Submodular Welfare Maximization

An overview of different variants of the submodular welfare maximization problem in combinatorial auctions. In particular, I studied the existing algorithmic and game theoretic results for submodular welfare maximization problem and its applications in other areas such as social networks

Usability of Humanly Computable Passwords

Reusing passwords across multiple websites is a common practice that compromises security. Recently, Blum and Vempala have proposed password strategies to help people calculate, in their heads, passwords for different sites without dependence on third-party tools or external devices. Thus far, the security and efficiency of these "mental algorithms" has been analyzed only theoretically. But are such methods usable? We present the first usability study of humanly computable password strategies, involving a learning phase (to learn a password strategy), then a rehearsal phase (to login to a few websites), and multiple follow-up tests. In our user study, with training, participants were able to calculate a deterministic eight-character password for an arbitrary new website in under 20 seconds

Causal Fair Metric: Bridging Causality, Individual Fairness, and Adversarial Robustness

Adversarial perturbation is used to expose vulnerabilities in machine learning models, while the concept of individual fairness aims to ensure equitable treatment regardless of sensitive attributes. Despite their initial differences, both concepts rely on metrics to generate similar input data instances. These metrics should be designed to align with the data's characteristics, especially when it is derived from causal structure and should reflect counterfactuals proximity. Previous attempts to define such metrics often lack general assumptions about data or structural causal models. In this research, we introduce a causal fair metric formulated based on causal structures that encompass sensitive attributes. For robustness analysis, the concept of protected causal perturbation is presented. Additionally, we delve into metric learning, proposing a method for metric estimation and deployment in real-world problems. The introduced metric has applications in the fields adversarial training, fair learning, algorithmic recourse, and causal reinforcement learning

Causal Adversarial Perturbations for Individual Fairness and Robustness in Heterogeneous Data Spaces

As responsible AI gains importance in machine learning algorithms, properties such as fairness, adversarial robustness, and causality have received considerable attention in recent years. However, despite their individual significance, there remains a critical gap in simultaneously exploring and integrating these properties. In this paper, we propose a novel approach that examines the relationship between individual fairness, adversarial robustness, and structural causal models in heterogeneous data spaces, particularly when dealing with discrete sensitive attributes. We use causal structural models and sensitive attributes to create a fair metric and apply it to measure semantic similarity among individuals. By introducing a novel causal adversarial perturbation and applying adversarial training, we create a new regularizer that combines individual fairness, causality, and robustness in the classifier. Our method is evaluated on both real-world and synthetic datasets, demonstrating its effectiveness in achieving an accurate classifier that simultaneously exhibits fairness, adversarial robustness, and causal awareness

Doxorubicin-induced renal inflammation in rats: Protective role of Plantago major

Objective: The aim of the present study was to evaluate the possible protective effect of Plantago major (P. major) extract against doxorubicin (DXR)-induced renal inflammation in rats. Materials and Methods: 80 male albino rats were randomly divided into 8 groups as follows: control, DXR, Ext (extract) 600, Ext1200, dexamethasone+DXR, vitamin E+DXR, Ext600+DXR, and Ext1200+DXR. Duration of the study was 35 days and DXR was intravenously injected on the 7th day of the experiment. Tumor necrosis factor-alpha (TNF-α) production and monocyte chemoattractant protein-1 (MCP-1) expression levels were assessed in the left kidney. Serum creatinine concentration and osmolarity were determined on the 1st, 14th, 21st, 28th and 35th days of the experiment. Results: DXR caused a significant increase in renal expression of MCP-1 and TNF-α production compared to control animals. Administration of dexamethasone, vitamin E and P. major extract significantly improved the expression of these inflammatory mediators compared to DXR group. Compared to day 1 in DXR group, serum osmolarity showed a significant increase on days 21, 28 and 35. Also, on these days, serum osmolarity in DXR group was significantly higher than that on the same days in control group. In Vit E+DXR and Ext 1200+DXR groups, there was no significant changes in serum osmolarity among different days of the study. However, in these groups, serum osmolarity on days 21, 28 and 35 showed a significant decrease compared to the same days in DXR group. Conclusion: Present results suggest that hydroethanolic extract of P. major protected renal tissue against DXR–induced renal inflammation