Reviewerly/exHarmony huggingface dataset:
A Resource-Efficient Keyword Spotting System Based on a One-Dimensional Binary Convolutional Neural Network

This paper proposes a resource-efficient keyword spotting (KWS) system based on a convolutional neural network (CNN). The end-to-end KWS process is performed based solely on 1D-CNN inference, where features are first extracted from a few convolutional blocks, and then the keywords are classified using a few fully connected blocks. The 1D-CNN model is binarized to reduce resource usage, and its inference is executed by employing a dedicated engine. This engine is designed to skip redundant operations, enabling high inference speed despite its low complexity. The proposed system is implemented using 6895 ALUTs in an Intel Cyclone V FPGA by integrating the essential components for performing the KWS process. In the system, the latency required to process a frame is 22 ms, and the spotting accuracy is 91.80% in an environment where the signal-to-noise ratio is 10 dB for Google speech commands dataset version 2.
Automatic Modulation Classification with Deep Neural Networks

Automatic modulation classification is an important component in many modern aeronautical communication systems to achieve efficient spectrum usage in congested wireless environments and other communications systems applications. In recent years, numerous convolutional deep learning architectures have been proposed for automatically classifying the modulation used on observed signal bursts. However, a comprehensive analysis of these differing architectures and the importance of each design element has not been carried out. Thus, it is unclear what trade-offs the differing designs of these convolutional neural networks might have. In this research, we investigate numerous architectures for automatic modulation classification and perform a comprehensive ablation study to investigate the impacts of varying hyperparameters and design elements on automatic modulation classification accuracy. We show that a new state-of-the-art accuracy can be achieved using a subset of the studied design elements, particularly as applied to modulation classification over intercepted bursts of varying time duration. In particular, we show that a combination of dilated convolutions, statistics pooling, and squeeze-and-excitation units results in the strongest performing classifier achieving 98.9% peak accuracy and 63.7% overall accuracy on the RadioML 2018.01A dataset. We further investigate this best performer according to various other criteria, including short signal bursts of varying length, common misclassifications, and performance across differing modulation categories and modes.
Application of Wavelet Transform to Urysohn-Type Equations

This paper deals with convolution-type Urysohn equations of the first kind. Finding a solution for such equations is an ill-posed problem. For it to be solved, regularization algorithms and the continuous wavelet transform are used. Similar to the Fourier transform, the continuous wavelet transform is applied to convolution-type equations (based on the Fourier and wavelet transforms) and to Urysohn equations with unknown shift. The wavelet transform is preferable for the cases with approximated right-hand sides and for type 1 equations. We demonstrated that the application of the wavelet transform to Urysohn-type equations with unknown shift translates into a solution of a nonlinear equation with an oscillating kernel. Depending on the availability of a priori information, a combination of regularization and iterative algorithms with the use of close equations are effective for solving convolution-type equations based on the continuous wavelet transform and Urysohn equation.
A Multi-Strategy Crazy Sparrow Search Algorithm for the Global Optimization Problem

A multi-strategy crazy sparrow search algorithm (LTMSSA) for logic-tent hybrid chaotic maps is given in the research to address the issues of poor population diversity, slow convergence, and easily falling into the local optimum of the sparrow search algorithm (SSA). Firstly, the LTMSSA employs an elite chaotic backward learning strategy and an improved discoverer-follower ratio factor to improve the population’s quality and diversity. Secondly, the LTMSSA updates the positions of discoverers and followers by the crazy operator and the Lévy flight strategy to expand the selection range of target following individuals. Finally, during the algorithm’s optimization search, the LTMSSA introduces the tent hybrid and Corsi variable perturbation strategies to improve the population’s ability to jump out of the local optimum. Different types and dimensions of test functions are used as performance benchmark functions to test the performance of the LTMSSA with SSA variants and other algorithms. The simulation results show that the LTMSSA can jump out of the optimal local solution, converge faster, and have higher accuracy. Its overall performance is better than the other seven algorithms, and the LTMSSA can find smaller optimal values than other algorithms in the welded beam and reducer designs. The results confirm that the LTMSSA is an effective aid for computationally complex practical tasks, provides high-quality solutions, and outperforms other algorithms.
Multi‐source to multi‐target domain adaptation method based on similarity measurement

