9,743 research outputs found
Implementing the AIFMD: Success or failure? ECMI Commentary No. 34, 28 March 2013
No full textThis commentary considers the implementation of the Alternative Investment Fund Managers Directive (AIFMD) by the European Commission. The AIFMD creates an internal market for asset management and as an endeavour to develop market-based finance is an important piece of legislation for the European economy. The author, Mirzha de Manuel Aramendía, considers the implementation of some of the provisions that raised concern among industry participants. He finds that, on balance, a practical and flexible approach to implementation has been followed that should help secure the success of the framework, which at present is still uncertain. The commentary also considers the remuneration guidelines adopted recently by the European Securities and Markets Authority (ESMA). It encourages EU and national authorities to commit to the success of the AIFMD framework, as part of a broader effort to develop capital markets and reduce the historical reliance of the European economy on bank finance
The (Not) Far-Away Path to Smart Cyber-Physical Systems: An Information-Centric Framework
No full textAdvances in embedded systems and communication technologies- A long with the availability of low-cost sensors-have led to a pervasive presence of cyber-physical systems. However, several intelligent functionalities that are necessary to meet user and application demands are missing from current solutions. A homogeneous and integrated framework supporting intelligent mechanisms can fill this gap
An Ensemble Approach for Cognitive Fault Detection and Isolation in Sensor Networks
No full textCognitive fault detection and diagnosis systems are systems able to provide timely information about possibly occurring faults without requiring any a priori knowledge about the process generating the data or the possible faults. This ability is crucial in sensor network scenarios where a priori information about the data generating process, the noise level or the dictionary of the possibly occurring faults is generally hard to obtain. We here present a novel cognitive fault detection and isolation system for sensor networks. The proposed solution relies on the modeling of spatial and temporal relationships present in the acquired datastreams and an ensemble of Hidden Markov Model change-detection tests working in the space of estimated parameters for fault detection and isolation purposes. The effectiveness of the proposed solution has been evaluated on both synthetically generated and real datasets
Just-in-time Adaptive Classifiers. Part II. Designing the classifier
No full textAging effects, environmental changes, thermal drifts, and soft and hard faults affect physical systems by changing their nature and behavior over time. To cope with a process evolution adaptive solutions must be envisaged to track its dynamics; in this direction, adaptive classifiers are generally designed by assuming the stationary hypothesis for the process generating the data with very few results addressing nonstationary environments. This paper proposes a methodology based on -nearest neighbor (NN) classifiers for designing adaptive classification systems able to react to changing conditions just-in-time (JIT), i.e., exactly when it is needed. -NN classifiers have been selected for their computational-free training phase, the possibility to easily estimate the model complexity and keep under control the computational complexity of the classifier through suitable data reduction mechanisms. A JIT classifier requires a temporal detection of a (possible) process deviation (aspect tackled in a companion paper) followed by an adaptive management of the knowledge base (KB) of the classifier to cope with the process change. The novelty of the proposed approach resides in the general framework supporting the real-time update of the KB of the classification system in response to novel information coming from the process both in stationary conditions (accuracy improvement) and in nonstationary ones (process tracking) and in providing a suitable estimate of . It is shown that the classification system grants consistency once the change targets the process generating the data in a new stationary state, as it is the case in many real applications
EVAD: encrypted vibrational anomaly detection with homomorphic encryption
No full textOne of the main concerns of cloud-based services based on machine and deep learning algorithms is the privacy of users’ data. This is particularly relevant when companies want to leverage such services because they have to outsource potentially sensible data to be processed. In this work, the problem of privacy-preserving anomaly detection on industrial vibrational data with machine learning is tackled. It consists in the detection of irregularities or deviations from expected patterns in the vibration signals generated by industrial machinery and equipment. Such anomalies can be indicative of potential equipment failures, maintenance needs, or process deviations, making their timely detection critical for ensuring the smooth operation and reliability of industrial systems. We combine this industrial need with the ability to guarantee data privacy by proposing encrypted vibrational anomaly detection (EVAD). EVAD allows the detection of anomalies on vibrational data in a privacy-preserving manner by integrating, for the first time in the literature, one-class support vector machines and homomorphic encryption, the latter being a particular kind of encryption that allows the computation of some operations directly on encrypted data. Experimental results show that, on two publicly available datasets for vibrational anomaly detection, EVAD is able to distinguish, in a privacy-preserving manner, between nominal and anomaly situations, in an effective and efficient way. To the best of our knowledge, EVAD represents the first privacy-preserving solution for the detection of anomalies in vibrational data present in the literature
Tiny Machine Learning for Concept Drift
No full textTiny machine learning (TML) is a new research area whose goal is to design machine and deep learning (DL) techniques able to operate in embedded systems and the Internet-of-Things (IoT) units, hence satisfying the severe technological constraints on memory, computation, and energy characterizing these pervasive devices. Interestingly, the related literature mainly focused on reducing the computational and memory demand of the inference phase of machine and deep learning models. At the same time, the training is typically assumed to be carried out in cloud or edge computing systems (due to the larger memory and computational requirements). This assumption results in TML solutions that might become obsolete when the process generating the data is affected by concept drift (e.g., due to periodicity or seasonality effect, faults or malfunctioning affecting sensors or actuators, or changes in the users’ behavior), a common situation in real-world application scenarios. For the first time in the literature, this article introduces a TML for concept drift (TML-CD) solution based on deep learning feature extractors and a k -nearest neighbors ( k -NNs) classifier integrating a hybrid adaptation module able to deal with concept drift affecting the data-generating process. This adaptation module continuously updates (in a passive way) the knowledge base of TML-CD and, at the same time, employs a change detection test (CDT) to inspect for changes (in an active way) to quickly adapt to concept drift by removing obsolete knowledge. Experimental results on both image and audio benchmarks show the effectiveness of the proposed solution, whilst the porting of TML-CD on three off-the-shelf micro-controller units (MCUs) shows the feasibility of what is proposed in real-world pervasive systems
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