Selective real‐time adversarial perturbations against deep reinforcement learning agents

Abstract Recent work has shown that deep reinforcement learning (DRL) is vulnerable to adversarial attacks, so that exploiting vulnerabilities in DRL systems through adversarial attack techniques has become a necessary prerequisite for building robust DRL systems. Compared to traditional deep learning systems, DRL systems are characterised by long sequential decisions rather than one‐step decision, so attackers must perform multi‐step attacks on them. To successfully attack a DRL system, the number of attacks must be minimised to avoid detecting by the victim agent and to ensure the effectiveness of the attack. Some selective attack methods proposed in recent researches, that is, attacking an agent at partial time steps, are not applicable to real‐time attack scenarios, although they can avoid detecting by the victim agent. A real‐time selective attack method that is applicable to environments with discrete action spaces is proposed. Firstly, the optimal attack threshold T for performing selective attacks in the environment Env is determined. Then, the observation states corresponding to when the value of the action preference function of the victim agent in multiple eposides exceeds the threshold T are added to the training set according to this threshold. Finally, a universal perturbation is generated based on this training set, and it is used to perform real‐time selective attacks on the victim agent. Comparative experiments show that our attack method can perform real‐time attacks while maintaining the attack effect and stealthiness

Support for Situation-Awareness in Trustworthy Ubiquitous Computing Application Software

Due to the dynamic and ephemeral nature of ubiquitous computing (ubicomp) environments, it is especially important that the application software in ubicomp environments is trustworthy. In order to have trustworthy application software in ubicomp environments, situation-awareness (SAW) in the application software is needed for enforcing flexible security policies and detecting violations of security policies. In this paper, an approach is presented to providing development and runtime support for incorporating SAW in trustworthy ubicomp application software. The development support is to provide SAW requirement specification and automated code generation for achieving SAW in trustworthy ubicomp application software, and the runtime support is for context acquisition, situation analysis, and situation-aware communication. To realize our approach, the improved Reconfigurable Context-Sensitive Middleware (RCSM) is developed for providing the above development and runtime support. Keywords: Trustworthy ubiquitous application software, situation-awareness, Situation-Aware Interface Definition Language (SA-IDL), Situation-Aware (SA) middleware, SA security policies, development and runtime support

Human mesenchymal stem cells in the tumour microenvironment promote ovarian cancer progression: the role of platelet-activating factor

Abstract Background The tumour microenvironment conferred by mesenchymal stem cells (MSCs) plays a key role in tumour development and progression. We previously determined that platelet-activating factor receptor (PAFR) was overexpressed in ovarian cancer cells (OCCs) and that PAF can promote ovarian cancer progression via PAF/PAFR-mediated inflammatory signalling pathways. Evidence suggests that MSCs can secrete high concentrations of PAF. Here, we investigated the role of PAF/PAFR signalling in the microenvironment mediated by MSCs and OCCs and its effect on cancer progression. Methods The PAF concentrations in the culture media of MSCs, OCCs and co-cultured MSCs and OCCs were determined by ELISA. The effects of MSCs on OCCs in vitro were assessed on cells treated with conditioned medium (CM). The expression and phosphorylation of key proteins in the PAF/PAFR signalling pathway were evaluated. In vivo, MSCs/RFP and SKOV3 cells were co-administered at different proportions to nude mice by interscapular injection. Mice in the WEB2086 group were intraperitoneally injected with the PAFR antagonist WEB2086 at a dose of 1 mg/kg.d for the duration of the animal experiments. Tumour progression was observed, and the weight and survival time of mice were measured. The PAF concentration in peripheral and tumour site blood was determined by ELISA. Results High concentrations of PAF were detected in CM from MSCs and MSCs co-cultured with OCCs. Both types of medium promoted non-mucinous OCC proliferation and migration but had no effect on mucinous-type OCCs. These effects could be blocked by PAFR inhibitors. The expression and phosphorylation of key proteins in the PAF/PAFR pathway significantly increased upon treatment with PAF and MSC-CM. In vivo, the tumour volume was larger following co-injection of SKOV3 cells and MSCs/RFP than following injection of SKOV3 cells alone. The tumour-promoting effect of MSCs/RFP was blocked by the PAFR antagonist WEB2086. Serum PAF concentrations significantly increased in co-injected mice. Conclusion Our results suggest that the tumour-promoting effect of MSCs on OCCs via their cross-talk in the tumour microenvironment was, at least in part, mediated by the PAF/PAFR pathway, suggesting a new target for the treatment of ovarian cancer

Predicting the category of fire department operations

Voluntary fire departments have limited human and material resources. Machine learning aided prediction of fire department operation details can benefit their resource planning and distribution. While there is previous work on predicting certain aspects of operations within a given operation category, operation categories themselves have not been predicted yet. In this paper we propose an approach to fire department operation category prediction based on location, time, and weather information, and compare the performance of multiple machine learning models with cross validation. To evaluate our approach, we use two years of fire department data from Upper Austria, featuring 16.827 individual operations, and predict its major three operation categories. Preliminary results indicate a prediction accuracy of 61%. While this performance is already noticeably better than uninformed prediction (34% accuracy), we intend to further reduce the prediction error utilizing more sophisticated features and models.Peer reviewe