ABSTRACT Accurately assessing the energy consumption associated with public buildings and the services they provide is essential for supporting sustainable infrastructure planning. This work presents an integrated modelling framework that combines machine learning prediction of building energy use with a graph based representation of user mobility, allowing a comprehensive estimation of total energy demand. The approach includes building operations, service related energy, travel by users and staff and supply chain logistics. Building energy consumption is predicted through supervised models trained on a curated subset of commercial facilities selected for their similarity to public service environments. Mobility is modelled using a synthetic geographical network that encodes population distribution, available transportation modes, behavioural tendencies and relocation dynamics. The framework is applied to a university reorganization scenario, exploring alternative facility configurations and varying degrees of remote activity. Results indicate that user travel is generally the dominant contributor to total energy demand, while the importance of building energy increases as in person attendance decreases. Sensitivity analyses confirm the robustness of the optimal configurations under different behavioural assumptions. We stress that this is not actually an optimization algorithm, but a parameter sweep; in real situations, constraints may be applied, for instance for building availability, costs, opportunity of sharing services or specific goals. The methodology is further demonstrated in a real healthcare application, where the predictive model enables reliable estimation of building energy use in the absence of direct measurements. Overall, the proposed framework illustrates how data driven modelling and intelligent system techniques can support sustainable decision making for complex public service infrastructures.

Natural Language Processing (NLP) algorithms have significantly advanced the capabilities of understanding, processing and generating human language. However, one persistent challenge in NLP is the problem of uncertainty, due e.g. to the inherent complexity of human language, variations in language usage across different contexts and domains, and the presence of noisy or incomplete data. In this work we take in consideration this problem for statistics derived from court documents by NLP systems.

Anonymization is an important aspect of data privacy protection, especially in the context of sensitive personal information collected through sensors. In this paper, we propose a new service-based architecture for anonymizing such data in real-time, ensuring that data is accessible to authorized users while maintaining privacy. Our architecture is based on the annotation of data at ingestion time, where privacy levels are assigned to sets of columns. The anonymization procedure is performed by compressing and encoding the data through an autoencoder model, where the encoder and decoder functions are defined as parametric functions composed of multiple hidden layers.

The use of data acquisition and fusion techniques allow to generate event graphs in support of criminal investigations. In this paper, an anonymized real case study will be presented to identify undue transactions through graph analysis. All the steps of an investigation protocol are illustrated by describing how the tools adopted in this paper allow to semi-automatically analyze huge amounts of data coming from different nature, identifying suspicious transactions with high precision.

Abstract Criminal investigation adopts Artificial Intelligence to enhance the volume of the facts that can be investigated and documented in trials. However, the abstract reasoning implied in legal justification and argumentation requests to adopt solutions providing high precision, low generalization error, and retrospective transparency. Three requirements that hardly coexist in today’s Artificial Intelligence solutions. In a controlled experiment, we then investigated the use of graph embeddings procedures to retrieve potential criminal actions based on patterns defined in enquiry protocols. We observed that a significant level of accuracy can be achieved but different graph reformation procedures imply different levels of precision, generalization, and transparency.

Events happening at defined moments in time and involving specific entities from a social or physical system can be organized in networks or graphs. The study of such event graphs may reveal causal relations between subsequent events or compound events that we define as “typed events”. Moreover, characteristic sequences of events or patterns can arise in consequence of phenomena affecting the system. Methods to build the event graph and to search for the typed events and their significance are described in detail. An embedding strategy to encode typed events in low dimensional vectors is defined, and both supervised and unsupervised learning is applied to search for meaningful patterns. Experiments have been conducted using data from a real investigation and some synthetic data.

