Benchmark for Security Testing on Embedded Systems

With the growing popularity of the Internet of Things (IoT), embedded devices continue to integrate more into our daily lives. For this reason, security for embedded devices is a vital issue to address. Attacks such as stack smashing, code injection, data corruption and Return Oriented Programming (ROP) are still a threat to embedded systems. As new methods are developed to defend embedded systems against such attacks, a benchmark to compare these methods is not present. In this work, a benchmark is presented that is aimed at testing the security of new techniques that defend against these common attacks. Two programs are developed that carry three key values needed for a benchmark: realistic embedded application, complex control flow, and being deterministic. The first application is a pin lock system and the second is a compression data logger. A complexity evaluation of the two applications revealed that the pin lock system contained 171 functions and 190 nodes with 252 edges in the control-flow graph, and the compression data logger contained 192 functions and 1,357 nodes with 2,123 edges in the control-flow graph. The current benchmark will be improved in the future by adding more applications with a wider range of complexity

Spatiotemporal Modeling of Multivariate Signals With Graph Neural Networks and Structured State Space Models

Multivariate signals are prevalent in various domains, such as healthcare, transportation systems, and space sciences. Modeling spatiotemporal dependencies in multivariate signals is challenging due to (1) long-range temporal dependencies and (2) complex spatial correlations between sensors. To address these challenges, we propose representing multivariate signals as graphs and introduce GraphS4mer, a general graph neural network (GNN) architecture that captures both spatial and temporal dependencies in multivariate signals. Specifically, (1) we leverage Structured State Spaces model (S4), a state-of-the-art sequence model, to capture long-term temporal dependencies and (2) we propose a graph structure learning layer in GraphS4mer to learn dynamically evolving graph structures in the data. We evaluate our proposed model on three distinct tasks and show that GraphS4mer consistently improves over existing models, including (1) seizure detection from electroencephalography signals, outperforming a previous GNN with self-supervised pretraining by 3.1 points in AUROC; (2) sleep staging from polysomnography signals, a 4.1 points improvement in macro-F1 score compared to existing sleep staging models; and (3) traffic forecasting, reducing MAE by 8.8% compared to existing GNNs and by 1.4% compared to Transformer-based models

Chemistry and biochemistry of Terpenoids from Curcumaand related species

Several curcuminoids have been identified from rhizome of the common spice Curcuma longa (Zingaberaceae) and related plant species. Curcuminoids are known to display several pharmacological properties summed up in numerous papers and reviews. In addition to curcuminoids, more than 250 mono-, sesqui- di-, and triterpenoids have been identified from curcuma species. These lipophilic compounds have better absorption than curcuminoids and also exhibit a wide spectrum of pharmacological properties. Little attention has been paid to these lipophilic compounds, which may be as physiologically active, if not more, as curcuminoids. This review focuses on Curcuma terpenoids and their physiological properties