@article {18547,
	title = {Anomaly and Change Detection in Graph Streams through Constant-Curvature Manifold Embeddings},
	journal = {IJCNN 2018 : International Joint Conference on Neural Networks},
	year = {2018},
	month = {07/2018},
	author = {Zambon, Daniele and Livi, Lorenzo and Alippi, Cesare}
}
@book {18544,
	title = {Artificial Intelligence in the Age of Neural Networks and Brain Computing},
	series = {Academic Press },
	year = {2018},
	pages = {420},
	edition = {1},
	abstract = {Artificial Intelligence in the Age of Neural Networks and Brain Computing demonstrates that existing disruptive implications and applications of AI is a development of the unique attributes of neural networks, mainly machine learning, distributed architectures, massive parallel processing, black-box inference, intrinsic nonlinearity and smart autonomous search engines. The book covers the major basic ideas of brain-like computing behind AI, provides a framework to deep learning, and launches novel and intriguing paradigms as future alternatives. The success of AI-based commercial products proposed by top industry leaders, such as Google, IBM, Microsoft, Intel and Amazon can be interpreted using this book. },
	author = {Kozma, Robert and Alippi, Cesare and Choe, Yoonsuck and Morabito, Francesco}
}
@conference {18593,
	title = {A characterization of the Edge of Criticality in Binary Echo State Networks},
	booktitle = {2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP)},
	year = {2018},
	month = {09/2018},
	address = {Aalborg, Denmark},
	author = {Verzelli, Pietro and Livi, Lorenzo and Alippi, Cesare}
}
@article {18545,
	title = {Concept Drift and Anomaly Detection in Graph Streams},
	journal = {IEEE Transactions on Neural Networks and Learning Systems},
	volume = {29},
	issue = {11},
	year = {2018},
	month = {11/2018},
	pages = {5592-5605},
	chapter = {5592},
	author = {Zambon, Daniele and Alippi, Cesare and Livi, Lorenzo}
}
@article {18536,
	title = {Credit Card Fraud Detection: A Realistic Modeling and a Novel Learning Strategy},
	journal = {IEEE Transactions on Neural Networks and Learning Systems},
	year = {2018},
	pages = {1-14},
	keywords = {Amplitude modulation, Area measurement, Companies, Concept drift, Credit card fraud detection, Credit cards, learning in nonstationary environments, Learning systems, Tools, Training, unbalanced classification.},
	issn = {2162-237X},
	doi = {10.1109/TNNLS.2017.2736643},
	author = {Dal Pozzolo, Andrea and Boracchi, Giacomo and Caelen, Olivier and Alippi, Cesare and Bontempi, Gianluca}
}
@article {18533,
	title = {Determination of the Edge of Criticality in Echo State Networks Through Fisher Information Maximization},
	journal = {IEEE Transactions on Neural Networks and Learning Systems},
	volume = {29},
	year = {2018},
	month = {March},
	pages = {706-717},
	keywords = {Echo state network (ESN), echo state networks, edge of criticality, ESN, Fisher information, Fisher information matrix, Fisher information maximization, Jacobian matrices, Learning systems, Neurons, nonparametric estimation, nonparametric estimator, nonparametric statistics, prediction error, Probability density function, recurrent neural nets, recurrent neural networks, Reservoirs, RNN, short-term memory capacity, Training},
	issn = {2162-237X},
	doi = {10.1109/TNNLS.2016.2644268},
	author = {Livi, Lorenzo and Bianchi, Filippo Maria and Alippi, Cesare}
}
@article {18508,
	title = {Investigating echo state networks dynamics by means of recurrence analysis},
	journal = {IEEE Transactions on Neural Networks and Learning Systems},
	volume = {29},
	year = {2018},
	month = {02/2018},
	pages = { 427 - 439},
	abstract = {In this paper, we elaborate over the well-known
interpretability issue in echo state networks. The idea is to investigate
the dynamics of reservoir neurons with time-series analysis
techniques taken from research on complex systems. Notably, we
analyze time-series of neuron activations with Recurrence Plots
(RPs) and Recurrence Quantification Analysis (RQA), which
permit to visualize and characterize high-dimensional dynamical
systems. We show that this approach is useful in a number of
ways. First, the two-dimensional representation offered by RPs
provides a way for visualizing the high-dimensional dynamics of
a reservoir. Our results suggest that, if the network is stable,
reservoir and input denote similar line patterns in the respective
RPs. Conversely, the more unstable the ESN, the more the RP
of the reservoir presents instability patterns. As a second result,
we show that the Lmax measure is highly correlated with the
well-established maximal local Lyapunov exponent. This suggests
that complexity measures based on RP diagonal lines distribution
provide a valuable tool to quantify the degree of network stability.
