Population aging, increasing social demands, and rising costs of treatments are stressing healthcare systems to the point of risking the sustainability of universal and accessible healthcare.

Abstract Detecting and reacting to non-technical losses (NTL) is a fundamental activity that energy providers need to face in their daily routines. This is known to be challenging since the phenomenon of NTL is multi-factored, dynamic and extremely contextual, which makes artificial intelligence (AI) and, in particular, machine learning, natural areas to bring effective and tailored solutions. If the human factor is disregarded in the process of detecting NTL, there is a high risk of performance degradation since typical problems like dataset shift and biases cannot be easily identified by an algorithm. This paper presents a case study on incorporating explainable AI (XAI) in a mature NTL detection system that has been in production in the last years both in electricity and gas. The experience shows that incorporating this capability brings interesting improvements to the initial system and especially serves as a common ground where domain experts, data scientists, and business analysts can meet.

By a trajectory, in the medical world, we mean the sequence of clinical events that occur to a patient in some time frame, as implicitly stored in patients' Electronical Health Records. A set of trajectories can be summarized in a trajectory graph, whose paths contain the most common trajectories followed by patients. The graph contains events on its nodes and the edges indicate the temporal relations. Previous works on building trajectory graphs only allow for one event at each node, and conversely for an event type to appear in only one node, thus losing information and potentially mixing different groups of patients. Here we develop a procedure to extract the trajectory graphs that goes beyond both limitations, thus more accurately reflecting the original dataset. In addition, it is close to a notion of patient state which clinicians use intuitively, facilitating interpretation. We evaluate the procedure on two real-world datasets, one related to diagnostics at hospital admissions, and the other on prescriptions in intensive care units, with reasonable and potentially useful results. The method is described here in the medical context only, but it is of general applicability for sequences of events.

Data from utility meters (gas, electricity, water) is a rich source of information for distribution companies, beyond billing. In this paper we present a supervised technique, which primarily but not only feeds on meter information, to detect meter anomalies and customer fraudulent behavior (meter tampering). Our system detects anomalous meter readings on the basis of models built using machine learning techniques on past data. Unlike most previous work, it can incrementally incorporate the result of field checks to grow the database of fraud and non-fraud patterns, therefore increasing model precision over time and potentially adapting to emerging fraud patterns. The full system has been developed with a company providing electricity and gas and already used to carry out several field checks, with large improvements in fraud detection over the previous checks which used simpler techniques.

Population aging in developed countries brings an increased prevalence of chronic disease and of polymedication-patients with several prescribed types of medication. Attention to chronic, polymedicated patients is a priority for its high cost and the associated risks, and tools for analyzing, understanding, and managing this reality are becoming necessary. We describe a prototype of a system for discovering, analyzing, and visualizing the co-occurrence of diagnostics, interventions, and medication prescriptions in a large patient database. The final tool is intended to be used both by health managers and planners and for primary care clinicians in direct contact with patients (for example for detecting unusual disease patterns and incorrect or missing medication). At the core of the analysis module there is a representation of diagnostics and medications as a hypergraph, and the most crucial functionalities rely on hypergraph transversal/variants of association rule discovery methods, with particular emphasis on discovering surprising or alarming combinations. The test database comes from the primary care system in the area of Barcelona for 2013, with over 1.6 million potential patients and almost 20 million diagnostics and prescriptions.

Interest in powering data enters at least partially using on-site renewable sources, e.g. solar or wind, has been growing. In fact, researchers have studied distributed services comprising networks of such "green" data centers, and load distribution approaches that "follow the renewables" to maximize their use. However, prior works have not considered where to site such a network for efficient production of renewable energy, while minimizing both data center and renewable plant building costs. Moreover, researchers have not built real load management systems for follow-the-renewables services. Thus, in this paper, we propose a framework, optimization problem, and solution approach for sitting and provisioning green data centers for a follow-the-renewables HPC cloud service. We illustrate the location selection tradeoffs by quantifying the minimum cost of achieving different amounts of renewable energy. Finally, we design and implement a system capable of migrating virtual machines across the green data centers to follow the renewables. Among other interesting results, we demonstrate that one can build green HPC cloud services at a relatively low additional cost compared to existing services.