Abstract Most existing domain adaption methods solve the problem of data distribution similarity in single‐source to single‐target domain or multi‐source to single‐target domain adaption, but the more realistic multi‐source to multi‐target domain transfer scenarios are ignored. At the same time, less attention is paid to the similarity of task distribution among domains and the corresponding relationship between coarse‐grained and fine‐grained, which leads to the problem of low classification accuracy. In this paper, a multi‐source to multi‐target domain adaption (MSMTDA) method based on similarity measurement is proposed. Firstly, a cross‐domain similarity measurement mechanism based on the idea of data distribution similarity and task distribution similarity is set up to judge and optimize the distribution difference among source domains and target domains. Secondly, global‐local‐bidirectional alignment is used to solve the problem of poor transfer performance in the existing distributed alignment. Then, the cross‐domain mutual learning strategy is proposed, which reduces classifier discrepancy caused by decision boundaries and distribution difference in multiple domains. Finally, the proposed method is verified on three benchmark datasets in image classification. The experimental results show that the proposed method performs better in both classification accuracy and training time when compared with the conventional method.
Feature attention gated context aggregation network for single image dehazing and its application on unmanned aerial vehicle images

Abstract Single‐image dehazing is a highly challenging ill‐posed task in the field of computer vision. To address this, a new image dehazing model with feature attention, named feature attention gated context aggregation network (FAGCA‐Net), is proposed to tackle the issues of incomplete or over‐dehazing caused by the original model's inability to handle non‐uniform haze density distributions. A feature attention module that combines channel attention and spatial attention is introduced. Additionally, the authors propose a new extended attention convolutional block, which not only addresses the grid artefacts caused by the extended convolution but also provides added flexibility in handling different types of feature information. At the same time, in addition to the input image itself, incorporating the dark channel and edge channel of the image as the final input of the model is helpful for the model learning process. To demonstrate the robustness of the new model, it is applied to two completely different dehazing datasets, and it achieves significant dehazing performance improvement over the original model. Finally, to verify the effectiveness of the model in practical production processes, the authors apply it as an image preprocessing step to a set of UAV (Unmanned Aerial Vehicle) images of foreign objects. The result shows that the UAV images after being processed by FAGCA‐Net for haze removal have a better impact on subsequent usage.
Background memory‐assisted zero‐shot video object segmentation for unmanned aerial and ground vehicles

Abstract Unmanned aerial vehicles (UAV) and ground vehicles (UGV) require advanced video analytics for various tasks, such as moving object detection and segmentation; this has led to increasing demands for these methods. We propose a zero‐shot video object segmentation method specifically designed for UAV and UGV applications that focuses on the discovery of moving objects in challenging scenarios. This method employs a background memory model that enables training from sparse annotations along the time axis, utilizing temporal modeling of the background to detect moving objects effectively. The proposed method addresses the limitations of the existing state‐of‐the‐art methods for detecting salient objects within images, regardless of their movements. In particular, our method achieved mean and values of 82.7 and 81.2 on the DAVIS'16, respectively. We also conducted extensive ablation studies that highlighted the contributions of various input compositions and combinations of datasets used for training. In future developments, we will integrate the proposed method with additional systems, such as tracking and obstacle avoidance functionalities.
Distracted driving behavior recognition based on improved MobileNetV2

In recent years, research on distracted driving behavior recognition has made significant progress, with an increasing number of researchers focusing on deep-learning-based algorithms. Aiming at the problems of the existing distracted driving recognition algorithm, such as its oversized model and difficulty in adapting to low computing environments, a lightweight network MobileNetV2, is chosen as the backbone network and improved to design a distracted driving behavior detection method that is both accurate and practical. The Ghost module is employed to replace point-by-point convolution to reduce the computation, the Leaky ReLU function helps mitigate the problem of dead neurons, as it prevents gradients from becoming zero for negative inputs. Finally, the channel pruning algorithm is used to further reduce the model parameters. The experiment results on the State Farm dataset show that the model’s test accuracy can reach 94.66%, and the number of parameters is only 0.23 M. The improved model has significantly fewer parameters than the baseline model, which demonstrates the effectiveness and applicability of the method.
Adversarial false data injection attacks on deep learning‐based short‐term wind speed forecasting

Abstract Developing accurate wind speed forecasting methods is indispensable to integrating wind energy into smart grids. However, current state‐of‐the‐art wind speed forecasting methods are almost data‐driven deep learning models, which may incur potential adversarial cyberattacks. To this end, this paper proposes an adversarial false data injection attack tactic to investigate such a cyber threat. First, targeting the deep learning‐based short‐term wind speed forecasting model, an optimization model is constructed to obtain the optimally false data that should be injected into the forecasting model input so as to expand the prediction deviation as much as possible. Then, as the optimization model is non‐differentiable, a particle swarm optimization‐based method is developed to solve the optimization problem, in which the near‐optimal solution is able to be explored, directing the false data that should be injected. At last, numerical studies of the proposed attack tactic are conducted on different‐hour ahead wind speed forecasting models, revealing the feasibility and effectiveness.
Multi-strategy Improved Seagull Optimization Algorithm

