The worst-case resource usage of a program can provide useful information for many software-engineering tasks, such as performance optimization and algorithmic-complexity-vulnerability discovery. This paper presents a generic, adaptive, and sound fuzzing framework, called DSE-SMC, for estimating worst-case resource usage. DSE-SMC is generic because it is black-box as long as the user provides an interface for retrieving resource-usage information on a given input; adaptive because it automatically balances between exploration and exploitation of candidate inputs; and sound because it is guaranteed to converge to the true resource-usage distribution of the analyzed program. DSE-SMC is built upon a key observation: resource accumulation in a program is isomorphic to the soft-conditioning mechanism in Bayesian probabilistic programming; thus, worst-case resource analysis is isomorphic to the maximum-a-posteriori-estimation problem of Bayesian statistics. DSE-SMC incorporates sequential Monte Carlo (SMC) -- a generic framework for Bayesian inference -- with adaptive evolutionary fuzzing algorithms, in a sound manner, i.e., DSE-SMC asymptotically converges to the posterior distribution induced by resource-usage behavior of the analyzed program. Experimental evaluation on Java applications demonstrates that DSE-SMC is significantly more effective than existing black-box fuzzing methods for worst-case analysis.

While in real life everyone behaves themselves at least to some extent, it is much more difficult to expect people to behave themselves on the internet, because there are few checks or consequences for posting something toxic to others. Yet, for people on the other side, toxic texts often lead to serious psychological consequences. Detecting such toxic texts is challenging. In this paper, we attempt to build a toxicity detector using machine learning methods including CNN, Naive Bayes model, as well as LSTM. While there has been numerous groundwork laid by others, we aim to build models that provide higher accuracy than the predecessors. We produced very high accuracy models using LSTM and CNN, and compared them to the go-to solutions in language processing, the Naive Bayes model. A word embedding approach is also applied to empower the accuracy of our models.