Beyond disciplinary frontiers: The value of the history of science in teaching

“The legitimate, safe, fruitful method, capable of preparing a mind to accept a physical hypothesis is the historical one”, argued the French theoretical physicist Pierre Duhem, in La th ́eorie physique: son objet et sa structure (1906). Starting from this position of thought, with this contribution we want to broaden our gaze to try to briefly outline an “overall conception” of the history of science, in the light of its possible declinations and its epistemological implications, beyond the disciplinary frontiers, to foster a true education of the mind or, better, to understand, according to the French style, the formative value of a philosophical history of science

Towards Adaptive Flow Programming for the IoT: The Fluidware Approach

The objective of this position paper is to present Fluidware, a proposal towards an innovative programming model for the IoT, conceived to ease the development of flexible and robust large-scale IoT services and applications. The key innovative idea of Fluidware is to abstract collectives of devices of the IoT fabric as sources, digesters, and targets of distributed 'flows' of contextualized events, carrying information about data produced and actuating commands. Accordingly, programming services and applications implies declaratively specifying 'funnel processes' to channel, elaborate, and re-direct such flows in a fully-distributed way, as a means to coordinate the activities of devices and realize services and applications. The potential applicability of Fluidware and its expected advantages are exemplified via example in the area of ambient assisted living

A toxicogenomic data space for system-level understanding and prediction of EDC-induced toxicity.

Endocrine disrupting compounds (EDCs) are a persistent threat to humans and wildlife due to their ability to interfere with endocrine signaling pathways. Inspired by previous work to improve chemical hazard identification through the use of toxicogenomics data, we developed a genomic-oriented data space for profiling the molecular activity of EDCs in an in silico manner, and for creating predictive models that identify and prioritize EDCs. Predictive models of EDCs, derived from gene expression data from rats (in vivo and in vitro primary hepatocytes) and humans (in vitro primary hepatocytes and HepG2), achieve testing accuracy greater than 90%. Negative test sets indicate that known safer chemicals are not predicted as EDCs. The rat in vivo-based classifiers achieve accuracy greater than 75% when tested for invitro to in vivoextrapolation. This study reveals key metabolic pathways and genes affected by EDCs together with a set of predictive models that utilize these pathways to prioritize EDCs in dose/time dependent manner and to predict EDCevokedmetabolic diseases

Towards Collective Sentiment Analysis in IoT-Enabled Scenarios

An interesting and innovative activity in Collective Intelligence systems is Sentiment Analysis (SA) which, starting from users' feedback, aims to identify their opinion about a specific subject, for example in order to develop/improve/customize products and services. The feedback gathering, however, is complex, time-consuming, and often invasive, possibly resulting in decreased truthfulness and reliability for its outcome. Moreover, the subsequent feedback processing may suffer from scalability, cost, and privacy issues when the sample size is large or the data to be processed is sensitive. Internet of Things (IoT) and Edge Intelligence (EI) can greatly help in both aspects by providing, respectively, a pervasive and transparent way to collect a huge amount of heterogeneous data from users (e.g., audio, images, video, etc.) and an efficient, low-cost, and privacy-preserving solution to locally analyze them without resorting to Cloud computing-based platforms. Therefore, in this paper we outline an innovative collective SA system which leverages on IoT and EI (specifically, TinyML techniques and the EdgeImpulse platform) to gather and immediately process audio in the proximity of entities-of-interest in order to determine whether audience' opinions are positive, negative, or neutral. The architecture of the proposed system, exemplified in a museum use case, is presented, and a preliminary, yet very promising, implementation is shown, reveling interesting insights towards its full development

A Methodology and Simulation-Based Toolchain for Estimating Deployment Performance of Smart Collective Services at the Edge

Research trends are pushing artificial intelligence (AI) across the Internet of Things (IoT)-edge-fog-cloud continuum to enable effective data analytics, decision making, as well as the efficient use of resources for QoS targets. Approaches for collective adaptive systems (CASs) engineering, such as aggregate computing, provide declarative programming models and tools for dealing with the uncertainty and the complexity that may arise from scale, heterogeneity, and dynamicity. Crucially, aggregate computing architecture allows for 'pulverization': applications can be decomposed into many deployable micromodules that can be spread across the ICT infrastructure, thus allowing multiple potential deployment configurations for the same application logic. This article studies the deployment architecture of aggregate-based edge services and its implications in terms of performance and cost. The goal is to provide methodological guidelines and a model-based toolchain for the generation and simulation-based evaluation of potential deployments. First, we address this subject methodologically by proposing an approach based on deployment code generators and a simulation phase whose obtained solutions are assessed with respect to their performance and costs. We then tailor this approach to aggregate computing applications deployed onto an IoT-edge-fog-cloud infrastructure, and we develop a corresponding toolchain based on Protelis and EdgeCloudSim. Finally, we evaluate the approach and tools through a case study of edge multimedia streaming, where the edge ecosystem exhibits intelligence by self-organizing into clusters to promote load balancing in large-scale dynamic settings