Finally, our analysis shows that all RQA measures fluctuate on
the proximity of the so-called edge of stability, where an ESN
typically achieves maximum computational capability. We verify
that the determination of the edge of stability provided by such
RQA measures is more accurate than two well-known criteria
based on the Jacobian matrix of the reservoir. Therefore, we
claim that RPs and RQA-based analyses can be used as valuable
tools to design an effective network given a specific problem.},
	doi = {10.1109/TNNLS.2016.2630802},
	author = {Bianchi, Filippo Maria and Livi, Lorenzo and Alippi, Cesare}
}
@conference {18546,
	title = {Moving Convolutional Neural Networks to Embedded Systems: The Alexnet and VGG-16 Case},
	booktitle = {Proceedings of the 17th ACM/IEEE International Conference on Information Processing in Sensor Networks},
	year = {2018},
	publisher = {IEEE Press},
	organization = {IEEE Press},
	address = {Piscataway, NJ, USA},
	keywords = {approximate computing, convolutional neural networks, deep learning, embedded systems},
	isbn = {978-1-5386-5298-5},
	doi = {10.1109/IPSN.2018.00049},
	url = {https://doi.org/10.1109/IPSN.2018.00049},
	author = {Alippi, Cesare and Disabato, Simone and Roveri, Manuel}
}
@article {18512,
	title = {A Pdf-free Change Detection Test Based on Density Difference Estimation},
	journal = {IEEE Transactions on Neural Networks and Learning Systems},
	volume = {29},
	issue = {2},
	year = {2018},
	month = {11/2016},
	pages = { 324 - 334},
	chapter = {324},
	abstract = {The ability to detect 1 online changes in stationarity or time variance in a data stream is a hot research topic with striking implications. In this paper, we propose a novel probability density function-free change detection test, which is based on the least squares density-difference estimation method and operates online on multidimensional inputs. The test does not require any assumption about the underlying data distribution, and is able to operate immediately after having been configured by adopting a reservoir sampling mechanism. Thresholds requested to detect a change are automatically derived once a false positive rate is set by the application designer. Comprehensive experiments validate the effectiveness in detection of the proposed method both in terms of detection promptness and accuracy.},
	doi = {10.1109/TNNLS.2016.2619909},
	author = {Bo, Li and Alippi, Cesare and Zhao, Dongbin}
}
@conference {18584,
	title = {Security: The Dark Side of Approximate Computing?},
	booktitle = {Proceedings of the International Conference on Computer-Aided Design},
	year = {2018},
	month = {11/2018},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	isbn = {978-1-4503-5950-4},
	doi = {10.1145/3240765.3243497},
	url = {http://doi.acm.org/10.1145/3240765.3243497},
	author = {Regazzoni, Francesco and Alippi, Cesare and Polian, Ilia}
}
@conference {18538,
	title = {Critical echo state network dynamics by means of Fisher information maximization},
	booktitle = {2017 International Joint Conference on Neural Networks (IJCNN)},
	year = {2017},
	month = {May},
	keywords = {Asymptotic stability, critical phase transitions, echo state network, edge of criticality, Electronic mail, ESN, Estimation, estimation theory, FIM, Fisher information matrix, Fisher information maximization, hidden neurons activations, high short term memory capacity, input signal, low prediction error, network dynamics, network theory (graphs), Neurons, optimisation, Probability density function, recurrent neural nets, Reservoirs, signal processing, statistics, Tuning, unsupervised approach, unsupervised learning},
	doi = {10.1109/IJCNN.2017.7965941},
	author = {Bianchi, Filippo Maria and Livi, Lorenzo and Jenssen, Robert and Alippi, Cesare}
}
@conference {18539,
	title = {Detecting changes at the sensor level in cyber-physical systems: Methodology and technological implementation},
	booktitle = {2017 International Joint Conference on Neural Networks (IJCNN)},
	year = {2017},
	month = {May},
	keywords = {actuators, adaptation mechanisms, adaptive systems, Change detection, change-point method, Computational modeling, cyber-physical systems, datastreams, fault detection and diagnosis, fault tolerant computing, ICI-based change detection test, Intelligence for Embedded and Cyber-physical Systems, Mann-Whitney change-point method, Mathematical model, model-free change detection test, Predictive models, Random variables, self-adaptive CPS, self-adaptive cyber-physical systems, self-configuration, self-healing skills, self-management, sensor acquisitions, sensor level, sensors, signal detection, Smart Sensor Networks, ST STM32 Nucleo platform, time-variant environments, Training},
