Reviewerly/exHarmony huggingface dataset:
Denoising and Inpainting of Sonar Images Using Convolutional Sparse Representation

Recently, sparse representation (SR) techniques have been extensively applied for sound navigation and ranging (sonar) images’ restoration tasks such as denoising and inpainting. However, fine details such as edges and texture information present in sonar images are not fully restored using these techniques. This is due to ineffective dictionary formation and learning of sparse components and dictionaries. In this article, the author proposes the convolutional SR (CSR)-based methods for sonar image denoising and inpainting tasks. To minimize the Gaussian noise from sonar images, an alternative direction method of the multiplier (ADMM) optimization algorithm is used for solving the convolutional basis pursuit denoising (CBPDN) problem with joint sparsity through an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L_{2,1}$ </tex-math></inline-formula> -norm term. For the removal of impulse noise from sonar images, an ADMM algorithm is used to solve a CSR problem with an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L_{1}$ </tex-math></inline-formula> data fidelity term and both <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L_{1}$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L_{2}$ </tex-math></inline-formula> gradient regularization terms. For the inpainting of sonar images, the additive mask simulation (MS) technique is used for solving CSR problems related to boundary masking. From the experimental results, it is found that the proposed sonar image denoising and inpainting methods have created better visual quality sonar images, higher peak signal-to-noise ratio (PSNR), and structure similarity (SSIM) values, when compared with the state-of-the-art sonar image denoising and inpainting methods.
A Multi-Scale Feature Fusion Network With Cascaded Supervision for Cross-Scene Crowd Counting

Counting the number of people in public places has received much attention, and researchers have devoted much effort to the task. However, the existing crowd counting approaches are mainly trained and tested in similar scenarios. The performance of crowd counting approaches degrades sharply when the test scenarios of the models are of different types from its training scenes. In practice, the crowd scenes are highly variable, and the lack of cross-scene capability could seriously limit the application of the existing approaches. We attribute the improvement in cross-scene crowd counting capability to the necessity of accommodating large changes in the scale of individuals and the ability to suppress the interference of cluttered backgrounds. To this end, we propose a multi-scale feature fusion network (MFFNet) with cascaded supervision. The multi-scale features extracted from the crowd images are upsampled and then combined into several feature blocks, followed by convolution and deconvolution operations on the feature blocks to derive feature matrices of different resolutions. The feature matrices are fused from bottom to top. In the process of feature fusion, the crowd density maps corresponding to the feature matrices of different resolutions are predicted separately. We devise cascaded supervision to synchronously optimize the network of different resolution density map prediction during training. The cross-scene crowd counting experiments are conducted on four types of scenes: ShanghaiTech Part_A (SHT A) with high-density crowd scenes and small-scale individuals, ShanghaiTech Part_B (SHT B) with sparse crowd distribution and medium-scale individuals, UCF_CC_50 dataset with extremely dense scenes and tiny scale individuals, and UCF-QNRF dataset with extreme variations. MFFNet exhibits the strongest scene adaptability relative to the state-of-the-art approaches, with an average decrease of 17.1% and 8.4% in mean absolute error (MAE) and mean square error (mse), respectively. The contributions of different components in our method are verified in the ablation study using the devised evaluation metrics. Our implementation will be available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/learnsharing/MFFNet</uri> .
Fisher’s measure of variability in repeated samples

Fisher (1943) claimed that the expected value of the sample variance of the number of species found in large samples, each of n specimens taken from the same population, is asymptotically θlog2. This is at odds with the value θlogn obtained directly from the Ewens Sampling Formula (ESF), where θ specifies the rate at which new species are found. To resolve this apparent contradiction, we assume the species frequency spectrum in the population is determined by the ESF and that the samples are disjoint subsets drawn sequentially from this single population. We find an explicit formula for the required expected value for p samples of arbitrary size; in the limit of large equally-sized samples, it indeed has the value θlog2. We obtain limit theorems for the sample variance of p samples of size n under various limiting regimes as p,n or both tend to ∞. We discuss further the behavior of the number of species present in all samples, and revisit Fisher’s log-series distribution as the limiting distribution of the number of specimens observed in typical species in a future, large sample.
Implementation of an E-Learning Module in Learning Training Platforms

