Adversarial Data Augmentation for HMM-based Anomaly Detection

In this work, we concentrate on the detection of anomalous behaviors in systems operating in the physical world and for which it is usually not possible to have a complete set of all possible anomalies in advance. We present a data augmentation and retraining approach based on adversarial learning for improving anomaly detection. In particular, we first define a method for gener- ating adversarial examples for anomaly detectors based on Hidden Markov Models (HMMs). Then, we present a data augmentation and retraining technique that uses these adversarial examples to improve anomaly detection performance. Finally, we evaluate our adversarial data augmentation and retraining approach on four datasets showing that it achieves a statistically significant perfor- mance improvement and enhances the robustness to adversarial attacks. Key differences from the state-of-the-art on adversarial data augmentation are the focus on multivariate time series (as opposed to images), the context of one-class classification (in contrast to standard multi-class classification), and the use of HMMs (in contrast to neural networks)

HMMs for Anomaly Detection in Autonomous Robots

Detection of anomalies and faults is a key element for long-term robot autonomy, because, together with subsequent diagnosis and recovery, allows to reach the required levels of robustness and persistency. In this paper, we propose an approach for detecting anomalous behaviors in autonomous robots starting from data collected during their routine operations. The main idea is to model the nominal (expected) behavior of a robot system using Hidden Markov Models (HMMs) and to evaluate how far the observed behavior is from the nominal one using variants of the Hellinger distance adopted for our purposes. We present a method for online anomaly detection that computes the Hellinger distance between the probability distribution of observations made in a sliding window and the corresponding nominal emission probability distribution. We also present a method for o!ine anomaly detection that computes a variant of the Hellinger distance between two HMMs representing nominal and observed behaviors. The use of the Hellinger distance positively impacts on both detection performance and interpretability of detected anomalies, as shown by results of experiments performed in two real-world application domains, namely, water monitoring with aquatic drones and socially assistive robots for elders living at home. In particular, our approach improves by 6% the area under the ROC curve of standard online anomaly detection methods. The capabilities of our o!ine method to discriminate anomalous behaviors in real-world applications are statistically proved

To explore or to exploit? Learning humans' behaviour to maximize interactions with them

Assume a robot operating in a public space (e.g., a library, a museum) and serving visitors as a companion, a guide or an information stand. To do that, the robot has to interact with humans, which presumes that it actively searches for humans in order to interact with them. This paper addresses the problem how to plan robot's actions in order to maximize the number of such interactions in the case human behavior is not known in advance. We formulate this problem as the exploration/exploitation problem and design several strategies for the robot. The main contribution of the paper than lies in evaluation and comparison of the designed strategies on two datasets. The evaluation shows interesting properties of the strategies, which are discussed

Measuring Progress in Robotics: Benchmarking and the ‘Measure-Target Confusion’

While it is often said that in order to qualify as a true science robotics should aspire to reproducible and measurable results that allow benchmarking, I argue that a focus on benchmarking will be a hindrance for progress. Several academic disciplines that have been led into pursuing only reproducible and measurable ‘scientific’ results—robotics should be careful not to fall into that trap. Results that can be benchmarked must be specific and context-dependent, but robotics targets whole complex systems independently of a specific context—so working towards progress on the technical measure risks missing that target. It would constitute aiming for the measure rather than the target: what I call ‘measure-target confusion’. The role of benchmarking in robotics shows that the more general problem to measure progress towards more intelligent machines will not be solved by technical benchmarks; we need a balanced approach with technical benchmarks, real-life testing and qualitative judgment

Assessment and management of iatrogenic withdrawal syndrome and delirium in pediatric intensive care units across Europe: an ESPNIC survey.