	doi = {10.1109/IJCNN.2017.7966066},
	author = {Alippi, Cesare and D{\textquoteright}Alto, Viviana and Falchetto, Mirko and Pau, Danilo and Roveri, Manuel}
}
@conference {18542,
	title = {Detecting changes in sequences of attributed graphs},
	booktitle = {2017 IEEE Symposium Series on Computational Intelligence (SSCI)},
	year = {2017},
	month = {Nov},
	keywords = {Aerospace electronics, Anomaly detection, application domains, Attributed graph, attributed graphs, Change detection, Concept drift, Dynamic/Evolving graph, Electronic mail, Embedding, geometric graphs, Graph matching, graph theory, graph-based representations, Markov chains, Markov processes, Microsoft Windows, pair-wise relations, Prototypes, real-world systems, Stationarity, topology, Training, variable order},
	doi = {10.1109/SSCI.2017.8285273},
	author = {Zambon, Daniele and Livi, Lorenzo and Alippi, Cesare}
}
@article {18535,
	title = {An Incremental Change Detection Test Based on Density Difference Estimation},
	journal = {IEEE Transactions on Systems, Man, and Cybernetics: Systems},
	volume = {47},
	year = {2017},
	month = {Oct},
	pages = {2714-2726},
	keywords = {Big data framework, Change detection, change detection method, density difference estimation, Estimation, false positive rates, Feature extraction, Histograms, incremental change detection test, incremental computing, incremental least squares density difference change detection method (LSDD-Inc), Internet of Things, Kernel, least squares approximations, least squares density difference, linear combination, LSDD, LSDD values, nonoverlapping data windows, probability, Probability density function, probability density function (pdf)-free, probability density functions, process continuous data streams, Training},
	issn = {2168-2216},
	doi = {10.1109/TSMC.2017.2682502},
	author = {Bu, Li and Zhao, Dongbin and Alippi, Cesare}
}
@article {18540,
	title = {A Kolmogorov-Smirnov Test to Detect Changes in Stationarity in Big Data},
	journal = {IFAC-PapersOnLine},
	volume = {50},
	year = {2017},
	pages = {14260 - 14265},
	abstract = {The paper proposes an effective change detection test for online monitoring data streams by inspecting the least squares density difference (LSDD) features extracted from two non-overlapped windows. The first window contains samples associated with the pre-change probability distribution function (pdf) and the second one with the post-change one (that differs from the former if a change in stationarity occurs). This method can detect changes by also controlling the false positive rate. However, since the window sizes is fixed after the test has been configured (it has to be small to reduce the execution time), the method may fail to detect changes with small magnitude which need more samples to reach the requested level of confidence. In this paper, we extend our work to the Big Data framework by applying the Kolmogorov-Smirnov test (KS test) to infer changes. Experiments show that the proposed method is effective in detecting changes.},
	keywords = {change detection test, KS test, LSDD},
	issn = {2405-8963},
	doi = {https://doi.org/10.1016/j.ifacol.2017.08.1821},
	url = {http://www.sciencedirect.com/science/article/pii/S2405896317324436},
	author = {Zhao, Dongbin and Bu, Li and Alippi, Cesare and Wei, Qinglai}
}
@conference {18541,
	title = {Learning in Nonstationary Environments: A Hybrid Approach},
	booktitle = {Artificial Intelligence and Soft Computing},
	year = {2017},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {Solutions present in the literature to learn in nonstationary environments can be grouped into two main families: passive and active. Passive solutions rely on a continuous adaptation of the envisaged learning system, while the active ones trigger the adaptation only when needed. Passive and active solutions are somehow complementary and one should be preferred than the other depending on the nonstationarity rate and the tolerable computational complexity. The aim of this paper is to introduce a novel hybrid approach that jointly uses an adaptation mechanism (as in passive solutions) and a change detection triggering the need to retrain the learning system (as in active solutions).},
