1,720,977 research outputs found
Quantum inspired approach for early classification of time series
No full textIs it possible to apply some fundamental principles of quantum-computing to time series classification algorithms? This is the initial spark that became the research question I decided to chase at the very beginning of my PhD studies. The idea came accidentally after reading a note on the ability of entanglement to express the correlation between two particles, even far away from each other. The test problem was also at hand because I was investigating on possible algorithms for real time bot detection, a challenging problem at present day, by means of statistical approaches for sequential classification. The quantum inspired algorithm presented in this thesis stemmed as an evolution of the statistical method mentioned above: it is a novel approach to address binary and multinomial classification of an incoming data stream, inspired by the principles of Quantum Computing, in order to ensure the shortest decision time with high accuracy. The proposed approach exploits the analogy between the intrinsic correlation of two or more particles and the dependence of each item in a data stream with the preceding ones. Starting from the a-posteriori probability of each item to belong to a particular class, we can assign a Qubit state representing a combination of the aforesaid probabilities for all available observations of the time series. By leveraging superposition and entanglement on subsequences of growing length, it is possible to devise a measure of membership to each class, thus enabling the system to take a reliable decision when a sufficient level of confidence is met. In order to provide an extensive and thorough analysis of the problem, a well-fitting approach for bot detection was replicated on our dataset and later compared with the statistical algorithm to determine the best option. The winner was subsequently examined against the new quantum-inspired proposal, showing the superior capability of the latter in both binary and multinomial classification of data streams. The validation of quantum-inspired approach in a synthetically generated use case, completes the research framework and opens new perspectives in on-the-fly time series classification, that we have just started to explore. Just to name a few ones, the algorithm is currently being tested with encouraging results in predictive maintenance and prognostics for automotive, in collaboration with University of Bradford (UK), and in action recognition from video streams
Emotion Recognition from Speech: An Unsupervised Learning Approach
Full text linkSpeech processing is quickly shifting toward affective computing, that requires handling emotions and modeling expressive
speech synthesis and recognition. The latter task has been so far achieved by supervised classifiers. This implies a prior labeling
and data preprocessing, with a cost that increases with the size of the database, in addition to the risk of committing errors. A
typical emotion recognition corpus therefore has a relatively limited number of instances. To avoid the cost of labeling, and at
the same time to reduce the risk of overfitting due to lack of data, unsupervised learning seems a suitable alternative to recognize
emotions from speech. The recent advances in clustering techniques make it possible to reach good performances, comparable
to that obtained by classifiers, with much less preprocessing load and even with generalization guarantees. This paper presents
a novel approach for emotion recognition from speech signal, based on some variants of fuzzy clustering, such as probabilistic,
possibilistic and graded-possibilistic fuzzy c-means. Experiments indicate that this approach (a) is effective in recognition, with
in-corpus performances comparable to other proposals in the literature but with the added value of complexity control and (b)
allows an innovative way to analyze emotions conveyed by speech using possibilistic membership degrees
Heatmap Visualization for Deep Learning Analysis of Waste Printed Circuit Boards
No full textWaste Printed Circuit Boards (WPCBs) are complex multi-material assemblies that present challenges for automated recycling and Critical Raw Material (CRMs) recovery. Visualization of the part of the WPCBs need more attention and contain high-level density CRMs is challenging in computer vision based system analysis. In this work, we propose a deep learning-based multi-label classification framework integrated with heatmap visualization for interpretable WPCB analysis. We fine-tuned the ResNet50 model as backbone and applied binary cross entropy for each class on custom multi-label V-PCB dataset converted from YOLO format. For visualization of the specific regions across the WPCBs with an image, we applied Gradient-weighted Class Activation Mapping (Grad-CAM) that generate class-specific activation maps corresponding to high density CRMs contained components. Experiments on a custom curated V-PCBs dataset achieve a micro-averaged F1 score of 97.67%. The proposed system provides accurate classification along with interpretable heatmaps, supporting automating vision-based disassembly methods and recovery processes in e-waste recycling
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
Automated Disassembly of Waste Printed Circuit Boards: The Role of Edge Computing and IoT
Full text linkThe ever-growing volume of global electronic waste (e-waste) poses significant environmental and health challenges. Printed circuit boards (PCBs), which form the core of most electronic devices, contain valuable metals as well as hazardous materials. The efficient disassembly and recycling of e-waste is critical for both economic and environmental sustainability. The traditional manual disassembly methods are time-consuming, labor-intensive, and often hazardous. The integration of edge computing and the Internet of Things (IoT) provides a novel approach to automating the disassembly process, potentially transforming the way e-waste is managed. Automated disassembly of WPCBs involves the use of advanced technologies, specifically edge computing and the IoT, to streamline the recycling process. This strategy aims to improve the efficiency and sustainability of e-waste management by leveraging real-time data analytics and intelligent decision-making at the edge of the network. This paper explores the application of edge computing and the IoT in the automated disassembly of WPCBs, discussing the technological framework, benefits, challenges, and future prospects. The experimental results show that the YOLOv10 model achieves 99.9% average precision (AP), enabling accurate real-time detection of electronic components, which greatly facilitates the automated disassembly process
Audio Surveillance of Road Traffic: An Approach Based on Anomaly Detection and Interval Type-2 Fuzzy Sets
Full text linkSurveillance systems are increasingly exploiting multimodal information for improved effectiveness. This paper presents an audio event detection method for road traffic surveillance, combining generative deep autoencoders and fuzzy modelling to perform anomaly detection. Baseline deep autoencoders are used to compute the reconstruction error of each audio segment, which provides a primary estimation of outlierness. To account for the uncertainty associated to this decision-making step, an interval type-2 fuzzy membership function composed of an optimistic/upper component and a pessimistic/lower component is used. The final class attribution employs a probabilistic method for interval comparison. Evaluation results obtained after defuzzification show that, with a careful parameter setting, the proposed membership function effectively improves the performance of the baseline autoencoder, and performs better than the state-of-the-art one-class SVM in anomaly detection
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
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