1,721,134 research outputs found
Angela Dibenedetto, Stefania Fasciano, Antonella Colucci, Michele Aresta “Nanosized particles: questioned for their potential toxicity, but some are applied in biomedicine
No full textDistributed artificial intelligence for edge computing
Full text linkIn an increasingly wide range of work and personal life settings, sensors and micro-devices embedded into objects generate continuous data streams with high volume, velocity and heterogeneity. Such data can be analyzed to detect and infer knowledge about phenomenons and events of interest. By learning from data, Artificial Intelligence (AI) methods enable automating an ever larger amount of activities and decision-making tasks with comparable or better proficiency than human experts. Big Data applications based on pervasive Internet of Things (IoT) deployments are now a well-established reality: they feed large Machine Learning (ML) models, which are trained exploiting the huge computational resources of cloud computing infrastructures to offer increasingly accurate prediction capabilities on fresh data. However, the increasing miniaturization of IoT devices equipped with highly accurate sensors enables novel Cyber-Physical Systems (CPSs) with tight feedback loops coupling computation, communication and control tasks. CPS applications are expanding in sensitive fields like high-precision manufacturing, telemedicine, and self-driving vehicles. Those scenarios require real-time response, high computational and bandwidth efficiency, cost-effectiveness to support business scalability and strict data privacy constraints. For this reason, classical cloud-based approaches are progressively integrated with the Edge Computing (EC) architectural model, which distributes significant processing and storage resources at the edge of the local network, in closer proximity to field devices and sensors. This paradigm allows AI-based IoT applications to scale even more, as models are trained with massive amounts of data generated by large deployments of micro- and nano-devices, and ML inference achieves ever greater accuracy. In this context, the Edge Intelligence paradigm --which promotes the integration of EC and AI-- is increasingly adopted to execute inference on data at the border of local networks, employing models trained in the cloud. The next logical step in Cloud-Edge AI cooperation is to enable training tasks on edge nodes as well. However, as of now flexible approaches to combine Edge Intelligence with cloud infrastructures, allowing dynamic migration of training and inference tasks, are not available yet. This thesis proposes a Cloud-Edge AI microservice architecture, based on Osmotic Computing principles. A full pipeline consisting in data collection from local devices, preprocessing, AI model training and inferencing is supported either on the edge, on the cloud or both, exploiting computational resources opportunistically based on device status, available bandwidth, and application requirements on latency, prediction accuracy, and privacy. To demonstrate the feasibility of the proposed framework, a prototype has been realized with commodity hardware leveraging open-source software technologies. It has been used in a small-scale intelligent manufacturing case study, carrying out experiments on elapsed time and network activity in (i) data gathering, (ii) AI model training and validation, and (iii) prediction tasks. Obtained results validate the key benefits of the approach.
In addition to architectural aspects, open issues still limiting the full applicability of EC include the heterogeneity of devices, services, and information that arise in pervasive contexts, the integrity of the gathered data, and the trustworthiness and dependability of autonomous decisions. The Semantic Web of Things (SWoT), coalescing the Semantic Web and IoT paradigms, has been proposed to overcome these problems. In SWoT environments, the dynamic exchange of knowledge fragments expressed in logic-based formalisms in volatile wireless networks of independent agents enables decentralized collaborative service discovery, autonomous decision and user decision support. A relevant problem in such scenarios consists in evaluating agreements and disagreements about knowledge produced by different interacting agents, in order to possibly reconcile conflicts and determine the best overall outcome to accomplish distributed coordination. In AI, Argumentation is recognized as a powerful formalism to negotiate and solve disagreements within a group of agents, which convey knowledge represented as a constellation of arguments and counterarguments. The argumentation literature provides a wealth of frameworks for agent decision-making and coordination. Nevertheless, few proposals leverage Semantic Web languages and technologies, which can provide a well-known formal model for arguments, well-studied inference algorithms for the assessment of argument relations and approaches to evaluate argument acceptability. This thesis presents a novel Bipolar Weighted Argumentation Framework, where arguments are modeled as Description Logics concept expressions in Web Ontology Language (OWL) 2, and their relations are assessed via semantic matchmaking, leveraging non-standard inference services with logic-based outcome explanation. Argument acceptability is computed via a novel propagation-based ranking semantics, which supports argument cycles and information fading. In order to make the proposal suitable for pervasive semantic agents in resource-constrained devices, optimizations in argument assessment and ranking evaluation are adopted, while a graph simplification approach via pruning is proposed and experimentally tested as a tunable trade-off between computational resource usage and accuracy of results. Validation of the approach has been carried out by means of a prototypical implementation of a player agent for the StarCraft II real-time strategy game, whose environment allows simulating the complexities of real CPS scenarios
8. Nanosized particles: questioned for their potential toxicity, but some are applied in biomedicine
No full textN
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
A propagation-based ranking semantics in Explainable Bipolar Weighted Argumentation
No full textThe Semantic Web of Things enables exchanging knowledge-based fragments in pervasive computing through machine-to-machine interactions, sustaining collaborative decision-making in networks of smart objects. Abstract argumentation is widely acknowledged, instead, as a general framework for decision-making in multi-agent systems, able to solve negotiation conflicts and evaluate acceptable options for a given scenario. Nevertheless, abstract argumentation frameworks disregard the problem of characterizing the argument structure and their mutual relations. This paper proposes a novel approach integrating Dung-style abstract argumentation with semantic matchmaking in pervasive computing. Object interactions are seen as an argumentative dialogue, where annotations shared by each agent play the role of arguments. A matchmaking scheme, exploiting non-standard non-monotonic inferences, allows the appraisal of argument relations in a bipolar weighted argumentation approach. A propagation-based gradual semantics provides acceptability ranking of arguments with a formal explanation. A fading mechanism is also exploited to take into account limited computational resources. As the proposed approach enables applications in cyber-physical multi-agent systems engineering, early validation and performance experiments are provided by means of a case study on a real-time strategy game
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Caratterizzazione istologica e biochimica di tuberi di Nephrolepis cordifolia (L.) Presl
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