	isbn = {978-3-319-59060-8},
	author = {Alippi, Cesare and Qi, Wen and Roveri, Manuel},
	editor = {Rutkowski, Leszek and Korytkowski, Marcin and Scherer, Rafa{\l} and Tadeusiewicz, Ryszard and Zadeh, Lotfi A. and Zurada, Jacek M.}
}
@conference {18537,
	title = {A lightweight and energy-efficient Internet-of-birds tracking system},
	booktitle = {2017 IEEE International Conference on Pervasive Computing and Communications (PerCom)},
	year = {2017},
	month = {March},
	keywords = {animal movement tracking, application server, Birds, cellular radio, cloud computing, energy assessment, energy conservation, energy consumption, energy efficient Internet-of-birds tracking system, GSM, GSM-based tracking device energy consumption reduction, Internet of Things, Internet-of-things vision, joint localization-transmission phase, Northern Italy, quality of service, Radar tracking, Receivers, Satellites, telecommunication power management, Tracking, Transmitters},
	doi = {10.1109/PERCOM.2017.7917862},
	author = {Alippi, Cesare and Ambrosini, Roberto and Longoni, Violetta and Cogliati, Dario and Roveri, Manuel}
}
@article {18531,
	title = {Model-Free Fault Detection and Isolation in Large-Scale Cyber-Physical Systems},
	journal = {IEEE Transactions on Emerging Topics in Computational Intelligence},
	volume = {1},
	year = {2017},
	month = {Feb},
	pages = {61-71},
	keywords = {Change detection algorithms, clustering methods, Computational modeling, cyber-physical systems, Fault detection, Hidden Markov models, monitoring, Sensor phenomena and characterization, Sensor systems},
	doi = {10.1109/TETCI.2016.2641452},
	author = {Alippi, Cesare and Ntalampiras, Stavros and Roveri, Manuel}
}
@article {18532,
	title = {Multiplex visibility graphs to investigate recurrent neural network dynamics},
	journal = {Nature-Scientific reports},
	volume = {7},
	year = {2017},
	month = {03/2017},
	pages = {44037-44049},
	chapter = {44037},
	doi = {doi:10.1038/srep44037},
	author = {Bianchi, Filippo Maria and Livi, Lorenzo and Alippi, Cesare and Jenssen, Robert}
}
@article {18534,
	title = {The (Not) Far-Away Path to Smart Cyber-Physical Systems: An Information-Centric Framework},
	journal = {Computer},
	volume = {50},
	year = {2017},
	month = {April},
	pages = {38-47},
	keywords = {adaptive systems, autonomic computing, communication technologies, CPSs, cyber-physical systems, cybersecurity, embedded systems, Energy management, fault detection and diagnosis, Fault diagnosis, Green computing, homogeneous integrated framework, information-centric framework, intelligent functionalities, Intelligent sensors, Intelligent systems, Internet of Things, IoT, learning (artificial intelligence), low-cost sensors, security, smart cyber-physical systems, smart technology},
	issn = {0018-9162},
	doi = {10.1109/MC.2017.111},
	author = {Alippi, Cesare and Roveri, Manuel}
}
@article {18510,
	title = {One-class classifiers based on entropic spanning graphs},
	journal = {IEEE Transactions on Neural Networks and Learning Systems},
	volume = {28},
	issue = {12},
	year = {2017},
	month = {11/2016},
	pages = { 2846 - 2858},
	chapter = {2846},
	abstract = {One-class classifiers offer valuable tools to assess the presence of outliers in data. In this paper, we propose a design methodology for one-class classifiers based on entropic spanning graphs. The spanning graph is learned on the embedded input data, with the aim to generate a partition of the vertices.
The final partition is derived by exploiting a criterion based on mutual information minimization. Here, we compute the mutual information by using a convenient formulation provided in terms of the alfa-Jensen difference. Once training is completed, in order to associate a confidence level with the classifier decision, a graph-based fuzzy model is constructed. The fuzzification process is based only on topological information of the vertices of the entropic spanning graph. As such, the proposed one-class classifier is suitable also for datasets with complex geometric structures. We provide experiments on well-known benchmarking datasets containing both feature vectors and labeled graphs. In addition, we apply the method on the problem of protein solubility recognition by considering several data representations for the samples. Experimental results demonstrate the effectiveness and versatility of the proposed method with respect to other state-of the-art approaches.
