Collaborative bandit learning has become an emerging focus for personalized recommendation. It leverages user dependence for joint model estimation and recommendation. As such online learning solutions directly learn from users, e.g., result clicks, they bring in new challenges in privacy protection. Despite the existence of recent studies about privacy in contextual bandit algorithms, how to efficiently protect user privacy in a collaborative bandit learning environment remains unknown.

The artificial bee colony (ABC) algorithm has been successfully used to find good solutions to real parameter optimization problems. Unfortunately, solutions to graph theory problems are often encoded as binary strings. In this paper we propose a modified ABC problem suitable for finding solutions to large graph theory problems. The ABC algorithm searches in a large dimension unit hypercube and the real-valued components are converted into binary substrings, which can be concatenated to form a potential solution to a graph theory problem. A different method of deploying scout bees is also incorporated to help escape local optima. Our modified ABC algorithm is tested on two difficult large graph problems.

The iterated prisoner's dilemma (IPD) game has been extensively investigated to help researchers gain a better understanding of how cooperation develops in populations. One criticism of IPD is it underestimates the level of cooperation particularly in human populations. The iterated snowdrift (ISD) game has emerged as a viable alternative model, in part because it predicts higher cooperation levels. To date no numerical analysis of ISD has been done. In this paper we report the results from a numerical analysis conducted on an ISD with an N-player, well-mixed population. Our results show, with certain cost-to benefit ratios, evolved ISD strategies can maintain surprisingly high quasi-stable levels of cooperation in the population.

While developing data-centric programs, users often run (portions of) their programs over real data, to see how they behave and what the output looks like. Doing so makes it easier to formulate, understand and compose programs correctly, compared with examination of program logic alone. For large input data sets, these experimental runs can be time-consuming and inefficient. Unfortunately, sampling the input data does not always work well, because selective operations such as filter and join can lead to empty results over sampled inputs, and unless certain indexes are present there is no way to generate biased samples efficiently. Consequently new methods are needed for generating example input data for data-centric programs.

Increasingly, organizations capture, transform and analyze enormous data sets. Prominent examples include internet companies and e-science. The Map-Reduce scalable dataflow paradigm has become popular for these applications. Its simple, explicit dataflow programming model is favored by some over the traditional high-level declarative approach: SQL. On the other hand, the extreme simplicity of Map-Reduce leads to much low-level hacking to deal with the many-step, branching dataflows that arise in practice. Moreover, users must repeatedly code standard operations such as join by hand. These practices waste time, introduce bugs, harm readability, and impede optimizations. Pig is a high-level dataflow system that aims at a sweet spot between SQL and Map-Reduce. Pig offers SQL-style high-level data manipulation constructs, which can be assembled in an explicit dataflow and interleaved with custom Map- and Reduce-style functions or executables. Pig programs are compiled into sequences of Map-Reduce jobs, and executed in the Hadoop Map-Reduce environment. Both Pig and Hadoop are open-source projects administered by the Apache Software Foundation. This paper describes the challenges we faced in developing Pig, and reports performance comparisons between Pig execution and raw Map-Reduce execution.