Abstract Aiming at the shortcomings of seagull optimization algorithm in the process of searching for optimization, such as slow convergence speed, low precision, easy falling into local optimal, and performance dependent on the selection of parameters, this paper proposes an improved gull optimization algorithm based on multi-strategy fusion based on the analysis of gull population characteristics. Firstly, L–C cascade chaotic mapping is used to initialize the population so that seagulls are more evenly distributed in the initial solution space. Secondly, to improve the algorithm’s global exploration ability in the early stage, the nonlinear convergence factor is incorporated to adjust the position of seagulls in the migration stage. At the same time, the group learning strategy was introduced after the population position update to improve the population quality and optimization accuracy further. Finally, in the late stage of the algorithm, the golden sine strategy of the Levy flight guidance mechanism is used to update the population position to improve the population’s diversity and enhance the local development ability of the algorithm in the late stage. To verify the optimization performance of the improved algorithm, CEC2017 and CEC2022 test suites are selected for simulation experiments, and box graphs are drawn. The test results show that the proposed algorithm has apparent convergence speed, accuracy, and stability advantages. The engineering case results demonstrate the proposed algorithm’s advantages in solving complex problems with unknown search spaces.
Learning through the eyes of another

Over the past decade, rapid technological advancements and budget constraints have increased the de­mand for online education (Martin et al., 2020). Furthermore, the COVID-19 pandemic has vastly ac­cele­ra­ted this trend, compelling almost all education providers to migrate their courses to online learning platforms (Theelen &amp; Van Breukelen, 2022). In view of other profound crises that affect mobility, such as climate change, political instabilities and future pandemics, it is safe to assume that online learning will remain in demand, even in a post-pandemic world ) (Bayne et al., 2020). In this context, while educational research has made significant progress in establishing design principles that ensure effective online teaching and learning, the main focus of this scholarly work is on the acquisition of declarative knowledge and cognitive skills. Moreover, since very little is known about the online teaching and distance learning of psychomotor skills (Kouhia et al., 2021; Lehtiniemi et al., 2023), this paper and exhibition explore how eye-tracking technology (ETT) creates unique opportunities to improve craft education in hybrid and distant learning settings.
Use of information and communication technologies to promote the learning process of students with intellectual disabilities

Students with intellectual disabilities have difficulties in advancing in the teaching-learning process, because traditional methods are used by teachers, and this causes poor performance. The objective is to introduce the use of computer and communication technologies to improve academic capabilities, the methodology used was descriptive, qualitative, and bibliographic review. The results obtained indicate that the use of new technologies enhanced the abilities of the students, confirming cognitive improvements for learning the subjects, in this way it was proven that these tools allow greater development of the academic skills of each of them.
Semantic Hybrid Signal Temporal Logic Learning-Based Data-Driven Anomaly Detection in the Textile Process

The development of sensor networks allows for easier time series data acquisition in industrial production. Due to the redundancy and rapidity of industrial time series data, accurate anomaly detection is a complex and important problem for the efficient production of the textile process. This paper proposed a semantic inference method for anomaly detection by constructing the formal specifications of anomaly data, which can effectively detect exceptions in process industrial operations. Furthermore, our method provides a semantic interpretation of exception data. Hybrid signal temporal logic (HSTL) was proposed to improve the insufficient expressive ability of signal temporal logic (STL) systems. The epistemic formal specifications of fault offline were determined, and a data-driven semantic anomaly detector (SeAD) was constructed, which can be used for online anomaly detection, helping people understand the causes and effects of anomalies. Our proposed method was applied to time-series data collected from a representative textile plant in Zhejiang Province, China. Comparative experimental results demonstrated the feasibility of the proposed method.
Interpreting archaeological boat material, the G1 boat of the Gokstad find

We will discuss the limitation of cardboard as a material for reconstruction models, and whether an alter­­native material could be helpful to overcome these limitations. We have experimented with 3D-print­ed plastic and ways to reduce the longitudinal high edge stiffness, to get around what we think is the main weakness using cardboard. The goal has been to open the possibilities of interpretation. The end result of the tests has been used to build a 1:5 reconstruction model, for our interpretation of the 10m boat from the Gokstad find (G1) from late 800’s. We found that the use of 3D-printed plastic, with a designed internal weakness, gives us more longitudinal flexibility in the model. This flexibility opens up for interpretations that are less feasible when using cardboard.
Automated cervical cell segmentation using deep ensemble learning

