Touchscreen-based music education tools typically rely on audio and visual feedback, despite the inherently sensorimotor nature of musical learning.Recent advances in surface haptics enable programmable tactile feedback on flat displays, opening new possibilities for multisensory interaction.This paper explores the use of TanvasTouch technology in music education, focusing on design patterns, pedagogical applications, and accessibility.We developed interactive activities using tactile textures to represent musical shapes and conducted an exploratory study with primary school students (ages 6-11).Participants engaged in guided touch tasks to recognize simple musical symbols and instrument shapes, allowing us to assess recognition, engagement, and usability.Results show that surface haptics can support exploratory learning experiences, enhancing traditional audio-visual approaches.However, blind shape recognition was largely unsuccessful, indicating that the technology cannot yet function as a standalone modality for visually impaired users.Instead, TanvasTouch is better suited as a complementary multisensory tool in inclusive educational settings.

Abstract In networked music performance (NMP) applications, which entail real-time audio streaming over the Internet, strict latency requirements are needed to ensure a realistic interaction between geographically dispersed musicians. Thus, NMP applications typically leverage uncompressed audio and unreliable transport protocols to avoid unnecessary processing and re-transmission delays. Given that no guarantee on packet delivery is offered, NMP applications must deal with late/lost audio packets to mitigate the impact of the resulting audio artifacts on the quality of the playback audio stream. This paper explores an audio packet loss concealment (PLC) technique based on autoregressive (AR) models. In particular, it investigates the algorithmic implementation of Burg’s method and the parameters configuration that offers the best trade-off between prediction error and computational time requirements. The purpose is to find the most suitable solution capable of running on a Raspberry Pi 4B within the real-time audio boundaries imposed by NMP applications. Additionally, we analyze the computational time required to fit the model and predict future samples by considering six implementations and various compilation flags. Results confirm that AR models can predict future audio samples more accurately than traditional PLC approaches, which consist of filling audio gaps with silence or repeating the last received audio segment. Furthermore, results demonstrate the effectiveness of the proposed solution in meeting the strict latency requirements when deployed on a Raspberry Pi 4B.

Current sound-based practices and systems developed in both academia and industry point to convergent research trends that bring together the field of Sound and Music Computing with that of the Internet of Things. This paper proposes a vision for the emerging field of the Internet of Sounds (IoS), which stems from such disciplines. The IoS relates to the network of Sound Things, i.e., devices capable of sensing, acquiring, processing, actuating, and exchanging data serving the purpose of communicating sound-related information. In the IoS paradigm, which merges under a unique umbrella the emerging fields of the Internet of Musical Things and the Internet of Audio Things, heterogeneous devices dedicated to musical and non-musical tasks can interact and cooperate with one another and with other things connected to the Internet to facilitate sound-based services and applications that are globally available to the users. We survey the state of the art in this space, discuss the technological and non-technological challenges ahead of us and propose a comprehensive research agenda for the field.

In the past two decades, we have witnessed the diffusion of an increasing number of technologies, products, and applications at the intersection of music and networking. As a result of the growing attention devoted by academy and industry to this area, three main research fields have emerged and progressively consolidated: the Networked Music Performances, Ubiquitous Music, and the Internet of Musical Things. Based on the review of the most relevant works in these fields, this paper attempts to delineate their differences and commonalities. The aim of this inquiry is helping avoid confusion between such fields and achieve a correct use of the terminology. A trend towards the convergence between such fields has already been identified, and it is plausible to expect that in the future their evolution will lead to a progressive blurring of the boundaries identified today.

Following the emergence of Elastic Optical Networks (EONs), Machine Learning (ML) has been intensively investigated as a promising methodology to address complex network management tasks, including, e.g., Quality of Transmission (QoT) estimation, fault management, and automatic adjustment of transmission parameters. Though several ML-based solutions for specific tasks have been proposed, how to integrate the outcome of such ML approaches inside Routing and Spectrum Assignment (RSA) models (which address the fundamental planning problem in EONs) is still an open research problem. In this study, we propose a dual-stage iterative RSA optimization framework that incorporates the QoT estimations provided by a ML regressor, used to define lightpaths’ reach constraints, into a Mixed Integer Linear Programming (MILP) formulation. The first stage minimizes the overall spectrum occupation, whereas the second stage maximizes the minimum inter-channel spacing between neighbor channels, without increasing the overall spectrum occupation obtained in the previous stage. During the second stage, additional interference constraints are generated, and these constraints are then added to the MILP at the next iteration round to exclude those lightpaths combinations that would exhibit unacceptable QoT. Our illustrative numerical results on realistic EON instances show that the proposed ML-assisted framework achieves spectrum occupation savings up to 52.4% (around 33% on average) in comparison to a traditional MILP-based RSA framework that uses conservative reach constraints based on margined analytical models.

