1,720,974 research outputs found
TinySV: Speaker Verification in TinyML with On-device Learning
Get PDFTinyML is a novel area of machine learning that gained huge momentum in the last few years thanks to the ability to execute machine learning algorithms on tiny devices (such as Internet-of-Things or embedded systems). Interestingly, research in this area focused on the efficient execution of the inference phase of TinyML models on tiny devices, while very few solutions for on-device learning of TinyML models are available in the literature due to the relevant overhead introduced by the learning algorithms.
The aim of this paper is to introduce a new type of adaptive TinyML solution that can be used in tasks, such as the presented Tiny Speaker Verification (TinySV), that require to be tackled with an on-device learning algorithm. Achieving this goal required (i) reducing the memory and computational demand of TinyML learning algorithms, and (ii) designing a TinyML learning algorithm operating with few and possibly unlabelled training data. The proposed TinySV solution relies on a two-layer hierarchical TinyML solution comprising Keyword Spotting and Adaptive Speaker Verification module. We evaluated the effectiveness and efficiency of the proposed TinySV solution on a dataset collected expressly for the task and tested the propos
To Personalize or Not To Personalize? Soft Personalization and the Ethics of ML for Health
No full textPersonalization is among the most promising outcomes of using Machine Learning models that can be trained on data representing a specific individual. Personalization is particularly promising in areas such as health and medicine, as several crucial aspects and determinants of health are individual. Yet additional ethical issues arise with increasingly personalized models, including privacy, acceptability, reliability and trade-offs. In this paper we discuss and propose ML models for health that can be personalized on individual users, while guaranteeing both their privacy and quality from an ethical and epistemic (knowledge-related) point of view. To achieve these goals, we argue that we need to control the learning and evolution of personalized models. We propose soft personalization as an ethicallyinformed framework to limit personalization and respect epistemic and ethical values that are specific for the health context, including representativity, quality, non-maleficence, beneficence, privacy. Based on an interdisciplinary approach combining the philosophical and computer science scholarship of our group, soft personalization is a way of developing different models that can be selected depending on their quality and safety. We characterize the approach theoretically and technically and make it concrete with a case study of glucose monitoring and anomaly detection through privacy-preserving ML. Our framework shows that, even when individual issues such as privacy can be mitigated, tradeoffs with other values remain and choices are necessary as to which values should be prioritized
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe 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
Variations on the Author
Get PDF“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
Unveiling the Potential of Tiny Machine Learning for Enhanced People Counting in UWB Radar Data
Get PDFTiny Machine Learning (TinyML) allows to move the intelligence processing as close as possible to where data are generated, hence reducing the latency with which a decision is made and being able to process data even when remote connection is scarce or absent. In this technological scenario, Ultra-Wideband (UWB) radar data represent a new and challenging source of data providing relevant information, while guaranteeing the privacy of users. This paper introduces a novel TinyML solution able to count the number of people in a given area by processing UWB radar data. This novel solution was carefully designed to guarantee a high counting accuracy, while reducing the memory and computational demand so as to be executed on tiny devices. Experimental results on a real-world UWB radar dataset show the effectiveness of the proposed solution
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe 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
StreamTinyNet: video streaming analysis with spatial-temporal TinyML
Get PDFTiny Machine Learning (TinyML) is a branch of Machine Learning (ML) that constitutes a bridge between the ML world and the embedded system ecosystem (i.e., Internet of Things devices, embedded devices, and edge computing units), enabling the execution of ML algorithms on devices constrained in terms of memory, computational capabilities, and power consumption. Video Streaming Analysis (VSA), one of the most interesting tasks of TinyML, consists in scanning a sequence of frames in a streaming manner, with the goal of identifying interesting patterns. Given the strict constraints of these tiny devices, all the current solutions rely on performing a frame-by-frame analysis, hence not exploiting the temporal component in the stream of data. In this paper, we present StreamTinyNet, the first TinyML architecture to perform multiple-frame VSA, enabling a variety of use cases that requires spatial-temporal analysis that were previously impossible to be carried out at a TinyML level. Experimental results on public-available datasets show the effectiveness and efficiency of the proposed solution. Finally, StreamTinyNet has been ported and tested on the Arduino Nicla Vision, showing the feasibility of what proposed.this paper has been accepted and presented at the WCCI24 conferenc
TyBox: an automatic design and code-generation toolbox for TinyML incremental on-device learning
Get PDFIncremental on-device learning is one of the most relevant and interesting challenges in the field of Tiny Machine Learning (TinyML). Indeed, differently from traditional TinyML solutions where the training is typically carried out on the Cloud and inference only occurs on the tiny devices (e.g., embedded systems or Internet-of-Things units), incremental on-device TinyML allows both the inference and the training of TinyML models directly on tiny devices.
This ability paves the way for TinyML-enabled intelligent devices that can learn directly on the field and adapt to evolving environments, different working conditions, or specific users. The literature in this field is quite limited with very few solutions focusing only on the incremental fine-tuning of machine learning models, whereas a general solution encompassing algorithms and code generation for incremental on-device TinyML is still perceived as missing.
The aim of this article is to introduce, to the best of our knowledge for the first time in the literature, a toolbox called TyBox for the automatic design and code generation of incremental on-device TinyML classification models. In more detail, starting from a " static" TinyML model, TyBox is able to (i) automatically design the "incremental" on-device version of the TinyML model that has been suitably designed to take into account the technological constraint on the RAM memory of the target tiny device, and (ii) autonomously provide the C++ codes and libraries to support the inference and learning of the incremental on-device TinyML model directly on the tiny devices.
TyBox has been extensively compared with a state-of-the-art incremental learning solution for TinyML and tested on an off-the-shelf tiny device (i.e., the Arduino Nano 33 BLE) in three relevant TinyML application tasks and scenarios: binary image classification, multi-class image classification, and ultra-wide-band human activity recognition. In addition, TyBox is released to the scientific community as a public repository
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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