E-learning is seeing a significant rise in terms of use in initial or continuing education. At first glance, it seems necessary for the authors to take an interest in the different definitions proposed for this term, although this does not necessarily lead to a consensus in the existing literature. E-learning addressed by this chapter will answer the frequently asked question, “What is e-learning?” This chapter is also intended to provide an overview of current standardization efforts that ensure the transferability of educational resources and the interoperability of virtual training environments. Hence, to highlight the issues and concerns relating to e-learning standards and requirements, the authors present the different standardization approaches adopted at the economic, technical, and educational levels. E-learning from a pedagogical point of view is included in the last part of this chapter.
A Dual-Staged heterogeneous stacked ensemble model for gender recognition using speech signal

Gender classification is one of the most popular topics in the field of machine learning. It can be done with images or sounds. With the aim of developing a low-complexity and highly accurate gender recognition model, this paper proposes a Dual-staged Heterogeneous Stacked Ensemble Model (DH-SEM). In the proposed ensemble model, the classification problem is addressed by the amalgamation of three supervised base learners at stage-1 and random forest as a Meta learner in stage-2. The proposed work uses a speech dataset compiled from various sources (Harvard-Haskins, Carnegie Mellon and McGill University) and consists of statistical features. Rigorous feature engineering has been performed to select the most discriminative information from the dataset. For gender recognition, the DH-SEM method adopts the reduced feature space. The gender recognition accuracy of the proposed DH-SEM model is 99.36%, which is highest as compared to the state-of-the-art methods. The robustness of the proposed technique is also validated by other performance evaluation metrics and receiver operating characteristics.
Task Scheduling with Makespan Minimization for Distributed Machine Learning Ensembles

Machine Learning problems are becoming increasingly complex, mostly due to the size of datasets. Data are also generated at increasing speed, which requires models to be updated regularly, at a significant computational cost. The project Continuously Evolving Distributed Ensembles proposes the creation of a distributed Machine Learning environment, in which datasets are divided into fixed-size blocks, and stored in a fault-tolerant distributed file system with replication. The base-models of the Ensembles, with a 1:1 relationship with data blocks, are then trained in a distributed manner, according to the principle of data locality. Specifically, the system is able to select which data blocks to use and in which nodes of the cluster, in order to minimize training time. A similar process takes place when making predictions: the best base-models are selected in real-time, according to their predictive performance and to the state of the nodes where they reside. This paper addresses the problem of assigning base model training tasks to cluster nodes, adhering to the principle of data locality. We present an instance generator and three datasets that will provide a means for comparison while studying other solution methods. For testing the system architecture, we solved the datasets with an exact method and the computational results validate, to comply to the project requirements, the need for a more stable and less demanding (in computational resource terms) solution method.
Yolo V5 for Traffic Sign Recognition and Detection Using Transfer Learning

With the advancement in the field of autonomous cars, we are coming closer to reliable integration. However, in order for an autonomous vehicle to function in an urban environment, it has to abide by traffic rules. In this paper, we design a vision system based on our trained YOLO v5 models for both classification on the GTSRB dataset and detection on the GTSDB dataset using transfer learning from the classification to the detection model to optimise results. Our choice of the YOLO v5 algorithm is justified by its capability to combine accuracy and speed simultaneously, making it suitable for real-time applications.
Greek secondary school teachers' degree of readiness in adopting mobile learning in the educational process

Much has been written and researched about mobile learning. The positive impact it has on education has been highlighted in these studies. However, the role of the teacher is also of fundamental importance. Teachers are not sidelined. On the contrary, they take on a different role: reinforcer and guide. Therefore, it is necessary to consider teachers' opinions on mobile learning. It was considered necessary to investigate the degree of readiness of secondary school teachers to adopt Mobile Learning, focusing on the Greek region. Specifically, the study examines how teachers' readiness is affected by their gender, years of teaching experience, whether or not they have attended relevant training, and the type of school they work in. A total of 175 teachers from schools in Heraklion, Rethymno and Rhodes were surveyed, focusing on secondary education. According to the survey, teachers recognize the potential benefits of using mobile devices in education.
An Efficient USE-Net Deep Learning Model for Cancer Detection