The cloud relies upon multi-data center (multi-DC) infrastructures distributed along the world, where people and enterprises pay for resources to offer their web-services to worldwide clients. Intelligent management is required to automate and manage these infrastructures, as the amount of resources and data to manage exceeds the capacities of human operators. Also, it must take into account the cost of running the resources (energy) and the quality of service towards web-services and clients. (De-)consolidation and priming proximity to clients become two main strategies to allocate resources and properly place these web-services in the multi-DC network. Here we present a mathematical model to describe the scheduling problem given web-services and hosts across a multi-DC system, enhancing the decision makers with models for the system behavior obtained using machine learning. After running the system on real DC infrastructures we see that the model drives web-services to the best locations given quality of service, energy consumption, and client proximity, also (de-)consolidating according to the resources required for each web-service given its load.

No abstract available.

Recommender systems have been strongly researched within the last decade. With the arising and popularization of digital social networks a new field has been opened for social recommendations. Considering the network topology, users interactions, or estimating trust between users are some of the new strategies that recommender systems can take into account in order to adapt their techniques to these new scenarios. We introduce MarkovTrust, a way to infer trust from Twitter interactions and to compute trust between distant users. MarkovTrust is based on Markov chains, which makes it simple to be implemented and computationally efficient. We study the properties of this trust metric and study its application in a recommender system of tweets.

Nowadays, virtualized platforms have become the most popular option to deploy complex enough services. The reason is that virtualization allows resource providers to increase resource utilization. Deployed services are expected to be always available, but these long-running services are especially sensitive to suffer from software aging phenomenon. This term refers to an accumulation of errors, which usually causes resource exhaustion, and eventually makes the service hang/crash. To counteract this phenomenon, a preventive approach to fault management, called software rejuvenation has been proposed. In this paper, we propose a framework which provides transparent and predictive software rejuvenation to web services that suffer software aging on virtualized platforms, achieving high levels of availability. To exploit the provider resources, the framework also seeks to maximize the number of services running simultaneously on the platform, while guaranteeing the resources needed by each service.

Graph mining is a challenging task by itself, and even more so when processing data streams which evolve in real-time. Data stream mining faces hard constraints regarding time and space for processing, and also needs to provide for concept drift detection. In this paper we present a framework for studying graph pattern mining on time-varying streams. Three new methods for mining frequent closed subgraphs are presented. All methods work on coresets of closed subgraphs, compressed representations of graph sets, and maintain these sets in a batch-incremental manner, but use different approaches to address potential concept drift. An evaluation study on datasets comprising up to four million graphs explores the strength and limitations of the proposed methods. To the best of our knowledge this is the first work on mining frequent closed subgraphs in non-stationary data streams.

With the massification of high speed Internet access, recent industry consumer reports show that Web site performance is increasingly becoming a key feature in determining user satisfaction, and finally, a decisive factor in whether a user will purchase on a Web site or even return to it. Traditional Web infrastructure capacity planning has focused on maintaining high throughput and availability on Web sites, optimizing the number of servers to serve peak hours to minimize costs. However, as we will show with our study, the conversion rate-the fraction of users that purchase on a site-is higher at peak hours, where systems are more exposed to suffer overload. In this article we propose a methodology to determine the thresholds of user satisfaction as the QoS delivered by an online business degrades, and to estimate its effects on actual sales. The novelty of the presented technique is that it does not involve any intrusive manipulation of production systems, but a learning process over historic sales data that is combined with system performance measurements. The methodology has been applied to Atra palo.com, a top national Travel and Booking site. For our experiments, we were given access to a 3 year long sales history dataset, as well as actual HTTP and resource consumption logs for several weeks. Obtained results enable autonomic resource managers to set best performance goals and optimize the number of server according to the workload, without surpassing the thresholds of user satisfaction and maximizing revenue for the site.

We propose new algorithms for adaptively mining closed rooted trees, both labeled and unlabeled, from data streams that change over time. Closed patterns are powerful representatives of frequent patterns, since they eliminate redundant information. Our approach is based on an advantageous representation of trees and a low-complexity notion of relaxed closed trees, as well as ideas from Galois Lattice Theory. More precisely, we present three closed tree mining algorithms in sequence: an incremental one, IncTreeMiner, a sliding-window based one, WinTreeMiner, and finally one that mines closed trees adaptively from data streams, AdaTreeMiner. By adaptive we mean here that it presents at all times the closed trees that are frequent in the current state of the data stream. To the best of our knowledge this is the first work on mining closed frequent trees in streaming data varying with time. We give a first experimental evaluation of the proposed algorithms.