Summary As now well established, the world population is aging rapidly and, according to World Health Organization (WHO), the amount of people aged 60 years and older is expected to total 2 billion in 2050. For this reason, an emerging important issue is the definition of a new generation of healthcare platforms capable of monitoring people's quality of life. In this article, we propose a new methodology that supports the entire requirement elicitation process starting from the initial phase of gathering the requirements, both clinical, technological, and end‐user, up to the choice of the most suitable solution. Our proposal provides a new new iterative model in the smart healthcare field research area. Furthermore we apply our proposal in a real scenario and we report the end‐to‐end implementation of the proposed methodology.

The Big Data revolution promises to build a data-driven ecosystem where better decisions are supported by enhanced analytics and data management.However, major hurdles still need to be overcome on the road that leads to commoditization and wide adoption of Big Data Analytics (BDA).Big Data complexity is the first factor hampering the full potential of BDA.The opacity and variety of Big Data technologies and computations, in fact, make BDA a failure prone and resource-intensive process, which requires a trial-and-error approach.This problem is even exacerbated by the fact that current solutions to Big Data application development take a bottom-up approach, where the last technology release drives application development.Selection of the best Big Data platform, as well as of the best pipeline to execute analytics, represents then a deal breaker.In this paper, we propose a return to roots by defining a Model-Driven Engineering (MDE) methodology that supports automation of BDA based on model specification.Our approach lets customers declare requirements to be achieved by an abstract Big Data platform and smart engines deploy the Big Data pipeline carrying out the analytics on a specific instance of such platform.Driven by customers' requirements, our methodology is based on an OWL-S ontology of Big Data services and on a compiler transforming OWL-S service compositions in workflows that can be directly executed on the selected platform.The proposal is experimentally evaluated in a real-world scenario focusing on the threat detection system of SAP.

Knowledge graphs are exploited in criminal investigation to integrate heterogeneous data sources and scale up the operational efficiency of enquiry protocols. Using a declarative perspective, protocols can be viewed as a set of data ingestion procedures and nested exact queries. This meets the probating nature of procedural justice that has to proceed from established facts. At the same time, the exact specification of queries represents a limit for enquiry protocols that can exclusively retrieve those facts in adherence to the designed queries. We then investigated the use of graph em-beddings procedures to extend the scope of a protocol by returning sub-graphs partially matching to its specification. Because exploring the entire set of sub-graphs quickly become computationally intractable, we developed an approach based on a hierarchical filtering procedure. A controlled experiment we executed has shown the feasibility of our approach.

The requirement elicitation process of a Smart Healthcare platform is challenging as conflicting needs must be conciliated. Stakeholders such as clinicians, patients, and caregivers, legal, organizational, and technological constraints, all these elements must be accounted for and coordinated. Starting from a real case study, this paper offers an overview of the aspects that was perceived as innovative by the stakeholders and experts involved in the desgn process.

Computational Intelligence techniques are adopted in many industrial applications, like visual-based quality control, image enhancement in consumer electronics, image quality enhancement, video-based recognition of identity or behaviors, audio-based speech recognition for enhanced human like interaction with machines, and so on.It also has a strong impact in medical applications, like medical image enhancement, semiautomatic detection of pathologies, prefiltering and reconstruction of volumes from medical scans, and so on.Despite this growing diffusion, there are still many possible areas where computational intelligence application is partial or could be extended and improved due to the actual limitations in terms of computational power or strict requirements in terms of assurance of the results. THEMES OF THIS SPECIAL ISSUEStarting from the above considerations, this special issue aims to investigate the impact of the adoption of advanced and innovative Computational Intelligence techniques in industrial and medical applications including the ones that takes advantage of recent Big Data Architectures.This edition of the special issue is focused primarily on signal processing for industrial and medical applications with special emphasis to stream processing and Big Data platforms.This issue is intended to provide a highly recognized international forum to present recent advances in Concurrency and Computation: Practice and Experience.We welcomed both theoretical contributions as well as articles describing interesting applications.Articles were invited for this special issue considering aspects of this problem, including:• Imaging for industrial applications.