},
	doi = { 10.1109/TNNLS.2016.2608983},
	author = {Livi, Lorenzo and Alippi, Cesare}
}
@article {18509,
	title = {Solving Multiobjective Optimization Problems in Unknown Dynamic Environments: An Inverse Modeling Approach},
	journal = {IEEE Transactions on Cybernetics},
	volume = {47},
	issue = {12},
	year = {2017},
	month = {11/2016},
	pages = {4223 - 4234},
	chapter = {4223},
	abstract = {Evolutionary multiobjective optimization in dynamic environments is a challenging task, as it requires the optimization algorithm converging to a time-variant Pareto optimal front. This paper proposes a dynamic multiobjective optimization algorithm which utilizes an inverse model set to guide the search towards promising decision regions. In order to reduce the number of fitness evaluations for change detection purpose, a two stage change detection test is proposed which uses the inverse model set to check potential changes in the objective function landscape. Both static and dynamic multiobjective benchmark optimization problems have been considered to evaluate the performance of the proposed algorithm. Experimental results show that the improvement in optimization performance is achievable when the proposed inverse model set is adopted.},
	doi = {10.1109/TCYB.2016.2602561},
	author = {Gee, Sen Bong and Tan, Kay Chen and Alippi, Cesare}
}
@proceedings {18514,
	title = {CCM: Controlling the Change Magnitude in High Dimensional Data},
	journal = {In Proceedings of the 2nd INNS Conference on Big Data 2016 (INNS Big Data 2016)},
	year = {2016},
	month = {10/2016},
	pages = {1-10},
	address = {Thessaloniki, Greece},
	abstract = {Change-detection algorithms are often tested on real-world datasets where changes are synthetically introduced. While this common practice allows generating multiple datasets to obtain stable performance measures, it is often quite arbitrary since the change magnitude is seldom controlled. Thus, experiments { in particular those on multivariate and high-dimensional data. We here present a rigorous framework for introducing changes having a controlled magnitude in multivariate datasets. In particular, we introduce changes by directly roto-translating the data, and we measure the change magnitude by the symmetric
Kullback-Leibler divergence between pre- ad post-change distributions. We present an iterative algorithm that identities the roto-translation parameters yielding the desired change magnitude, and we prove its convergence analytically. We also illustrate our MATLAB framework that introduces changes having a controlled magnitude in real-world datasets, which is made publicly available for download.
},
	author = {Alippi, Cesare and Boracchi, Giacomo and Carrera, Diego}
}
@conference {18447,
	title = {Change Detection in Multivariate Datastreams: Likelihood and  Detectability Loss},
	booktitle = {25th International Joint Conference on Artificial Intelligence (IJCAI-16)},
	year = {2016},
	month = {07/2016},
	address = { New York, USA},
	abstract = {We address the problem of detecting changes in multivariate datastreams, and we investigate the intrinsic difficulty that change-detection methods have to face when the data-dimension scales. In particular, we consider the general approach that detects changes by comparing the distribution of the log-likelihood of the datastream over different time windows. Despite the fact that this approach constitutes the frame for several change-detection methods, its effectiveness when the dimension of data scales has never been investigated, which is indeed the goal of our paper. We show that the magnitude of the change can be naturally measured by the symmetric Kullback-Leibler divergence between the pre- and post-change distributions, and that the detectability of a change of a given magnitude worsens when the data-dimension increases. This structural problem, which we refer to as detectability loss, is due to the linear relationship existing between the variance of the log-likelihood and the data dimension, and reveals to be harmful even at low data-dimensions (say, 10). We analytically derive the detectability loss on Gaussian-distributed datastreams, and empirically demonstrate that this problem holds also on real-world datasets.},
	author = {Alippi, Cesare and Boracchi, Giacomo and Carrera, Diego and Roveri, Manuel}
}
@article {18511,
	title = {A Cloud to the Ground: The New Frontier of Intelligent and Autonomous Networks of Things},
	journal = {IEEE Communication Magazine},
	volume = {54},
	issue = {12},
	year = {2016},
	month = {11/2016},
	pages = {14 - 20},
	chapter = {14},
	abstract = {The Internet-of-Things (IoT) paradigm is supporting -and will support- an ever-increasing number of services and applications impacting on almost every aspect of our everyday life. The current trend is forecasting IoT to connect tens of billion objects by 2020 yielding a very-high volume of data to be acquired, transmitted and processed. IoT typically relies on Cloud Computing to process, analyze and store the data acquired by IoT entities. Unfortunately, the need to transmit all data from the information producing objects to the Cloud for a subsequent processing/analysis phase would require a large bandwidth and increase the latency in the {\textquotedblleft}decision-making process{\textquotedblright} whenever decisions/reactions must be promptly taken by the IoT units.