The distribution of video contents generated by Content Providers (CPs) significantly contributes to increase the congestion within the networks of Internet Service Providers (ISPs). To alleviate this problem, CPs can serve a portion of their catalogues to the end users directly from servers (i.e., the caches) located inside the ISP network. Users served from caches perceive an increased QoS (e.g., average retrieval latency is reduced) and, for this reason, caching can be considered a form of traffic prioritization. Hence, since the storage of caches is limited, its subdivision among several CPs may lead to discrimination. A static subdivision that assignes to each CP the same portion of storage is a neutral but ineffective appraoch, because it does not consider the different popularities of the CPs' contents. A more effective strategy consists in dividing the cache among the CPs proportionally to the popularity of their contents. However, CPs consider this information sensitive and are reluctant to disclose it. In this work, we propose a protocol based on Shamir Secret Sharing (SSS) scheme that allows the ISP to calculate the portion of cache storage that a CP is entitled to receive while guaranteeing network neutrality and resource efficiency, but without violating its privacy. The protocol is executed by the ISP, the CPs and a Regulator Authority (RA) that guarantees the actual enforcement of a fair subdivision of the cache storage and the preservation of privacy. We perform extensive simulations and prove that our approach leads to higher hit-rates (i.e., percentage of requests served by the cache) with respect to the static one. The advantages are particularly significant when the cache storage is limited.

Machine Learning (ML) is under intense investigation in optical networks as it promises to lead to automation of a variety of management tasks, as amplifier gain equalization, fault recognition, Quality of Transmission (QoT) estimation, and many others. Though several studies focus on each of these specific tasks, the integration of ML-based estimations inside Routing and Spectrum Assignment (RSA) is still largely unexplored.This paper moves towards such integration. We develop a framework that leverages the probabilistic outputs of a ML-based QoT estimator to define the reach constraints in an Integer Linear Programming (ILP) formulation for RSA in an elastic optical network. In this integrated procedure, the RSA problem is solved iteratively by updating the reach constraints based on the outcome of a QoT estimator, to exclude lightpaths with unacceptable QoT. In our numerical evaluation, the proposed integrated method achieves savings in spectrum occupation up to 30% (around 20% on average) compared to traditional ILP-based RSA approaches with reach constraints based on margined analytical models.

Multicore fibers (MCFs) are one of the main technological enablers for space-division multiplexing. In principle, MCFs could scale the fiber capacity by a factor equal to the number of cores, but in practice such increase is hindered by transmission impairments due to the inter-core crosstalk between adjacent lit cores. The entity of such crosstalk depends on the number of cores and on their disposition within the fiber cladding, and also on the baud rate and modulation format used for transmission. As first MCF applications are expected over point-to-point systems, in this paper we concentrate on the resource allocation over a single link. Specifically, we study the Baud rate, Modulation format, Core and Spectrum Assignment problem in a multicore flexi-grid link, considering distance-adaptive reaches for different baud rates, modulation formats, and crosstalk impairments. We show that the problem is NP-hard and provide two integer linear programs, as well as heuristic approaches to solve it over large/practical traffic instances. Our problem formulations incorporate modeling of the exact inter-core crosstalk contributions depending on the number of lit neighbor cores. Numerical results are provided in a high-spatial-efficiency 19-core fiber considering different transmission impairment conditions.