The growing complexity of software systems is resulting in an increasing number of software faults. According to the literature, software faults are becoming one of the main sources of unplanned system outages, and have an important impact on company benefits and image. For this reason, a lot of techniques (such as clustering, fail-over techniques, or server redundancy) have been proposed to avoid software failures, and yet they still happen. Many software failures are those due to the software aging phenomena. In this work, we present a detailed evaluation of our chosen machine learning prediction algorithm (M5P) in front of dynamic and non-deterministic software aging. We have tested our prediction model on a three-tier web J2EE application achieving acceptable prediction accuracy against complex scenarios with small training data sets. Furthermore, we have found an interesting approach to help to determine the root cause failure: The model generated by machine learning algorithms.

Advanced analysis of data streams is quickly becoming a key area of data mining research as the number of applications demanding such processing increases. Online mining when such data streams evolve over time, that is when concepts drift or change completely, is becoming one of the core issues. When tackling non-stationary concepts, ensembles of classifiers have several advantages over single classifier methods: they are easy to scale and parallelize, they can adapt to change quickly by pruning under-performing parts of the ensemble, and they therefore usually also generate more accurate concept descriptions. This paper proposes a new experimental data stream framework for studying concept drift, and two new variants of Bagging: ADWIN Bagging and Adaptive-Size Hoeffding Tree (ASHT) Bagging. Using the new experimental framework, an evaluation study on synthetic and real-world datasets comprising up to ten million examples shows that the new ensemble methods perform very well compared to several known methods.

Abstract It has been argued that the actual distribution of word frequencies could be reproduced or explained by generating a random sequence of letters and spaces according to the so‐called intermittent silence process. The same kind of process could reproduce or explain the counts of other kinds of units from a wide range of disciplines. Taking the linguistic metaphor, we focus on the frequency spectrum, i.e., the number of words with a certain frequency, and the vocabulary size, i.e., the number of different words of text generated by an intermittent silence process. We derive and explain how to calculate accurately and efficiently the expected frequency spectrum and the expected vocabulary size as a function of the text size.

In this paper we introduce a new approach to the consolidation strategy of a data center that allows an important reduction in the amount of active nodes required to process a heterogeneous workload without degrading the offered service level. This article reflects and demonstrates that consolidation of dynamic workloads does not end with virtualization. If energy-efficiency is pursued, the workloads can be consolidated even more using two techniques, memory compression and request discrimination, which were separately studied and validated in previous work and are now to be combined in a joint effort. We evaluate the approach using a representative workload scenario composed of numerical applications and a real workload obtained from a top national travel website. Our results indicate that an important improvement can be achieved using 20% less servers to do the same work. We believe that this serves as an illustrative example of a new way of management: tailoring the resources to meet high level energy efficiency goals.

Closed patterns are powerful representatives of frequent patterns, since they eliminate redundant information. We propose a new approach for mining closed unlabeled rooted trees adaptively from data streams that change over time. Our approach is based on an efficient representation of trees and a low complexity notion of relaxed closed trees, and leads to an on-line strategy and an adaptive sliding window technique for dealing with changes over time. More precisely, we first present a general methodology to identify closed patterns in a data stream, using Galois Lattice Theory. Using this methodology, we then develop three closed tree mining algorithms: an incremental one IncTreeNat, a sliding-window based one, WinTreeNat, and finally one that mines closed trees adaptively from data streams, AdaTreeNat. To the best of our knowledge this is the first work on mining frequent closed trees in streaming data varying with time. We give a first experimental evaluation of the proposed algorithms.

We present a new approach for dealing with distribution change and concept drift when learning from data sequences that may vary with time. We use sliding windows whose size, instead of being fixed a priori, is recomputed online according to the rate of change observed from the data in the window itself. This delivers the user or programmer from having to guess a time-scale for change. Contrary to many related works, we provide rigorous guarantees of performance, as bounds on the rates of false positives and false negatives. Using ideas from data stream algorithmics, we develop a time- and memory-efficient version of this algorithm, called ADWIN2. We show how to combine ADWIN2 with the Naïve Bayes (NB) predictor, in two ways: one, using it to monitor the error rate of the current model and declare when revision is necessary and, two, putting it inside the NB predictor to maintain up-to-date estimations of conditional probabilities in the data. We test our approach using synthetic and real data streams and compare them to both fixed-size and variable-size window strategies with good results.

