Live GPU Forensics: The Process of Recovering Video Frames from NVIDIA GPU

The purpose of this research is to apply a graphics processing unit (GPU) forensics method to recover video artifacts from NVIDIA GPU. The tested video specs are 512 x 512 in resolution for video 1 and 800 x 600 in resolution for video 2. Both videos are mpeg4 video codec. A VLC player was used in the experiment. A special program has been developed using OpenCL to recover 1) patterns that are frames consist of pixel values and 2) dump data from the GPU global memory. The dump data that represent the video frame were located using simple steps. The recovery process was successful. For 512 x 512 resolution video, the frames were partially recovered but it shows enough information for the forensics investigator to determine what was viewed last. The research indicates that it is harder, but not impossible, to obtain a viewable frame from higher-resolution vide

Managing Controlled Unclassified Information in Research Institutions

In order to operate in a regulated world, researchers need to ensure compliance with ever-evolving landscape of information security regulations and best practices. This work explains the concept of Controlled Unclassified Information (CUI) and the challenges it brings to the research institutions. Survey from the user perceptions showed that most researchers and IT administrators lack a good understanding of CUI and how it is related to other regulations, such as HIPAA, ITAR, GLBA, and FERPA. A managed research ecosystem is introduced in this work. The workflow of this efficient and cost effective framework is elaborated to demonstrate how controlled research data are processed to be compliant with one of the highest level of cybersecurity in a campus environment. Issues beyond the framework itself is also discussed. The framework serves as a reference model for other institutions to support CUI research. The awareness and training program developed from this work will be shared with other institutions to build a bigger CUI ecosystem

Husformer: A Multi-Modal Transformer for Multi-Modal Human State Recognition

Human state recognition is a critical topic with pervasive and important applications in human-machine systems.Multi-modal fusion, the combination of metrics from multiple data sources, has been shown as a sound method for improving the recognition performance. However, while promising results have been reported by recent multi-modal-based models, they generally fail to leverage the sophisticated fusion strategies that would model sufficient cross-modal interactions when producing the fusion representation; instead, current methods rely on lengthy and inconsistent data preprocessing and feature crafting. To address this limitation, we propose an end-to-end multi-modal transformer framework for multi-modal human state recognition called Husformer.Specifically, we propose to use cross-modal transformers, which inspire one modality to reinforce itself through directly attending to latent relevance revealed in other modalities, to fuse different modalities while ensuring sufficient awareness of the cross-modal interactions introduced. Subsequently, we utilize a self-attention transformer to further prioritize contextual information in the fusion representation. Using two such attention mechanisms enables effective and adaptive adjustments to noise and interruptions in multi-modal signals during the fusion process and in relation to high-level features. Extensive experiments on two human emotion corpora (DEAP and WESAD) and two cognitive workload datasets (MOCAS and CogLoad) demonstrate that in the recognition of human state, our Husformer outperforms both state-of-the-art multi-modal baselines and the use of a single modality by a large margin, especially when dealing with raw multi-modal signals. We also conducted an ablation study to show the benefits of each component in Husformer