Situation identification in smart wearable computing systems based on machine learning and Context Space Theory

Wearable devices and smart sensors are increasingly adopted to monitor the behaviors of human and artificial agents. Many applications rely on the capability of such devices to recognize daily life activities performed by the monitored users in order to tailor their behaviors with respect to the occurring situations. Despite the constant evolution of smart sensing technologies and the numerous research in this field, an accurate recognition of in-the-wild situations still represents an open research challenge. This work proposes a novel approach for situation identification capable of recognizing the activities and the situations in which they occur in different environments and behavioral contexts, processing data acquired by wearable and environmental sensors. An architecture of a situation-aware wearable computing system is proposed, inspired by Endsley's situation-awareness model, consisting of a two-step approach for situation identification. The approach first identifies the daily life activities via a learning-based technique. Simultaneously, the context in which the activities are performed is recognized using Context Space Theory. Finally, the fusion between the context state and the activities allows identifying the complex situations in which the user is acting. The knowledge regarding the situations forms the basis on which novel and smarter applications can be realized. The approach has been evaluated on the ExtraSensory public dataset and compared with state-of-the-art techniques, achieving an accuracy of 96% for the recognition of situations and with significantly low computational time, demonstrating the efficacy of the two-step situation identification approach

STAT3 Impairs STAT5 Activation in the Development of IL-9-Secreting T Cells

Th cell subsets develop in response to multiple activating signals, including the cytokine environment. IL-9-secreting T cells develop in response to the combination of IL-4 and TGF-β, although they clearly require other cytokine signals, leading to the activation of transcription factors including STAT5. In Th17 cells, there is a molecular antagonism of STAT5 with STAT3 signaling, although whether this paradigm exists in other Th subsets is not clear. In this paper, we demonstrate that STAT3 attenuates the ability of STAT5 to promote the development of IL-9-secreting T cells. We demonstrate that production of IL-9 is increased in the absence of STAT3 and cytokines that result in a sustained activation of STAT3, including IL-6, have the greatest potency in repressing IL-9 production in a STAT3-dependent manner. Increased IL-9 production in the absence of STAT3 correlates with increased endogenous IL-2 production and STAT5 activation, and blocking IL-2 responses eliminates the difference in IL-9 production between wild-type and STAT3-deficient T cells. Moreover, transduction of developing Th9 cells with a constitutively active STAT5 eliminates the ability of IL-6 to reduce IL-9 production. Thus, STAT3 functions as a negative regulator of IL-9 production through attenuation of STAT5 activation and function

SAJaS: enabling JADE-based simulations

Multi-agent systems (MAS) are widely acknowledged as an appropriate modelling paradigm for distributed and decentralized systems, where a (potentially large) number of agents interact in non-trivial ways. Such interactions are often modelled defining high-level interaction protocols. Open MAS typically benefit from a number of infrastructural components that enable agents to discover their peers at run-time. On the other hand, multi-agent-based simulations (MABS) focus on applying MAS to model complex social systems, typically involving a large agent population. Several MAS development frameworks exist, but they are often not appropriate for MABS; and several MABS frameworks exist, albeit sharing little with the former. While open agent-based applications benefit from adopting development and interaction standards, such as those proposed by FIPA, MABS frameworks typically do not support them. In this paper, a proposal to bridge the gap between MAS simulation and development is presented, including two components. The Simple API for JADE-based Simulations (SAJaS) enhances MABS frameworks with JADE-based features. While empowering MABS modellers with modelling concepts offered by JADE, SAJaS also promotes a quicker development of simulation models for JADE programmers. In fact, the same implementation can, with minor changes, be used as a large scale simulation or as a distributed JADE system. In its current version, SAJaS is used in tandem with the Repast simulation framework. The second component of our proposal consists of a MAS Simulation to Development (MASSim2Dev) tool, which allows the automatic conversion of a SAJaS-based simulation into a JADE MAS, and vice-versa. SAJaS provides, for certain kinds of applications, increased simulation performance. Validation tests demonstrate significant performance gains in using SAJaS with Repast when compared with JADE, and show that the usage of MASSim2Dev preserves the original functionality of the system. © Springer-Verlag Berlin Heidelberg 2015

PTHrP on MCF-7 breast cancer cells: a growth factor or an antimitogenic peptide?

Letter to the Edito