Anik Dey

Ira Ceka, Feitong Qiao, Anik Dey et al.

Despite their remarkable success, large language models (LLMs) have shown limited ability on safety-critical code tasks such as vulnerability detection. Typically, static analysis (SA) tools, like CodeQL, CodeGuru Security, etc., are used for vulnerability detection. SA relies on predefined, manually-crafted rules for flagging various vulnerabilities. Thus, effectiveness of SA in detecting vulnerabilities depends on human experts and is known to report high error rates. In this study we investigate whether LLM prompting can be an effective alternative to these static analyzers in the partial code setting. We propose prompting strategies that integrate natural language instructions of vulnerabilities with contrastive chain-of-thought reasoning, augmented using contrastive samples from a synthetic dataset. Our findings demonstrate that security-aware prompting techniques can be effective alternatives to the laborious, hand-crafted rules of static analyzers, which often result in high false negative rates in the partial code setting. When leveraging SOTA reasoning models such as DeepSeek-R1, each of our prompting strategies exceeds the static analyzer baseline, with the best strategies improving accuracy by as much as 31.6%, F1-scores by 71.7%, pairwise accuracies by 60.4%, and reducing FNR by as much as 37.6%.

Pascale Fung, Dario Bertero, Yan Wan et al.

Since the late 1990s when speech companies began providing their customer-service software in the market, people have gotten used to speaking to machines. As people interact more often with voice and gesture controlled machines, they expect the machines to recognize different emotions, and understand other high level communication features such as humor, sarcasm and intention. In order to make such communication possible, the machines need an empathy module in them which can extract emotions from human speech and behavior and can decide the correct response of the robot. Although research on empathetic robots is still in the early stage, we described our approach using signal processing techniques, sentiment analysis and machine learning algorithms to make robots that can "understand" human emotion. We propose Zara the Supergirl as a prototype system of empathetic robots. It is a software based virtual android, with an animated cartoon character to present itself on the screen. She will get "smarter" and more empathetic through its deep learning algorithms, and by gathering more data and learning from it. In this paper, we present our work so far in the areas of deep learning of emotion and sentiment recognition, as well as humor recognition. We hope to explore the future direction of android development and how it can help improve people's lives.