Xiruo Wang

Xiruo Wang, Xinyi Jiang, Ziqi Lyu

Generative AI has made visual storytelling widely accessible, yet current prompt-based interactions often force users into a trade-off between precise control and creative flow. We present One Kiss, a co-creative comic generation system that introduces "Affective Steering". Instead of writing text prompts, users guide the tone of their story through emoji inputs, whose semantic ambiguity becomes a resource rather than a limitation. Unlike traditional text-to-image tools that rely on explicit descriptions, One Kiss uses a dual-stream input in which users define structural pacing by sketching panel frames and set atmospheric tone by pairing keywords with emojis. This mechanism enables "Genre Flux," where emotional inputs accumulate across panels and gradually shift the genre of a story. A preliminary study (N = 6) suggests that this soft steering approach may reframe the user's role from prompt engineer to narrative director, with ambiguity serving as a source of creative surprise rather than a loss of control.

Xiruo Wang, Risto Miikkulainen

Malware detection have used machine learning to detect malware in programs. These applications take in raw or processed binary data to neural network models to classify as benign or malicious files. Even though this approach has proven effective against dynamic changes, such as encrypting, obfuscating and packing techniques, it is vulnerable to specific evasion attacks where that small changes in the input data cause misclassification at test time. This paper proposes a new approach: MDEA, an Adversarial Malware Detection model uses evolutionary optimization to create attack samples to make the network robust against evasion attacks. By retraining the model with the evolved malware samples, its performance improves a significant margin.