Breast cancer (BrCa) is the most common disease in women worldwide. Classifying the BrCa image is extremely important for finding BrCa at an earlier stage and monitoring BrCa during treatment. The computer-aided detection methods have been used to interpret BrCa and improve the detection of BrCa during the screening and treatment stages. However, if a new BrCa image is generated for the treatment, it will not classify correctly. The main objective of this research is to classify the BrCa images for newly generated images. The model performs preprocessing, segmentation, feature extraction, and classification. In preprocessing, a hybrid median filtering (HMF) is used to eliminate the noise in the images. The contrast of the images is enhanced using quadrant dynamic histogram equalization (QDHE). Then, ROI segmentation is performed using the USE-Net deep learning model. The CaffeNet model is used for feature extraction on the segmented images, and finally, classification is made using the improved random forest (IRF) with extreme gradient boosting (XGB). The model obtained 97.87% accuracy, 98.45% sensitivity, 95.24% specificity, 98.96% precision, and 98.70% f1-score for ultrasound images. The model gives 98.31% accuracy, 99.29% sensitivity, 90.20% specificity, 98.82% precision, and 99.05% f1-score for mammogram images.
Capped Asymmetric Elastic Net Support Vector Machine for Robust Binary Classification

Recently, there are lots of literature on improving the robustness of SVM by constructing nonconvex functions, but they seldom theoretically study the robust property of the constructed functions. In this paper, based on our recent work, we present a novel capped asymmetric elastic net (CaEN) loss and equip it with the SVM as CaENSVM. We derive the influence function of the estimators of the CaENSVM to theoretically explain the robustness of the proposed method. Our results can be easily extended to other similar nonconvex loss functions. We further show that the influence function of the CaENSVM is bounded, so that the robustness of the CaENSVM can be theoretically explained. Other theoretical analysis demonstrates that the CaENSVM satisfies the Bayes rule and the corresponding generalization error bound based on Rademacher complexity guarantees its good generalization capability. Since CaEN loss is concave, we implement an efficient DC procedure based on the stochastic gradient descent algorithm (Pegasos) to solve the optimization problem. A host of experiments are conducted to verify the effectiveness of our proposed CaENSVM model.
Relation Extraction Based on Prompt Information and Feature Reuse

ABSTRACT To alleviate the problem of under-utilization features of sentence-level relation extraction, which leads to insufficient performance of the pre-trained language model and underutilization of the feature vector, a sentence-level relation extraction method based on adding prompt information and feature reuse is proposed. At first, in addition to the pair of nominals and sentence information, a piece of prompt information is added, and the overall feature information consists of sentence information, entity pair information, and prompt information, and then the features are encoded by the pre-trained language model ROBERTA. Moreover, in the pre-trained language model, BIGRU is also introduced in the composition of the neural network to extract information, and the feature information is passed through the neural network to form several sets of feature vectors. After that, these feature vectors are reused in different combinations to form multiple outputs, and the outputs are aggregated using ensemble-learning soft voting to perform relation classification. In addition to this, the sum of cross-entropy, KL divergence, and negative log-likelihood loss is used as the final loss function in this paper. In the comparison experiments, the model based on adding prompt information and feature reuse achieved higher results of the SemEval-2010 task 8 relational dataset.
The state of art, opportunities and challenges of blockchain in the insurance industry: a systematic literature review

Abstract Blockchain technologies are quickly changing the competitive business landscape. However, key stakeholders are still sceptical about how, when, and what blockchain might bring to their businesses. This study aims to analyse how blockchain might reshape the insurance industry from an economic and business perspective, as well as to identify which are the challenges and enablers that specifically affect blockchain adoption within this industry, through a Systematic Literature Review (SRL). We also classify existing challenges into five strategic areas: Social, Technological, Environmental, Economic, and Political. Additionally, we provide several recommendations to the manager on identifying the existing hurdles and smoothing transformation.
Decentralizing Online Food Delivery Services: A Blockchain and IoT Model for Smart Cities

Abstract With the worldwide pandemic outbreak, the style of restaurant food consumption underwent a major shift towards online delivery services. This poses an urgent need in trust between main stakeholders of the process: requiring restaurants to correspond to the quality declared, providing high quality and safe to consume meals, and obliging delivery entities to deliver food carefully, scrupulously following the delivery conditions. In this research, we explore a novelty approach that combines IoT, Blockchain technology, and city LoRa network to create a new trusted, decentralized approach for food distribution process in the context of a Smart City. This approach allows controlling the food delivery process using sensors data to control live location, temperature, vibrations, and shakings during the transportation process. We also suggest a fresh perspective on a rating system of delivery entities, where reputation points will be provided both from the side of the restaurant and the final consumer. This will create more trust towards the delivery entity since information will be tamper-proof and immutable due to the nature of Blockchain. This novel system proposal allows rethinking the online food delivery process in the context of Smart City, using the city’s LoRa LPWAN radio frequency technology and Blockchain decentralized solution.
Pre-training in Medical Data: A Survey