The Fog Computing (FC) paradigm aims at addressing these problems by extending Cloud Computing towards the edge of the network. In this direction, this paper introduces a novel FC-IoT paradigm designed
to move computing, storage and applications/services close to IoT objects so as to reduce communication bandwidth and energy consumption as well as {\textquotedblleft}decision-making{\textquotedblright} latency. The proposed IoT-based solution has been designed to have intelligent and autonomous IoT objects that are integrated with a FC and Fog Networking approach. The distinguishing features of the intelligent FC-IoT platform are low-latency, self-adaptation, low energy consumption and spectrum efficiency.
},
	doi = { 10.1109/MCOM.2016.1600541CM},
	author = {Alippi, Cesare and Fantacci, Romano and Marabissi, Dania and Roveri, Manuel}
}
@conference {18449,
	title = {Ensemble LSDD-based Change Detection Tests},
	booktitle = {IEEE-INNS  International Joint Conference on Neural Networks (IJCNN16)},
	year = {2016},
	month = {07/2016},
	address = {Vancouver, Canada},
	abstract = {The least squares density difference change detection test (LSDD-CDT) has proven to be an effective method in detecting concept drift by inspecting features derived from the discrepancy between two probability density functions (pdfs). The first pdf is associated with the concept drift free case, the second to the possible post change one. Interestingly, the method permits to control the ratio of false positives. This paper introduces and investigates the performance of a family of LSDD methods constructed by exploring different ensemble options applied to the basic CDT procedure. Experiments show that most of proposed methods are characterized by improved performance in change detection once compared with the direct ensemble-free counterpart.},
	author = {Bu, Li and Alippi, Cesare and Zhao, Dongbin}
}
@conference {18513,
	title = {An improved Hilbert-Huang Transform for non-linear and time-variant signals},
	booktitle = {26th Italian Workshop on Neural Networks (WIRN 2016)},
	year = {2016},
	month = {05/2016},
	pages = {1-8},
	address = {Vietri sul Mare, Salerno, Italy},
	abstract = {Learning in non-stationary/evolving environments requires methods able to process and deal with non-stationary streams. In this paper we propose a novel algorithm providing a time-frequency decomposition
of time-variant signals. Outcoming signals can be used to identify anomalous events/patterns or extract features associated with the time variance of the signal, precious information for any consequent learning
action. The paper extends the Hilbert-Huang Transform notoriously used to deal with time-variant signals by introducing (i) a new Empirical Mode Decomposition that identies the number of frequency modes of
the signal and (ii) an extension of the Hilbert Transform that eliminates negative frequency-values in the time-frequency spectrum. The effectiveness of the proposed Transform has been tested on both synthetic and real time-variant signals acquired by a real-world intelligent system for landslide monitoring.
},
	author = {Alippi, Cesare and Wen, Qi and Roveri, Manuel}
}
@article {18543,
	title = {Model Complexity, Regularization, and Sparsity [Guest Editorial]},
	year = {2016},
	keywords = {Adaptation models, Computational complexity, machine learning, Sparse matrices, Special issues and sections},
	doi = {10.1109/MCI.2016.2602071},
	author = {Alippi, Cesare and Boracchi, Giacomo and Wohlberg, Brendt}
}
@conference {18448,
	title = {One-Class Classification Through Mutual Information Minimization},
	booktitle = {IEEE-INNS  International Joint Conference on Neural Networks (IJCNN16)},
	year = {2016},
	month = {07/2016},
	address = {Vancouver, Canada},
	abstract = {In one-class classification problems, a model is synthesized by using only information coming from the nominal state of the data generating process. Many important applications can be cast in the one-class classification framework, such as anomaly and change in stationarity detection, and fault recognition. In this paper, we present a novel design methodology for oneclass classifiers derived from graph-based entropy estimators. The entropic graph is used to generate a partition of the input nominal conditions, which corresponds to the classifier model. Here we propose a criterion based on mutual information minimization to learn such a partition. The _-Jensen difference is considered, which provides a convenient way for estimating the mutual information. The classifier incorporates also a fuzzy model, providing a confidence value for a generic test sample during operational modality, expressed as a membership degree of the sample to the nominal conditions class. The fuzzification mechanism is based only on topological properties of the entropic spanning graph vertices; as such, it allows to model clusters of arbitrary shapes. We show preliminary {\textendash} yet very promising {\textendash} results on both synthetic problems and real-world datasets for one-class classification.
