Global Triggers for Attacking and Analyzing Ranking Models

Text ranking models based on BERT are now well established for a wide range of pas- sage and document ranking tasks. However, the robustness of BERT-based ranking models under adversarial attack is under-explored. In this work, we argue that BERT- rankers are vulnerable to adversarial attacks targeting retrieved documents given a query. We propose algorithms for generating adversarial perturbation of documents locally to individual queries or globally across the dataset using gradient-based optimization methods. The aim of our algorithms is to add a small number of tokens to a highly relevant or non-relevant document to cause a significant rank demotion or promotion. Our experiments show that a few number of tokens can already change the document rank by a large margin. Besides, we find that BERT-rankers heavily rely on the docu- ment start/head for relevance prediction, making the initial part of the document more susceptible to adversarial attacks. More interestingly, our statistical analysis finds a small set of recurring adversar- ial tokens that when concatenated to documents result in successful rank demo- tion/promotion of any relevant/non-relevant document respectively. Finally, our ad- versarial tokens also show particular topic preferences within and across datasets, exposing potential biases from BERT pre-training or downstream datasets

The Zeal Shortage

Although the duty of zealous advocacy enjoys nominal approval in most state bar rules and the secondary literature, today the majority of writings about zeal in the practice of law present zeal in a negative light. Critics use this word to object to lawyers\u27 dishonesty, hyperpartisanship, aggressive or confrontational work styles, rudeness, and disregard for the interests of adversaries, the courts, and the public. This article, part of a Hofstra University symposium, builds on the literature that praises zealous advocacy (much of it written by symposium honoree Monroe Freedman) to identify a shortage of zeal in American legal practice and identifies legal education as a culprit. Arguing that new rules of professional responsibility could enhance the supply of zealous advocacy, the article endorses the Massachusetts variation on Model Rule 1.3, and presents a new Model Rule 1.18(e) with comments

Coercive Assimilationism: The Perils of Muslim Women\u27s Identity Performance in the Workplace

Should employees have the legal right to “be themselves” at work? Most Americans would answer in the negative because work is a privilege, not an entitlement. But what if being oneself entails behaviors, mannerisms, and values integrally linked to the employee’s gender, race, or religion? And what if the basis for the employer’s workplace rules and professionalism standards rely on negative racial, ethnic or gender stereotypes that disparately impact some employees over others? Currently, Title VII fails to take into account such forms of second-generation discrimination, thereby limiting statutory protections to phenotypical or morphological bases. Drawing on social psychology and antidiscrimination literature, this Article argues that in order for courts to keep up with discrimination they should expansively interpret Title VII to address identity-based discrimination rooted in negative implicit stereotypes of low status groups. In doing so, the Article brings to the forefront Muslim women’s identity performance at the intersection of religion, race, gender, and ethnicity—a topic marginalized in the performativity literature. I argue that Muslim female employees at the intersection of conflicting stereotypes and contradictory identity performance pressures associated with gender, race, and religion are caught in a triple bind that leaves them worse off irrespective of their efforts to accommodate or reject coercive assimilationism at work

Coercive Assimilationism: The Perils of Muslim Women\u27s Identity Performance in the Workplace

Should employees have the legal right to be themselves at work? Most Americans would answer in the negative because work is a privilege, not an entitlement. But what if being oneself entails behaviors, mannerisms, and values integrally linked to the employee\u27s gender, race, or religion? And what if the basis for the employer\u27s workplace rules and professionalism standards rely on negative racial, ethnic or gender stereotypes that disparately impact some employees over others? Currently, Title VII fails to take into account such forms of second-generation discrimination, thereby limiting statutory protections to phenotypical or morphological bases. Drawing on social psychology and antidiscrimination literature, this Article argues that in order for courts to keep up with discrimination they should expansively interpret Title VII to address identity-based discrimination rooted in negative implicit stereotypes of low status groups. In doing so, the Article brings to the forefront Muslim women\u27s identity performance at the intersection of religion, race, gender, and ethnicity--a topic marginalized in the performativity literature. I argue that Muslim female employees at the intersection of conflicting stereotypes and contradictory identity performance pressures associated with gender, race, and religion are caught in a triple bind that leaves them worse off irrespective of their efforts to accommodate or reject coercive assimilationism at work

Flow Table Management in Programmable Network Data Planes

The design-space of network devices is constantly evolving, driven by the continual demand for increased global inter-connectivity, intelligent orchestration, and distributed computation between cloud and edge resources. Modern businesses are increasingly reliant on a connected world for a competitive advantage as well as essential operations. Meanwhile, there is an increasing number of attacks on critical communication infrastructure from a variety of malicious actors. Thus, there is an increasing urgency to improve all aspects of security in data communication networks. Additionally, Software-Defined Networking (SDN) has increasingly gained traction and utility across data centers and network administration. SDN concepts enable increased flexibility for network operators, including the ability to implement a broad class of custom network functions for real-time diagnostics as well as traffic management. While SDN has notable advantages over traditional network appliances, current implementations are often more susceptible to malicious attacks due to increased complexity and abstractions imposed on packet classification and table management. This dissertation investigates architectural techniques to improve the reliability and performance of data plane processing hardware. Our techniques are applicable to both traditional packet processing as well as SDN data plane architectures. The contributions of this research include two novel and complementary techniques to improve data plane performance through optimizing the use of available packet classification resources. By leveraging storage-efficient stochastic data structures and machine learning inspired replacement policies, our techniques improve data plane processing efficiency and predictability. The first technique leverages a Bloom Filter to prioritize established traffic and prevent malicious starvation of expensive packet classification resources. This Pre-Classification technique is general enough to be considered for any classification pipeline with non-uniform processing requirements. The second technique, originally developed for speculative microprocessors, adapts the Hashed Perceptron binary classifier to flow table cache management. The proposed Flow Correlator mechanism leverages the Hashed Perceptron to correlate flow activity with temporal patterns and transport/network layer hints. This technique demonstrates the viability of associating temporal patterns to network flows enabling improvements in flow table cache management. Amenable to hardware implementations, both Flow Correlator and Pre-Classification techniques show promise in improving the reliability and performance of flow-centric packet processing architectures