchantress of Abstraction and Bride of Science: can women scientists escape being icons, role-models and heroines." Why do so many current organizations and events identify with and recognize Ada Lovelace? We are well into the 21st century; Ada was born 200 years ago. Why do so many women today seem to look to her as a model and icon?

Suresh Venkatasubramanian of the University of Utah presented a method for finding disparate impact in algorithms last year at the ACM Conference on Knowledge Discovery and Data Mining. If you have ever searched for hotel rooms online, you have probably had this experience: surf over to another website to read a news story and the page fills up with ads for travel sites, offering deals on hotel rooms in the city you plan to visit. Buy something on Amazon, and ads for similar products will follow you around the Web. The practice of profiling people online means companies get more value from their advertising dollars and users are more likely to see ads that interest them. The practice has a downside, though, when the profiling is based on sensitive attributes, such as race, sex, or sexual orientation.

Peter Naur, a Danish computer scientist and 2005 recipient of the ACM A.M. Turing Award for fundamental contributions to programming language design and the definition of Algol 60, to compiler design, and to the art and practice of computer programming, died January 3 after a brief illness. Born in Fredricksberg, Denmark, Naur studied astronomy at the University of Copenhagen, where he received his Ph.D. in that field before going to Kings College Cambridge in the 1950s to conduct research both into astronomy and the emerging field of computer programming. As he told Computerworld Denmark in a 2014 interview (http://bit.ly/1O13v1I), "I had the great privilege to get to Cambridge in the early 1950s. Here I discovered that calculations of planetary motion that could take several hours, could now be carried out in seconds with a computer."

In a world with more mobile phones than flush toilets, digital devices are now standard equipment among even the world's poorest and most remote people. Farmers in these areas are getting tools for their devices that help deliver water, nutrients, and medicine to plants as needed; test for crop diseases and malnourishment; and survey their soil for future planning. In some cases, these emerging apps are the biggest new technologies resource-poor farms have seen in hundreds of years. That is not very surprising to Rajiv "Raj" Khosla, professor of Precision Agriculture at the College of Agricultural Sciences of Colorado State University. "What we're finding is that many small-scale farmers in resource-poor environments are still farming in the 1500s. They're looking for leapfrog technologies," he said.

Though with progress, model learning and performing posterior inference still remains a common challenge for using deep generative models, especially for handling discrete hidden variables. This paper is mainly concerned with algorithms for learning Helmholz machines, which is characterized by pairing the generative model with an auxiliary inference model. A common drawback of previous learning algorithms is that they indirectly optimize some bounds of the targeted marginal log-likelihood. In contrast, we successfully develop a new class of algorithms, based on stochastic approximation (SA) theory of the Robbins-Monro type, to directly optimize the marginal log-likelihood and simultaneously minimize the inclusive KL-divergence. The resulting learning algorithm is thus called joint SA (JSA). Moreover, we construct an effective MCMC operator for JSA. Our results on the MNIST datasets demonstrate that the JSA's performance is consistently superior to that of competing algorithms like RWS, for learning a range of difficult models.

This paper offers a characterization of fundamental limits on the classification and reconstruction of high-dimensional signals from low-dimensional features, in the presence of side information. We consider a scenario where a decoder has access both to linear features of the signal of interest and to linear features of the side information signal; while the side information may be in a compressed form, the objective is recovery or classification of the primary signal, not the side information. The signal of interest and the side information are each assumed to have (distinct) latent discrete labels; conditioned on these two labels, the signal of interest and side information are drawn from a multivariate Gaussian distribution. With joint probabilities on the latent labels, the overall signal-(side information) representation is defined by a Gaussian mixture model. We then provide sharp sufficient and/or necessary conditions for these quantities to approach zero when the covariance matrices of the Gaussians are nearly low-rank. These conditions, which are reminiscent of the well-known Slepian-Wolf and Wyner-Ziv conditions, are a function of the number of linear features extracted from the signal of interest, the number of linear features extracted from the side information signal, and the geometry of these signals and their interplay. Moreover, on assuming that the signal of interest and the side information obey such an approximately low-rank model, we derive expansions of the reconstruction error as a function of the deviation from an exactly low-rank model; such expansions also allow identification of operational regimes where the impact of side information on signal reconstruction is most relevant. Our framework, which offers a principled mechanism to integrate side information in high-dimensional data problems, is also tested in the context of imaging applications.

