eXplainable Modeling (XM): Data Analysis for Intelligent Agents

Intelligent agents perform key tasks in several application domains by processing sensor data and taking actions that maximize reward functions based on internal models of the environment and the agent itself. In this paper we present eXplainable Modeling (XM), a Python software which supports data analysis for intelligent agents. XM enables to analyze state-models, namely models of the agent states, discovered from sensor traces by data-driven methods, and to interpret them for improved situation awareness. The main features of the tool are described through the analysis of a real case study concerning aquatic drones for water monitoring

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)

Multivariate sensor signals collected by aquatic drones involved in water monitoring: A complete dataset

Sensor data generated by intelligent systems, such as autonomous robots, smart buildings and other systems based on artificial intelligence, represent valuable sources of knowledge in today's data-driven society, since they contain information about the situations these systems face during their operation. These data are usually multivariate time series since modern technologies enable the simultaneous acquisition of multiple signals during long periods of time. In this paper we present a dataset containing sensor traces of six data acquisition campaigns performed by autonomous aquatic drones involved in water monitoring. A total of 5.6 h of navigation are available, with data coming from both lakes and rivers, and from different locations in Italy and Spain. The monitored variables concern both the internal state of the drone (e.g., battery voltage, GPS position and signals to propellers) and the state of the water (e.g., temperature, dissolved oxygen and electrical conductivity). Data were collected in the context of the EU-funded Horizon 2020 project INTCATCH (http://www.intcatch.eu) which aims to develop a new paradigm for monitoring water quality of catchments. The aquatic drones used for data acquisition are Platypus Lutra boats. Both autonomous and manual drive is used in different parts of the navigation. The dataset is analyzed in the paper “Time series segmentation for state-model generation of autonomous aquatic drones: A systematic framework” [1] by means of recent time series clustering/segmentation techniques to extract data-driven models of the situations faced by the drones in the data acquisition campaigns. These data have strong potential for reuse in other kinds of data analysis and evaluation of machine learning methods on real-world datasets [2]. Moreover, we consider this dataset valuable also for the variety of situations faced by the drone, from which machine learning techniques can learn behavioral patterns or detect anomalous activities. We also provide manual labeling for some known states of the drones, such as, drone inside/outside the water, upstream/downstream navigation, manual/autonomous drive, and drone turning, that represent a ground truth for validation purposes. Finally, the real-world nature of the dataset makes it more challenging for machine learning methods because it contains noisy samples collected while the drone was exposed to atmospheric agents and uncertain water flow conditions

Interpersonal sensitivity in the at-risk mental state for psychosis

Background Interpersonal sensitivity is a personality trait described as excessive awareness of both the behaviour and feelings of others. Although interpersonal sensitivity has been found to be one of the vulnerability factors to depression, there has been little interest in its relationship with the prodromal phase of psychosis. The aims of this study were to examine the level of interpersonal sensitivity in a sample of individuals with an at-risk mental state (ARMS) for psychosis and its relationship with other psychopathological features. Method Method. Sixty-two individuals with an ARMS for psychosis and 39 control participants completed a series of self-report questionnaires, including the Interpersonal Sensitivity Measure (IPSM), the Prodromal Questionnaire (PQ), the Ways of Coping Questionnaire (WCQ) and the Depression and Anxiety Stress Scale (DASS). Results Individuals with an ARMS reported higher interpersonal sensitivity compared to controls. Associations between interpersonal sensitivity, positive psychotic symptoms (i.e. paranoid ideation), avoidant coping and symptoms of depression, anxiety and stress were also found. Conclusions This study suggests that being 'hypersensitive' to interpersonal interactions is a psychological feature of the putatively prodromal phase of psychosis. The relationship between interpersonal sensitivity, attenuated positive psychotic symptoms, avoidant coping and negative emotional states may contribute to long-term deficits in social functioning. We illustrate the importance, when assessing a young client with a possible ARMS, of examining more subtle and subjective symptoms in addition to attenuated positive symptoms. © 2012 Cambridge University Press

Proper Versus Improper Mixtures: Towards a Quaternionic Quantum Mechanics

The density operators obtained by taking partial traces do not represent proper mixtures of the subsystems of a compound physical system, but improper mixtures, since the coefficients in the convex sums expressing them never bear the ignorance interpretation. As a consequence, assigning states to these subsystems is problematical in standard quantum mechanics (subentity problem). Basing on the proposal provided in the SR interpretation of quantum mechanics, where improper mixtures are considered as true nonpure states conceptually distinct from proper mixtures, we show here that proper and improper mixtures can be represented by different density operators in the quaternionic formulation of quantum mechanics, hence they can be distinguished also from a mathematical viewpoint. A simple example related to the quantum theory of measurement is provided.Comment: 10 pages, standard latex, accepted for publication in Theoretical and Mathematical Physic

Using virtual reality to investigate psychological processes and mechanisms associated with the onset and maintenance of psychosis: a systematic review

PURPOSE: In the last decade researchers have embraced virtual reality to explore the psychological processes and mechanisms that are involved in the onset and maintenance of psychosis. A systematic review was conducted to synthesise the evidence of using virtual reality to investigate these mechanisms. METHODS: Web of Science, PsycINFO, Embase, and Medline were searched. Reference lists of collected papers were also visually inspected to locate any relevant cited journal articles. In total 6001 articles were potentially eligible for inclusion; of these, 16 studies were included in the review. RESULTS: The review identified studies investigating the effect of interpersonal sensitivity, childhood bullying victimisation, physical assault, perceived ethnic discrimination, social defeat, population density and ethnic density on the real-time appraisal of VR social situations. Further studies demonstrated the potential of VR to investigate paranoid ideation, anomalous experiences, self-confidence, self-comparison, physiological activation and behavioural response. CONCLUSIONS: The reviewed studies suggest that VR can be used to investigate psychological processes and mechanisms associated with psychosis. Implications for further experimental research, as well as for assessment and clinical practise are discussed. The present review has been registered in the PROSPERO register: CRD42016038085