The advent of Big Data has revolutionized the way in which data are collected, analyzed, and processed, becoming a pre-requisite for each enterprise that competes in the global market. In this respect, the commodization of Big Data analytics is an essential goal to be faced in the near future. Recently, some preliminary approaches have been presented mostly focusing on distributing Big Data platforms as a service, while less has been done on cross-platform Big Data analytics. In this paper, we propose a model-based methodology for Big Data Analytics-as-a-Service that extends existing techniques by supporting cross-communication between batch and stream processing, deployment on multiple platforms, and end-to-end verification against users' requirements.

Modern hearing aids are not simple passive sound enhancers, but rather complex devices that can log (via smartphones) multivariate real-time data from the acoustic environment of a user. In the evotion project (http://h2020evotion.eu) such hearing aids are integrated with a Big Data analytics platform to bring about ecologically valid evidence to support the hearing healthcare sector. Here, we present the background of the Big Data analytics platform and demonstrate that modeling of longitudinally sampled data from hearing aids can support clinical investigations with hypotheses about hearing aid usage prognosis, and support public health decision-making within the hearing healthcare sector by simulation techniques. We found, that distinct characteristics of the acoustic environment significantly modulate how hearing impaired individuals use their hearing aids. Higher sound levels and an increased sound diversity but degraded signal quality all predicts more minutes of use per hour. By simulation, we show that a projected increase in the overall sound levels by 10dB followed by a 4dB increase in noise exposure will increase the need for hearing aid use by an additional 1 hour/day across a population of hearing impaired hearing aid users.

In this paper we propose a new architecture and methodology to define a collaborative environment aimed at supporting the innovation process. In the first part of this paper we analyse the data collected during an experiment for testing a collaborative environment and, in the second part, we propose an architecture to support and stimulate innovation processes. Our solution is based on three components, namely a (i) collaborative platform, (ii) a tool able to extract knowledge from shared documents, external data sources, and collaborative activities, and (iii) a recommender system. More specifically, we focus on how knowledge items are extracted from incoming knowledge flows to be proposed to a team, whose members are capable of proposing values for design issues and/or evaluating these choices from her own specific perspective. Furthermore, the aim of the proposed framework is not limited to the selection of relevant knowledge but, more broadly, on aligning the team on a restricted set of information items, producing a convergence of objectives that accelerates the kinetics of the collaborative work.

Technical enhancements of mobile technologies are paving the way to the definition of high-quality and accurate geolocation solutions based on data collected and managed by GSM/3G networks. We present a technique that provides geolocation and mobility prediction both at network and service level, does not require any change to the existing mobile network infrastructure, and is entirely performed on the mobile network side, making it more robust than other positioning systems with respect to location spoofing and other terminal-based security threats. Our approach is based on a novel database correlation technique over Received Signal Strength Indication (RSSI) data, and provides a geolocation and tracking technique based on advanced map- and mobility-based filtering. The performance of the geolocation algorithm has been carefully validated by an extensive experimentation, carried out on real data collected from the mobile network antennas of a complex urban environment.

This paper addresses the problem of edge restoration in digital images. Taking advantage of an ensemble approach, multiple type-1 fuzzy filters are combined to reach a decision. The fuzzy logic concept for linguistic variables and possibility theory is discussed with regard to knowledge representation and inference procedures. To improve conventional deinterlacing issues, we adopt type-1 fuzzy set concepts to design a weight-measuring approach. We demonstrate that the fuzzy ensemble approach model is well suited to image processing and provide case studies in the video-deinterlacing field. In our proposed method, five fuzzy membership functions (MFs) of linguistic variable-based fuzzy logic filters are derived from the type-1 (a.k.a. ordinary or primary) fuzzy MF. The weight-measuring process of our proposed model is used to assign weights to six candidate deinterlaced pixels (CDPs) that are interpolated according to edge direction. The use of a different MF for each direction allows the filter to characterize each pixel variation influence independently, according to its direction. The weights from all MFs are multiplied with the CDPs. The results of the empirical trials clearly show that the proposed system can successfully deal with several image types containing motion or detail elements.