Traditional theories of social models conceptualize teams as distributed processors, disregarding the interdependence necessary to multi-task. Yet, interdependence characterizes social behavior. Instead, traditional theory favor cooperation, a state of least entropy production (LEP), without understanding the causes, limits or consequences of cooperation. As a simple example of interdependence, foraging prey overgraze forests free of predators. In our model, interdependence creates uncertainty, tradeoffs and signals (e.g., prices, coordination, innovation). Unlike individuals, the ability of teams to multitask reflects a quantum-like entanglement that represents maximum entropy production (MEP) when solving the problems signaled by society to improve its welfare. Our model supports findings that evolution in nature is driven by the MEP from making intelligent choices. Exploiting interdependence improves team intelligence, improves performance and reduces the risk of human error; forced cooperation disorganizes it by increasing the risk of error; e.g., if team cooperation improves teamwork, widespread forced cooperation in an autocracy or bureaucracy reduces social intelligence by adding unnecessary noise to signals. In our model, competition between teams self-organizes outsiders willing to sort through the noise for signals of the choices that improve social welfare (e.g., teams in courtrooms; science; entertainment; sports; businesses). Social systems organized around competition (e.g., stronger signals from robust checks and balances) better control a society by more correctly sizing teams to solve problems with fewer errors compared to autocracies or bureaucracies. Overall, we predict, the density of MEP directed at solving problems in a society with the constraints imposed from strong checks and balances, yet able to freely self-organize its labor and capital within those constraints, is denser.

As autonomous systems are becoming more and more pervasive, they often have to make decisions concerning moral and ethical values. There are many approaches to incorporating moral values in autonomous decision-making that are based on some sort of logical deduction. However, we argue here, in order for decision-making to seem persuasive to humans, it needs to reflect human values and judgments. Employing some insights from our ongoing researchusing features of the blackboard architecture for a context-aware recommender system, and a legal decision-making system that incorporates supra-legal aspects, we aim to explore if this architecture can also be adapted to implement a moral decision-making system that generates rationales that are persuasive to humans. Our vision is that such a system can be used as an advisory system to consider a situation from different moral perspectives, and generate ethical pros and cons of taking a particular course of action in a given context.

We know too that guilt may be alleviated by private confession Theorists conceive of shame and guilt as belonging to the (namely to a priest or a psychotherapist) plus the family of self-conscious emotions (Lewis 1990) (Fischer renouncing of past failings in future. Because of their private and Tangney 1995) (Tangney and Dearing 2002), invoked character, such confessions and atonements, given through self-reflection and self-evaluation. Though both their cost (prayers or fees), render temptation defecting less have evolved to promote cooperation, guilt and shame can probable. Public or open confession of guilt can be coordinated be treated separately. Guilt is an inward private phenomenon, with apology for better effect, and the cost appertained though it can promote apology, and even spontaneous to some common good (like charity), or as individual public confession. Shame is inherently public, though it compensation to injured parties.

We study the complexity of a combinatorial variant of the Shift Bribery problem in elections. In the standard Shift Bribery problem, we are given an election where each voter has a preference order over the set of candidates and where an outside agent, the briber, can pay each voter to rank the briber's favorite candidate a given number of positions higher. The goal is to ensure the victory of the briber's preferred candidate. The combinatorial variant of the problem, introduced in this paper, models settings where it is possible to affect the position of the preferred candidate in multiple votes, either positively or negatively, with a single bribery action. This variant of the problem is particularly interesting in the context of large-scale campaign management problems (which, from the technical side, are modeled as bribery problems). We show that, in general, the combinatorial variant of the problem is highly intractable; specifically, NP-hard, hard in the parameterized sense, and hard to approximate. Nevertheless, we provide parameterized algorithms and approximation algorithms for natural restricted cases.