The possibility of flexibly assigning spectrum resources with channels of different sizes greatly improves the spectral efficiency of optical networks, but can also lead to unwanted spectrum fragmentation. We study this problem in a scenario where traffic demands are categorized in two types (low or high bit-rate) by assessing the performance of three allocation policies. Our first contribution consists of exact Markov chain models for these allocation policies, which allow us to numerically compute the relevant performance measures. However, these exact models do not scale to large systems, in the sense that the computations required to determine the blocking probabilities-which measure the performance of the allocation policies-become intractable. In order to address this, we first extend an approximate reduced-state Markov chain model that is available in the literature to the three considered allocation policies. These reduced-state Markov chain models allow us to tractably compute approximations of the blocking probabilities, but the accuracy of these approximations cannot be easily verified. Our main contribution then is the introduction of reduced-state imprecise Markov chain models that allow us to derive guaranteed lower and upper bounds on blocking probabilities, for the three allocation policies separately or for all possible allocation policies simultaneously.

Abstract Measurements gathered by smart metres and collected through the automatic metering infrastructure of smart grids can be accessed by numerous external subjects for different purposes, ranging from billing to grid monitoring. Therefore, to prevent the disclosure of personal information through the analysis of energy consumption patterns, the metering data must be securely handled. Peer‐to‐peer networking is a promising approach for interconnecting communication nodes among the automatic metering infrastructure to efficiently perform data collection while ensuring privacy and confidentiality, but it is also prone to various security attacks. This paper discusses the impact of the most relevant peer‐to‐peer attack scenarios on the performance of a protocol for privacy preserving aggregation of metering data. The protocol relies on communication gateways located in the customers’ households and interconnected by means of a variant of the Chord overlay. We also propose some countermeasures to mitigate the effects of such attacks: we integrate a verifiable secret sharing scheme based on Pedersen commitments in the aggregation protocol, which ensures data integrity, with compliance checks aimed at identifying the injection of altered measurements. Moreover, we introduce Chord auxiliary routing tables to counteract the routing pollution performed by dishonest nodes. The paper evaluates the computational complexity and effectiveness of the proposed solutions through analytical and numerical results. Copyright © 2014 John Wiley & Sons, Ltd.

The privacy-preserving management of energy consumption measurements gathered by Smart Meters plays a pivotal role in the Automatic Metering Infrastructure of Smart Grids. Grid users and standardization committees are requiring that utilities and third parties collecting aggregated metering data are prevented from accessing measurements at the household granularity, and data perturbation is a technique used to provide a trade-off between the privacy of individual users and the precision of the aggregated measurements. In this paper, we discuss a decisional attack to aggregation with data-perturbation, showing that a curious entity can exploit the temporal correlation of Smart Grid measurements to detect the presence or absence of individual data generated by a given user inside an aggregate. We also propose a countermeasure to such attack and show its effectiveness using both synthetic and real home energy consumption measurement traces.

The widespread deployment of Automatic Metering Infrastructures in Smart Grid scenarios rises great concerns about privacy preservation of user-related data, from which detailed information about customer's habits and behaviours can be deduced. Therefore, the users' individual measurements should be aggregated before being provided to External Entities such as utilities, grid managers and third parties. This paper proposes a security architecture for distributed aggregation of smart metering data relying on Gateways placed at the customers' premises, which collect the data generated by local Meters and provide communication and cryptographic capabilities. We propose a secure communication protocol based on multiparty computation aimed at preventing Gateways and External Entities from inferring information about individual data. The routing of information flows can be centralized or it can be performed in a distributed fashion using a protocol similar to Chord.

The widespread deployment of Automatic Metering Infrastructures in Smart Grid scenarios rises great concerns about privacy preservation of user-related data, from which detailed information about customer's habits and behaviors can be deduced. Therefore, the users' individual measurements should be aggregated before being provided to External Entities such as utilities, grid managers and third parties. This paper proposes a security architecture for distributed aggregation of additive data, in particular energy consumption metering data, relying on Gateways placed at the customers' premises, which collect the data generated by local Meters and provide communication and cryptographic capabilities. The Gateways communicate with one another and with the External Entities by means of a public data network. We propose a secure communication protocol aimed at preventing Gateways and External Entities from inferring information about individual data, in which privacy-preserving aggregation is performed by means of a cryptographic homomorphic scheme. The routing of information flows can be centralized or it can be performed in a distributed fashion using a protocol inspired by Chord. We compare the performance of both approaches to the optimal solution minimizing the data aggregation delay.