Cervical cell segmentation is a fundamental step in automated cervical cancer cytology screening. The aim of this study was to develop and evaluate a deep ensemble model for cervical cell segmentation including both cytoplasm and nucleus segmentation.The Cx22 dataset was used to develop the automated cervical cell segmentation algorithm. The U-Net, U-Net + + , DeepLabV3, DeepLabV3Plus, Transunet, and Segformer were used as candidate model architectures, and each of the first four architectures adopted two different encoders choosing from resnet34, resnet50 and denseNet121. Models were trained under two settings: trained from scratch, encoders initialized from ImageNet pre-trained models and then all layers were fine-tuned. For every segmentation task, four models were chosen as base models, and Unweighted average was adopted as the model ensemble method.U-Net and U-Net + + with resnet34 and denseNet121 encoders trained using transfer learning consistently performed better than other models, so they were chosen as base models. The ensemble model obtained the Dice similarity coefficient, sensitivity, specificity of 0.9535 (95% CI:0.9534-0.9536), 0.9621 (0.9619-0.9622),0.9835 (0.9834-0.9836) and 0.7863 (0.7851-0.7876), 0.9581 (0.9573-0.959), 0.9961 (0.9961-0.9962) on cytoplasm segmentation and nucleus segmentation, respectively. The Dice, sensitivity, specificity of baseline models for cytoplasm segmentation and nucleus segmentation were 0.948, 0.954, 0.9823 and 0.750, 0.713, 0.9988, respectively. Except for the specificity of cytoplasm segmentation, all metrics outperformed the best baseline models (P < 0.05) with a moderate margin.The proposed algorithm achieved better performances on cervical cell segmentation than baseline models. It can be potentially used in automated cervical cancer cytology screening system.
Implementing Google Tag Manager (GTM) in library websites

Purpose The purpose of this research paper is to explore and offer insightful information on the useful use of Google Tag Manager (GTM) in the context of library websites and to bridge the gap between GTM’s technical features and the practical requirements of libraries. It gives libraries the ability to use GTM’s capabilities to increase user engagement, data-driven decision-making and improve online services. Design/methodology/approach This study reviews existing literature on GTM in the context of websites and libraries. The methodology involves identifying keywords and searching terms related to GTM, digital marketing, user engagement, Web analytics and library websites. Sources and databases were consulted, including library science journals, marketing journals, academic databases, publications on digital marketing and search platforms such as Google Books, Google Scholar, Google Search Engine, JSTOR and library associations like the American Library Association. Initial screening was done based on titles and abstracts, followed by a thorough-text review, categorization and synthesizing of the findings. Findings GTM provides libraries with a potent tool to improve their online presence, customize user experiences and collect insightful real-time data. Libraries may harness GTM’s potential to better engage people and provide services by properly implementing it and maintaining it over time. It can be a flexible instrument that supports contemporary library services in the digital era. The findings of this study indicate that GTM technology may be used in library services; nevertheless, there are several barriers, such as librarians’ attitudes and technical abilities, that prevent GTM acceptance in library services. Originality/value This study covers the implementation of a free GTM tool in library websites that will help the library and information professionals to leverage the GTM in the library’s online presence. Furthermore, this study recommends that libraries and librarians should develop guidelines and policies for the critical adoption of a free GTM tool in the library environment, which will support improving the library’s user engagement and tracking of library website traffic.
Convivial aesthetic in social innovation: a nested framework from three projects in Milano

ABSTRACTSocial structures are increasingly becoming subject matters of design, because considered integral to service design, akin to interfaces, physical elements, and spaces. The paper explores the use of aesthetics to reshape social structures in social innovation. It delves into three social innovation projects in Milan, formulating the concept of ‘convivial aesthetic’, which refers to the design and creation of materials, interactions, social forms, and a sense of community that fosters autonomy, personal interdependence, and positive social change. The analysis of the projects is based on a framework consisting of four elements: material support, social interaction, ordinary social forms, and the sense of community.The latter is a crucial component of convivial aesthetic: by fostering a feeling of belonging and shared purpose, participants become part of a larger social community with collective goals. Designers use symbols, rituals, and events to strengthen this sense of community.The paper acknowledges the limitations of this framework, particularly its focus on smaller communities and the need for further exploration of larger societal structures. However, it highlights the value of convivial aesthetic in shifting the focus of design from physical objects to immaterial interactions and social forms, ultimately promoting positive social transformation.KEYWORDS: Aestheticconvivialityservice designsocial innovationsocial formscommunity Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1. https://www.facebook.com/Nutrire-Milano-energie-per-il-cambiamento-186357474712947; https://www.facebook.com/mercatodellaterradimilano.2. The full partnership is organised in two levels: a|cube (coordinator), Politecnico di Milano – Polimi DESIS Lab, Spazio Aperto Servizi, Fondazione Politecnico di Milano, Kilowatt mainly active on advanced training and experimentation; and Avanzi, Comunità del Giambellino, Dynamoscopio, Gruppo Cooperativo Cgm, PerMicro, active on the project start-up phase for scouting and community engagement.3. See https://lascuoladeiquartieri.it/; https://www.facebook.com/lascuoladeiquartieri; and https://www.instagram.com/lascuoladeiquartieri/.