Packet scheduling is key to quality of service (QoS) capabilities of broadband wired and wireless networks. In a heterogeneous traffic environment, a comprehensive QoS packet scheduler must strike a balance between flow fairness and access delay. Many advanced packet scheduling solutions have targeted fair bandwidth allocation while protecting delay-constrained traffic by adding priority queue(...

The US Security and Exchange Commission implemented Regulation Fair Disclosure in 2000, requiring that an issuer must make relevant information disclosed to any investor available to the general public in a fair manner. Focusing on firms that are affected by the regulation, we propose a model that characterizes the behavior of two types of investors—one professional investor and many small inve...

A common real-life problem is to fairly allocate a number of indivisible objects and a fixed amount of money among a group of agents. Fairness requires that each agent weakly prefers his consumption bundle to any other agent’s bundle. In this context, fairness is incompatible with budget balance and nonmanipulability (Green and Laffont 1979). Our approach here is to weaken or abandon nonmanipul...

Fair clustering aims to partition a dataset while mitigating bias in the original dataset. Developing fair algorithms has gained increasing attention from machine learning community. In this paper, we propose k-means algorithm, first (FFC), which consists of an initialization stage, relaxation and improvement stage. is employed cluster each group. Then combination step refinement are applied en...

Some cryptographic tasks, such as contract signing and other related tasks, need to ensure complex, branching time security properties. When defining such properties one needs to deal with subtle problems regarding the scheduling of non-deterministic decisions, the delivery of messages sent on resilient (non-adversarially controlled) channels, fair executions (executions where no party, both ho...

Machine learning is now being used to make crucial decisions about people’s lives. For nearly all of these decisions there is a risk that individuals of a certain race, gender, sexual orientation, or any other subpopulation are unfairly discriminated against. Our recent method has demonstrated how to use techniques from counterfactual inference to make predictions fair across different subpopul...