Opportunistic Reasoning for the Semantic Web: Adapting Reasoning to the Environment

Despite the efforts devoted so far, the Semantic Web vision appears to be an eluding target. We propose a paradigm shift for the Semantic Web centred around the pragmatics of developing Semantic Web applications in order to overcome the bootstrapping problem it suffers from. This paradigm is based on the vision of the Semantic Web as the result emerging from the integration and collaboration of a plethora of Semantic Web applications, rather that as a global entity. On the basis of this assumption we describe and propose Opportunistic Reasoning as a general purpose reasoning model suitable for the development of reasonably scalable Semantic Web applications

Decoupled Adversarial Contrastive Learning for Self-supervised Adversarial Robustness

Adversarial training (AT) for robust representation learning and self-supervised learning (SSL) for unsupervised representation learning are two active research fields. Integrating AT into SSL, multiple prior works have accomplished a highly significant yet challenging task: learning robust representation without labels. A widely used framework is adversarial contrastive learning which couples AT and SSL, and thus constitute a very complex optimization problem. Inspired by the divide-and-conquer philosophy, we conjecture that it might be simplified as well as improved by solving two sub-problems: non-robust SSL and pseudo-supervised AT. This motivation shifts the focus of the task from seeking an optimal integrating strategy for a coupled problem to finding sub-solutions for sub-problems. With this said, this work discards prior practices of directly introducing AT to SSL frameworks and proposed a two-stage framework termed Decoupled Adversarial Contrastive Learning (DeACL). Extensive experimental results demonstrate that our DeACL achieves SOTA self-supervised adversarial robustness while significantly reducing the training time, which validates its effectiveness and efficiency. Moreover, our DeACL constitutes a more explainable solution, and its success also bridges the gap with semi-supervised AT for exploiting unlabeled samples for robust representation learning. The code is publicly accessible at https://github.com/pantheon5100/DeACL.Comment: Accepted by ECCV 2022 oral presentatio

Connecting the Cytoskeleton to the Endoplasmic Reticulum and Golgi

A tendency in cell biology is to divide and conquer. For example, decades of painstaking work have led to an understanding of endoplasmic reticulum (ER) and Golgi structure, dynamics, and transport. In parallel, cytoskeletal researchers have revealed a fantastic diversity of structure and cellular function in both actin and microtubules. Increasingly, these areas overlap, necessitating an understanding of both organelle and cytoskeletal biology. This review addresses connections between the actin/microtubule cytoskeletons and organelles in animal cells, focusing on three key areas: ER structure and function; ER-to-Golgi transport; and Golgi structure and function. Making these connections has been challenging for several reasons: the small sizes and dynamic characteristics of some components; the fact that organelle-specific cytoskeletal elements can easily be obscured by more abundant cytoskeletal structures; and the difficulties in imaging membranes and cytoskeleton simultaneously, especially at the ultrastructural level. One major concept is that the cytoskeleton is frequently used to generate force for membrane movement, with two potential consequences: translocation of the organelle, or deformation of the organelle membrane. While initially discussing issues common to metazoan cells in general, we subsequently highlight specific features of neurons, since these highly polarized cells present unique challenges for organellar distribution and dynamics

An Empirical Study & Evaluation of Modern CAPTCHAs

For nearly two decades, CAPTCHAs have been widely used as a means of protection against bots. Throughout the years, as their use grew, techniques to defeat or bypass CAPTCHAs have continued to improve. Meanwhile, CAPTCHAs have also evolved in terms of sophistication and diversity, becoming increasingly difficult to solve for both bots (machines) and humans. Given this long-standing and still-ongoing arms race, it is critical to investigate how long it takes legitimate users to solve modern CAPTCHAs, and how they are perceived by those users. In this work, we explore CAPTCHAs in the wild by evaluating users' solving performance and perceptions of unmodified currently-deployed CAPTCHAs. We obtain this data through manual inspection of popular websites and user studies in which 1,400 participants collectively solved 14,000 CAPTCHAs. Results show significant differences between the most popular types of CAPTCHAs: surprisingly, solving time and user perception are not always correlated. We performed a comparative study to investigate the effect of experimental context -- specifically the difference between solving CAPTCHAs directly versus solving them as part of a more natural task, such as account creation. Whilst there were several potential confounding factors, our results show that experimental context could have an impact on this task, and must be taken into account in future CAPTCHA studies. Finally, we investigate CAPTCHA-induced user task abandonment by analyzing participants who start and do not complete the task.Comment: Accepted at USENIX Security 202