},
	author = {Livi, Lorenzo and Alippi, Cesare}
}
@conference {18450,
	title = {Online Model-free Sensor Fault Identification and Dictionary Learning in Cyber-Physical Systems},
	booktitle = {IEEE-INNS  International Joint Conference on Neural Networks (IJCNN16)},
	year = {2016},
	month = {07/2016},
	address = {Vancouver, Canada},
	abstract = {This paper presents a model-free method for the online identification of sensor faults and learning of their fault dictionary. The method, designed having in mind Cyber-Physical Systems (CPSs), takes advantage of functional relationships among the datastreams acquired by CPS sensing units. Existing model-free change detection mechanisms are proposed to detect faults and identify the fault type thanks to a fault dictionary
which is built over time. The main features of the proposed algorithm are its ability to operate without requiring any a priori information about the system under inspection or the nature of the possibly occurring faults. As such, the method follows the model-free approach, characterized by the fact the fault dictionary
is constructed online once faults are detected. Whenever available, humans can be considered in the loop to label a fault or a fault class in the dictionary as well as introduce fault instances generated thanks to a priori information. Experimental results on both synthetic and real datasets corroborate the effectiveness of the proposed fault diagnosis system.
},
	author = {Alippi, Cesare and Ntalampiras, Stavros and Roveri, Manuel}
}
@article {15.AlGaSte2003,
	title = {An Application Level Synthesis Methodology for Multidimensional Embedded Processing Systems},
	journal = {IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems},
	volume = {22},
	number = {11},
	year = {2003},
	month = {November},
	pages = {1457-1470},
	abstract = {The implementation of multidimensional systems in embedded devices is a major design challenge due to the high algorithmic complexity of the applications. The authors suggest a novel application-level synthesis methodology for those parts of the embedded application which are characterized by being Lebesgue measurable (the computation involved in signal and image processing systems is Lebesgue measurable). The synthesis methodology, based on perturbation analysis, supports the design of analog, digital, or mixed implementations at the very high level of the system design cycle. The outputs of the methodology are quantitative indications regarding the maximum performance loss tolerable by the subsystems composing the application. Such information, augmented with a stochastic description of the tolerated perturbations, can be related to lower synthesis levels and guide the designer toward the final implementation of the embedded device. The perturbation analysis is based on randomized algorithms for an effective evaluation of the performance loss of the computational flow once affected by behavioral perturbations and a Tabu-search-inspired optimizing algorithm for distributing the tolerable performance loss at the system output along the computational subsystems composing the possibly multidimensional processing.},
	keywords = {application-level synthesis, multidimensional systems, randomized algorithms, robustness analysis, tabu search, yield maximization},
	doi = {http://dx.doi.org/10.1109/TCAD.2003.818304},
	author = {Alippi, Cesare and Galbusera, Andrea and Stellini, Marco}
}
@conference {5.AlGaSte2002,
	title = {An Application Level Synthesis Methodology for Embedded Systems},
	booktitle = {ISCAS 2002},
	year = {2002},
	month = {May 26-29},
	pages = {473-476},
	address = {Scottsdale},
	abstract = {Time-to-market, cost and power consumption requirements are pushing research in embedded systems towards the development of sophisticated CAD environments. The paper suggests a novel synthesis methodology for embedded devices based on an application level perturbation analysis. The methodology is based on randomised algorithms for evaluating the effective performance loss of the computational flow induced by perturbations and a Tabu-search optimising algorithm for distributing the tolerable performance loss along the computational subsystems composing the computation.},
	doi = {http://dx.doi.org/10.1109/ISCAS.2002.1010743},
	author = {Alippi, Cesare and Galbusera, Andrea and Stellini, Marco}
}