Abstract Medical data refers to health-related information associated with regular patient care or as part of a clinical trial program. There are many categories of such data, such as clinical imaging data, bio-signal data, electronic health records (EHR), and multi-modality medical data. With the development of deep neural networks in the last decade, the emerging pre-training paradigm has become dominant in that it has significantly improved machine learning methods′ performance in a data-limited scenario. In recent years, studies of pre-training in the medical domain have achieved significant progress. To summarize these technology advancements, this work provides a comprehensive survey of recent advances for pre-training on several major types of medical data. In this survey, we summarize a large number of related publications and the existing benchmarking in the medical domain. Especially, the survey briefly describes how some pre-training methods are applied to or developed for medical data. From a data-driven perspective, we examine the extensive use of pre-training in many medical scenarios. Moreover, based on the summary of recent pre-training studies, we identify several challenges in this field to provide insights for future studies.
Abnormal nodes sensing model in regional wireless networks based on convolutional neural network

Abstract There are some problems in abnormal node sensing in regional wireless networks, such as low sensing accuracy and poor judgment results of abnormal states of sensing nodes. Therefore, this paper develops a method for abnormal node sensing in regional wireless networks based on convolutional neural network. In addition, we will analyze the structure of regional wireless network nodes and determine the distribution mode of wireless network nodes. The regional wireless network node data are extracted and the pivot quantity and two-dimensional Gaussian distribution state are constructed using the median to build the regional wireless network node deployment model according to the confidence interval of the data characteristics; analyze the basic principle of convolution neural network, determine the operation mode of convolution kernel, classify the regional wireless network node data using Bayesian network, set a safety distance to determine the abnormal node of the regional wireless network, train the determined abnormal data as the input data of convolutional neural network and input it into the constructed perception model of the abnormal node of the regional wireless network, the loss function is set to continuously update the iterative results to realize the perception of abnormal node in the regional wireless network. The simulation results show that the sensing range of this method is relatively consistent with the range set by the sample, and the sensing accuracy reaches more than 95%, and the abnormal state error of abnormal nodes in the evaluation sample area is always less than 2%, which verifies that this method improves the sensing accuracy, reduces the error, and has higher application value.
Conversations: Conversations with a Prominent Propagator: Barbara Ericson

opinion Free Access Share on Conversations: Conversations with a Prominent Propagator: Barbara Ericson Authors: David P. Bunde Knox College Knox CollegeView Profile , Zack Butler Rochester Institute of Technology Rochester Institute of TechnologyView Profile , Christopher L. Hovey University of Colorado Boulder University of Colorado BoulderView Profile , Cynthia Taylor Oberlin College Oberlin CollegeView Profile Authors Info & Claims ACM InroadsVolume 14Issue 1March 2023pp 16–19https://doi.org/10.1145/3583091Published:21 February 2023Publication History 1citation154DownloadsMetricsTotal Citations1Total Downloads154Last 12 Months154Last 6 weeks21 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteView all FormatsPDF
Side effects of learning from low-dimensional data embedded in a Euclidean space

The low-dimensional manifold hypothesis posits that the data found in many applications, such as those involving natural images, lie (approximately) on low-dimensional manifolds embedded in a high-dimensional Euclidean space. In this setting, a typical neural network defines a function that takes a finite number of vectors in the embedding space as input. However, one often needs to consider evaluating the optimized network at points outside the training distribution. This paper considers the case in which the training data are distributed in a linear subspace of $${\mathbb {R}}^d$$ . We derive estimates on the variation of the learning function, defined by a neural network, in the direction transversal to the subspace. We study the potential regularization effects associated with the network’s depth and noise in the codimension of the data manifold. We also present additional side effects in training due to the presence of noise.
A novel method for local anomaly detection of time series based on multi entropy fusion

Dynamic Shannon entropy, which is fused by Shannon entropy and permuted distribution entropy (PDE), achieves good results in local anomaly recognition of time series. However, the dynamic Shannon entropy performs poorly in identifying various abnormal behaviors of complex signals with noise and chaotic characteristics and periodic background. It also gets unsatisfying results in anomaly detection of signals containing relatively large uncertainty anomaly information. Deng entropy, an extended entropy of Shannon entropy, expands the information capacity of Shannon entropy and is more excellent at describing uncertainty and complexity. In this paper, dynamic Deng entropy (DyEd) is proposed by fusing Deng entropy and PDE to identify local anomalies of time series. Six numerical experiments and an empirical application are conducted to illustrate the efficiency of the proposed method. Results show that the proposed method is of great success for signal anomaly detection with periodic backgrounds.