Analgesia and sedation are essential for the care of children in the pediatric intensive care unit (PICU); however, when prolonged, they may be associated with iatrogenic withdrawal syndrome (IWS) and delirium. We sought to evaluate current practices on IWS and delirium assessment and management (including non-pharmacologic strategies as early mobilization), and to investigate associations between presence of an analgosedation protocol and IWS and delirium monitoring, analgosedation weaning, and early mobilization. A multicenter cross-sectional survey-based study collecting data from one experienced physician or nurse per PICU in Europe was conducted from January to April 2021. We then investigated differences among PICUs that did or did not follow an analgosedation protocol. Among 357 PICUs, 215 (60%) responded across 27 countries. IWS was systematically monitored with a validated scale in 62% of PICUs, mostly using the Withdrawal Assessment Tool-1 (53%). Main first-line treatment for IWS was a rescue bolus with interruption of weaning (41%). Delirium was systematically monitored in 58% of PICUs, mostly with the Cornell Assessment of Pediatric Delirium scale (48%) and the Sophia Observation Scale for Pediatric Delirium (34%). Main reported first-line treatment for delirium was dexmedetomidine (45%) or antipsychotic drugs (40%). Seventy-one percent of PICUs reported to follow an analgosedation protocol. Multivariate analyses adjusted for PICU characteristics showed that PICUs using a protocol were significantly more likely to systematically monitor IWS (Odds Ratio [OR ]1.92, 95% Confidence Interval [CI] 1.01-3.67) and delirium (OR 2.00, 95% CI 1.07-3.72), use a protocol for analgosedation weaning (OR 6.38, 95% CI 3.20-12.71), and promote mobilization (OR 3.38, 95% CI 1.63-7.03). Monitoring and management of IWS and delirium are highly variable among European PICUs. The use of an analgosedation protocol was associated with increased likelihood of monitoring IWS and delirium, performing a structured analgosedation weaning, and promoting mobilization. Education on this topic and interprofessional collaborations are highly needed to help reduce the burden of analgosedation-associated adverse outcomes

A novel aromatic oil compound inhibits microbial overgrowth on feet: a case study

<p>Abstract</p> <p>Background</p> <p>Athlete's Foot (Tinea pedis) is a form of ringworm associated with highly contagious yeast-fungi colonies, although they look like bacteria. Foot bacteria overgrowth produces a harmless pungent odor, however, uncontrolled proliferation of yeast-fungi produces small vesicles, fissures, scaling, and maceration with eroded areas between the toes and the plantar surface of the foot, resulting in intense itching, blisters, and cracking. Painful microbial foot infection may prevent athletic participation. Keeping the feet clean and dry with the toenails trimmed reduces the incidence of skin disease of the feet. Wearing sandals in locker and shower rooms prevents intimate contact with the infecting organisms and alleviates most foot-sensitive infections. Enclosing feet in socks and shoes generates a moisture-rich environment that stimulates overgrowth of pungent both aerobic bacteria and infectious yeast-fungi. Suppression of microbial growth may be accomplished by exposing the feet to air to enhance evaporation to reduce moistures' growth-stimulating effect and is often neglected. There is an association between yeast-fungi overgrowths and disabling foot infections. Potent agents virtually exterminate some microbial growth, but the inevitable presence of infection under the nails predicts future infection. Topical antibiotics present a potent approach with the ideal agent being one that removes moisture producing antibacterial-antifungal activity. Severe infection may require costly prescription drugs, salves, and repeated treatment.</p> <p>Methods</p> <p>A 63-y female volunteered to enclose feet in shoes and socks for 48 hours. Aerobic bacteria and yeast-fungi counts were determined by swab sample incubation technique (1) after 48-hours feet enclosure, (2) after washing feet, and (3) after 8-hours socks-shoes exposure to a aromatic oil powder-compound consisting of <it>arrowroot, baking soda, basil oil, tea tree oil, sage oil, and clove oil</it>.</p> <p>Conclusion</p> <p>Application of this novel compound to the external surfaces of feet completely inhibited both aerobic bacteria and yeast-fungi-mold proliferation for 8-hours in spite of being in an enclosed environment compatible to microbial proliferation. Whether topical application of this compound prevents microbial infections in larger populations is not known. This calls for more research collected from subjects exposed to elements that may increase the risk of microbial-induced foot diseases.</p