In this paper; we show how to extend the argument due to Bonet, Pitassi and Raz to show that bounded-depth Frege proofs do not have feasible interpolation, assuming that factoring of Blum integers or computing the Diffie-Hellman function is sufficiently hard. It follows as a corollary that bounded-depth Frege is not automatizable; in other words, there is no deterministic polynomial-time algorithm that will output a short proof if one exists. A notable feature of our argument is its simplicity.

This article studies the computational power of various discontinuous real computational models that are based on the classical analog recurrent neural network (ARNN). This ARNN consists of finite number of neurons; each neuron computes a polynomial net function and a sigmoid-like continuous activation function. We introduce arithmetic networks as ARNN augmented with a few simple discontinuous (e.g., threshold or zero test) neurons. We argue that even with weights restricted to polynomial time computable reals, arithmetic networks are able to compute arbitrarily complex recursive functions. We identify many types of neural networks that are at least as powerful as arithmetic nets, some of which are not in fact discontinuous, but they boost other arithmetic operations in the net function (e.g., neurons that can use divisions and polynomial net functions inside sigmoid-like continuous activation functions). These arithmetic networks are equivalent to the Blum-Shub-Smale model, when the latter is restricted to a bounded number of registers. With respect to implementation on digital computers, we show that arithmetic networks with rational weights can be simulated with exponential precision, but even with polynomial-time computable real weights, arithmetic networks are not subject to any fixed precision bounds. This is in contrast with the ARNN that are known to demand precision that is linear in the computation time. When nontrivial periodic functions (e.g., fractional part, sine, tangent) are added to arithmetic networks, the resulting networks are computationally equivalent to a massively parallel machine. Thus, these highly discontinuous networks can solve the presumably intractable class of PSPACE-complete problems in polynomial time.

The computational power of recurrent neural networks is shown to depend ultimately on the complexity of the real constants (weights) of the network. The complexity, or information contents, of the weights is measured by a variant of resource-bounded Kolmogorov (1965) complexity, taking into account the time required for constructing the numbers. In particular, we reveal a full and proper hierarchy of nonuniform complexity classes associated with networks having weights of increasing Kolmogorov complexity.

Sequential algorithms given by Angluin 1987 and Schapire 1992 learn deterministic nite automata DFA exactly from Membership and Equivalence queries.These algorithms are feasible, in the sense that they take time polynomial in n and m, where n is the number of states of the automaton and m is the length of the longest counterexample to an Equivalence query.This paper studies whether parallelism can lead to substantially more e cient algorithms for the problem.We show that no CRCW PRAM machine using a number of processors polynomial in n and m can identify DFA in on= log n time.Furthermore, this lower bound is tight up to constant factors: we develop a CRCW PRAM learning algorithm that uses polynomially many processors and exactly learns DFA in time On= log n.

For a set L that is polynomial-time reducible (in short, $ \leqslant _{\text{T}}^{\text{P}} $-reducible) to some sparse set, the authors investigate the computational complexity of such sparse sets relative to L and prove the first lower bounds on the complexity of recognizing such sets. Sets A and B are constructed such that both of them are $ \leqslant _{\text{T}}^{\text{P}} $-reducible to some sparse set, but A (respectively, B) is $ \leqslant _{\text{T}}^{\text{P}} $-reducible to no sparse set in ${\text{P}}^A $ (respectively, ${\text{NP}}^B \cap {\text{co-NP}}^B $); that is, the complexity of sparse sets to which A (respectively, B) is $ \leqslant _{\text{T}}^{\text{P}} $-reducible is more than ${\text{P}}^A $ (respectively, ${\text{NP}}^B \cap {\text{co-NP}}^B $). From these results or application of the proof technique the authors obtain (1) lower bounds for the relative complexity of generating polynomial-size circuits for some sets in ${{\text{P}} / {{\operatorname{poly}}}}$ and (2) separations of the classes of sets equivalent or reducible to sparse sets under various polynomial-time reducibilities.