451 research outputs found
Unsupervised human process discovery in smart homes
No full textThe advances in the Internet of Things (IoT) have enabled the automation of various tasks like switching on the heating at home from work, seeing who is at your front door from the couch, supporting nurses in elderly homes, or the efficient delivery of packages. By enabling the connection between the physical and digital worlds, the IoT has shown how environments can be augmented with technology to enhance their capabilities, making them more intelligent, responsive, and adaptive. This widespread adoption of embedded systems turned pervasive (or ubiquitous) computing into reality: while sensors gather real-time data about the environment, actuators are used to automate the execution of many tasks that help the users of such environments. These environments, referred to as smart environments or smart spaces, represent an emerging class of IoT-based applications and are centered on their human users. Among smart spaces, smart homes and offices are representative examples. The goal is to enhance the quality of life, improve productivity, and provide personalized services by understanding and responding to the needs and preferences of the users, realizing the paradigm known as Ambient Intelligence (AmI). The literature presents various definitions of AmI systems and a set of distinct features that characterize them: sensitivity, responsiveness, adaptivity, ubiquity, and transparency. Sensitivity pertains to the AmI system's ability to perceive and comprehend the surrounding environment and its interaction context. Responsiveness and adaptivity, closely tied to sensitivity, indicate the system's capacity to promptly react, either proactively or reactively, to changes in the context in accordance with user preferences. Collectively, sensitivity, responsiveness, and adaptivity contribute to the overarching concept of context awareness. Lastly, the terms ubiquity and transparency directly relate to the idea of pervasive computing. Smart environments process and analyze the data collected from sensors to extract meaningful information. In this context, AmI is realized by utilizing techniques such as machine learning, artificial intelligence, and human-computer interaction (HCI). The rich data automatically collected via IoT sensors in smart spaces is used to get insights about the human behavior of the user (e.g., sleep tracking) or to perform automated actions for the user (e.g., automatically opening the blinds). For instance, current applications of human behavior monitoring in smart spaces include smart thermostats (e.g., Google Nest Learning Thermostat) and ambient assisted living (e.g., elderly fall detection systems). Modeling human activities and habits is not a simple task, due to the flexible and unstructured nature of human behavior. Recently, although it is still difficult to represent them following a precise flow of tasks, approaches have been proposed that model human habits as workflows. In particular, the research community and manufacturers have shown a great interest in applying process mining (PM) to smart spaces. Process mining is a fairly recent research discipline that combines data mining techniques with techniques used in Business Process Management (BPM), such as process modeling and process analysis. Process mining aims to extract, monitor, and improve processes based on real-world data. In particular, process discovery is a process mining technique used to discover and generate the process model describing the underlying behavior shown in the event log. The mined process model can be visualized in different forms, such as Petri nets, process flowcharts, or BPMN diagrams. Visualization helps to understand the structure and dynamics of processes within the smart space. However, even though process models could be extracted from smart space data, multiple important challenges arose. This thesis presents an overview of how some of the aforementioned research challenges are handled and to what degree they are addressed by the author
Un canzoniere petrarchesco nelle «Ricordanze» di Lorenzo Guidetti
No full textQuesto saggio descrive e studia il ms. segnato ASFi, Carte Strozziane, s. III, 291. Si tratta del Canzoniere di Francesco Petrarca sottoscritto il 22 gennaio 1464 ab incarnatione da Lorenzo di Francesco Guidetti, allievo di Cristoforo Landino. La seriazione dei Fragmenta è ricostruita e analizzata attraverso la doppia numerazione dei componimenti che il codice tramanda (soggetta nelle ultime carte a numerosi interventi correttori) e attraverso due tavole incipitarie, entrambe parziali, trascritte integralmente: l’orizzonte che si individua è quello dell’editio variorum. La sezione conclusiva del saggio è dedicata all'edizione e discussione delle chiose al testo. Si dà inoltre notizia e si descrive un inedito libro di «Ricordanze», autografo di Lorenzo Guidetti, rintracciato in un archivio privato (Firenze, Archivio Michon Pecori), da cui si ricavano nuove informazioni sulla preziosa attività di copista del Guidetti e nello specifico sul canzoniere e i Trionfi di Petrarca. Fra gli elementi d'interesse si indica il codice donato a Giuliano di Piero di Cosimo de’ Medici (Plut. 63.22) e lo zibaldone (Acquisti e Doni 82) della Biblioteca Medicea Laurenziana. Si registra inoltre, e si colloca fra le prime attestazioni, l’uso di frottola e canzoniere. Si riconosce la mano di Lorenzo Guidetti nel laurenziano Gadd. rel. 94 (rubrica e rare chiose) e nelle postille alla princeps del commento ai Trionfi di Bernardo Lapini (BNCF, B 2 10). Si segnala infine un secondo codice pertrarchesco, sottrattosi fino ad oggi ai pur sistematici censimenti e ricognizioni, conservato presso l'Archivio di Stato di Firenze (ASFI, Cerchi, 751), contenente Canzoniere e Trionfi di Petrarca, e appartenuto a Bindaccio de' Cerchi:
The essay focuses on the ms. ASFI, Carte Strozziane, s. III, 291, a copy of Petrarch’s Canzoniere, transcribed by Lorenzo di Francesco Guidetti, a disciple of Cristoforo Landino, and dated ab incarnatione 22 January 1464. The ms. is acephalous and mutilated, but the amount and the order of the 'Rime sparse' are reconstructed and analysed through the dual numbering of the poems (subject to numerous corrections in the last leaves: the context is that of the editio variorum) and through the two tables of incipits, both partial. In the margin of the text the copyist added several annotations to the poems, some of which are edited and discussed. The A. also remarked the occurrences of the words 'frottola' (in relation with Rvf 105) and 'Canzoniere' (as title of the poems collection): this last appears in an unpublished book of 'Ricordanze', in Guidetti’s own hand (Firenze, Archivio Michon Pecori), that provides new informations about the circulation of Petrarch’s 'Canzoniere' and 'Trionfi', and on the background of Guidetti’s valuable work as a copyist (e.g. the codex given to Giuliano di Piero di Cosimo de’ Medici, BML 63.22, and the zibaldone Acquisti e Doni 82 of the same library): his hand is recognised in the BML Laurentian Gadd. rel. 94 and in the glosses to the princeps of Ilicino’s commentary on the Trionfi (BNCF, B 2 10)
An Integration Gateway for Sensing Devices in Smart Environments
No full textSmart Environments, and in particular Smart Homes, have recently attracted the attention of many
researchers and industrial vendors. The proliferation of low-power sensing devices requires
integration gateways hiding the complexity of heterogeneous technologies. We propose a ZigBee
integration gateway to access and integrate low-power ZigBee devices
Service discovery in mobile social networks
No full textWe present a new service discovery algorithm, termed SIDEMAN, which considers human mobility for service dissemination and discovery. SIDEMAN takes advantage of mobile social networking characteristics, such as user membershIP to a restricted number of communities and interest for similar services among users in the same community. We evaluated the performance of SIDEMAN via simulations in a scenario based on traces collected at the IEEE conference Infocom in 2006. Our algorithm has been compared to the social version of two popular data dissemination techniques, namely, flooding and gossIPing. We have measured how proactive an algorithm is in distributing services of interest (Recall), how many services are already with a user when they are needed (Gain), the energy cost for service discovery, and the time needed to reply a service query. We show that SIDEMAN obtains perfect Recall and a Gain that is always comparable to that of the other algorithms. Furthermore, most services are retrieved in reasonable time and at a lower energy cost than that of the flooding and gossIPing-based solutions
Fault-Diagnosis of Grid Structures
No full textTheprobl] offaul diagnosis in grid-connected systems is considered. A diagnosisalnosisFI caln DAGS and based on the PMCmodel is presented. DAGS provides a diagnosis which is shown to be correct, alrect, possibl incomplB[I if thecardinalq; of theactual faul set isbelB a bound T # , dependent of theactual syndrome #. A bound T independent of # is alF derived by a worst-caseanalt-c covering the cases oftriangul;; square,hexagonal and octagonal grids. T is shown to be #(n ), where n is the size of the system, for al the grids considered. c 2002El2FB#I Science B.V.Al rights reserved. Keywords: FauleDFBVU#IFqV Faul diagnosis;System-l;FV diagnosis;Paralos architectures 1. I363S-222 Faul diagnosis is of primary importance to provide highdependabilBk incomplE systems. It aims at identifying the(faul] ornon-faulB] state of the units composing a system. Upon identi#cation,fault units may be eitherreplrF; orisolEIB from the rest of the system, andfaul recovery or recon#guration techniques may be used to restore a coherent state,alte,FB the system to resume operation,possibl with reduced performance (graceful degradation). System-l-T( diagnosis was introduced by Preparata et al [14] and has beendeepl investigated in lBI#]VFqBE It aims at diagnosing systems composed by units(usualE processors), with the requirement that they are abl to test each other by exchanging information through point-to-pointbi-directional li-di A system is represented by the system graph G=(N; L), an undirected graph where node set N represents units and # Corresponding author. Instituto diElB;;BFq;E] del;;BFq;E]BFl; del CNR, Via S. Maria 46, 56126, Pisa,ItalB E-mail address: [email protected] (S. Chessa). 0304-3975/02/$ - see front matter c 2002El2FVVU Science B.V.Al rights reserved. PII: S0304-3975(..
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