Recent work has investigated adaptive filters, which are filters that change their internal representation in response to queries that yield false positives. These include: (1) strongly adaptive filters, which guarantee a false-positive probability of at most ɛ for any query regardless of the history of prior queries, i.e., against adaptive adversaries, (2) support-optimal filters, which guarantee an average false-positive probability of at most ɛ over sufficiently large query sequences, when the adversary is oblivious, (3) other adaptive filters that change their representation and empirically perform better, but do not come with any specific provable guarantees beyond static filters. In this article, we investigate the performance advantages that strongly adaptive filters offer on (non-adversarial) skewed query distributions, which are common in database applications. In our theoretical and experimental results, we model query distribution skewness with the Zipfian distribution with parameter z . We consider two strongly adaptive filters: the broom filter and the telescoping adaptive filter (TAF). We also consider two adaptive (but not strongly adaptive) filters: the adaptive cuckoo filter (ACF), and a non-adaptive rank-and-select quotient filter augmented with a cache of recent false positives, which we call the cache-augmented filter (CAF). We prove upper bounds on the false-positive rates of the broom filter, the TAF, and the CAF as a function of the Zipfian parameter z as the length of the query sequence tends to infinity. We provide an implementation of the broom filter, based on the (non-adaptive) rank-and-select quotient filter. We validate the above bounds experimentally on synthetic Zipfian query sequences on the broom filter, the TAF, and the CAF. Finally, we measure the observed false-positive rate of the broom filter, the TAF, the CAF, and the ACF on highly skewed real-world network trace data. We find that all adaptive filters achieved 1-2 orders of magnitude lower false-positive rates than non-adaptive filters. We further find that the broom filter and the TAF outperform the CAF only when the ratio of distinct negative queries to positive set size is high; otherwise, the CAF and the strongly adaptive filters yield similar false-positive rates.

Cerebral palsy (CP) affects millions globally, many of whom rely on technology to support daily living and communication.Yet, there is a notable absence of formal autoethnographic research exploring the lived experiences of people with CP, particularly those who use augmentative and alternative communication (AAC) devices.This paper presents a longitudinal autoethnographic study of Tianchi, an international PhD student with CP as he navigated the final stages of his doctoral journey.Using a structured, questionnairebased diary method, we captured daily reflections on his everyday life, use of assistive technologies and communication experiences.Thematic analysis of the data revealed three key findings: Tianchi demonstrates resilience through technical proficiency and creative adaptations of mainstream technologies; the hidden labour of communication challenges complicate his access to community support and his reliance on both care and technology exposes fragile interdependencies.We further discuss: the importance of reducing input exertion for people with CP, the high costs of being heard and recognize the unanticipated benefits of non-assistive technologies.Ultimately, this study underscores the need for more participatory, first-person research approaches with communities living with CP.

Identifying users' similarity is a very important researching point because its result can be applied to many application systems. In social networks, the user circles are built not only based on their relationships in real-life, but also on common interests. Some existing approaches cannot fully capture users' similarity from the perspective of their common interests, while some other approaches are too time-consuming or space-consuming. In this paper, we propose a method of identifying users' interest similarity based on clustering Hot Vertices (HotV). A hot vertex in a social network is an account which has a large number of fans. The approach extracts users' common interests by mining and clustering the hot vertices that the two users are following simultaneously. Both the experiment and theoretical analysis have proved that the proposed approach makes a significant improvement on the precision of similarity measuring with a relatively low time and space complexity.