Reviewerly/exHarmony huggingface dataset:
LRP-GUS: A Visual Based Data Reduction Algorithm for Neural Networks

Deriving general rules to estimate a neural network sample complexity is a difficult problem. Therefore, in practice, datasets are often large to ensure sufficient class samples representation. This comes at the cost of high power consumption and long training time. This paper introduces a novel data reduction method for Deep Learning classifiers, called LRP-GUS, focusing on visual features. The idea behind LRP-GUS is to reduce the size of our training dataset by exploiting visual features and their relevance. The proposed technique is tested on the MNIST and Fashion-MNIST datasets. We evaluate the method using compression rates, accuracy and $$F_1$$ scores per class. For instance, our method achieves compression rates of 96.10% for MNIST and 75.94% for Fashion-MNIST, at the cost of a drop of 3% test accuracy for both datasets.
Information-Theoretically Secure Neural Network Training with Flexible Deployment

Neural networks have been widely used in image processing, speech recognition, and other fields. When multiple parties collaborate to train neural network models by combining their individual data, some data are sensitive and cannot be leaked to anyone other than the data holder. In this paper, we propose new protocols for private neural network training that protect the privacy of sensitive data when they are integrated from different sources. Our protocols can reach information-theoretic security and allow for the flexible configuration of the number of parties based on actual requirements. We conduct experiments in settings with different numbers of participants, where we train neural networks on the MNIST dataset. The experimental results demonstrate that our protocols can successfully implement the task of neural network training while offering the benefit of flexible deployment.
DaCon: Multi-Domain Text Classification Using Domain Adversarial Contrastive Learning

Multi-Domain Text Classification (MDTC) aims to classify text from a specific domain based on knowledge learned from multiple other domains. Recent state-of-the-art approaches address the MDTC problem using a shared-private model design (i.e., a shared feature encoder and multiple domain-specific encoders) which requires massive amounts of labeled data. However, some domains in real-world scenarios lack sufficient labeled data, resulting in significant performance degradation when recent approaches are directly applied to these low-resource domains because all domain-specific features are set to zeros. In this paper, we propose DaCon, a novel MDTC approach that employs domain adversarial supervised contrastive learning to improve classification accuracy. In particular, DaCon uses supervised contrastive learning to align text classification features with respect to label information, pulling pairs of the same class together and pushing pairs of different classes apart in the embedding space and aligns features with respect to domain information through domain adversarial learning. We conduct experiments on the widely-used benchmarks FDU-MTL. Our results demonstrate that DaCon achieves state-of-the-art performance for MDTC with or without labeled data from the target domain.
Exploring the Role of Recursive Convolutional Layer in Generative Adversarial Networks

This paper aims to study the potentialities of incorporating recursive layers into Generative Adversarial Networks (GANs). Drawing inspiration from biological systems, in which feedback connections are prevalent, different studies investigated their impact on artificial neural networks. These studies have shown that feedback connections improve performance in tasks such as image classification and segmentation. Motivated by this insight, in this work we investigate whether also image generation can benefit from recursive architectures. To support our argument, we introduce a recursive layer into a standard generative architecture, specifically a Wasserstein GAN with gradient penalty (WGAN-GP), resulting in a novel model we refer to as the Looping Generative Adversarial Network (LoGAN). The performance of the LoGAN architecture is compared with the corresponding feedforward WGAN-GP both qualitatively and quantitatively. Preliminary experiments suggest that the use of recursive layers holds significant potential to generate higher-quality samples in GANs. The code is publicly available at https://github.com/bcorrad/LoGAN .
ELM-Based Liver Disease Prediction Model

The liver is an important part of the digestive system. Unfortunately, an unhealthy diet affects the liver and it kills more than 2 million people worldwide every year. With so many people affected, it is important to develop computational disease prediction models. In the present work, we develop liver disease prediction model based on ELM classifier to classify whether a patient is suffering from liver disease or not based on the data. The dataset used in the work is a standard dataset known as Indian liver patient dataset (ILPD) which is freely available. The computational results show that the proposed model outperforms other existing models.
ANODE-GAN: Incomplete Time Series Imputation by Augmented Neural ODE-Based Generative Adversarial Networks

Missing data is a commonly encountered problems in time series analysis, impeding accurate data analysis. Various methods have been proposed to impute missing values, including statistical, machine learning, and deep learning approaches. However, these methods either involve multi-steps, neglect temporal information, or are incapable of imputing missing data at desired time points. To overcome these limitations, this paper proposes a novel generative framework for imputing missing data, named the Augmented Neural Ordinary Differential Equation-assisted Generative Adversarial Network (ANODE-GAN). ANODE-GAN utilizes a Variational AutoEncoder (VAE) module to maps an incomplete time series instance to fixed-dimension initial latent vectors, generates continuous-time latent dynamics, and finally decodes them into complete data. With the aid of an additional discriminative network, ANODE-GAN can produce complete data that is closest to the original time series according to the squared error loss. By combining the generator and discriminator, ANODE-GAN is capable of imputing missing data at any desired time point while preserving the original feature distributions and temporal dynamics. Moreover, ANODE-GAN is evaluated on real-world datasets with varying missing rates by conducting the imputation task. A set of rigorous experiments show ANODE-GAN outperforms baseline methods in terms of Mean Square Error (MSE).
Adapted Methods for GAN Vocoders via Skip-Connections ISTFT and Cooperative Structure

Although recent works on GAN vocoders have improved the quality of synthesized speech by replacing current network design modules with various new modules, they often neglect to fully exploit the potential of the existing network architecture. Aside from exploring new network modules, exploiting the potential of existing network architectures is another effective strategy to enhance GAN vocoders. We thus propose two improvement methods that can further exploit the information contained in the network and apply them to HiFi-GAN. The first method is to introduce the skip-connections inverse short-time Fourier transform (skip-connections iSTFT) in the generator, and the second method is to introduce the cooperative structure in the discriminator. Experimental results show both methods synthesize speech with better quality compared to HiFi-GAN, with MOS improvement of 0.04 for the first method and 0.06 for the second method, and both methods also outperform HiFi-GAN in objective evaluation of MCD and PESQ. In addition, we also perform joint training of these two methods.
SAL: Salient Adversarial Attack with LRP Refinement

Deep Neural Networks (DNNs) are susceptible to attacks by adversarial examples, which could cause serious consequences in safety-critical systems. Towards recent studies on generating efficient adversarial examples, how to enhance the imperceptibility of adversarial examples has been an issue worth more investigating. In this paper, we propose a novel salient adversarial attack method based on Layer-Wise Relevance Propagation (LRP), named SAL, which restricts perturbations to salient regions and subsequently refines them using the LRP interpretation algorithm, reducing perturbations to some pixel points. We conduct sufficient experiments on the ImageNet-Compatible dataset. Experiment results demonstrate that our method is capable of generating higher imperceptibility adversarial examples in quite less time, compared to the representative PerC-AL method. Besides, the robustness and transferability of our method are validated to perform better than the baseline methods.
Optimization assisted autoregressive technique with deep convolution neural network-based entropy filter for image demosaicing

ABSTRACTThis paper presents an image demosaicing based on an optimization-driven deep learning model, namely the Autoregressive Water Wave Optimization algorithm (Autoregressive-WWO). The proposed method is devised by assimilating the Wave Optimization algorithm (WWO), and the Conditional autoregressive value at risk (CAViaR) model. Here, the input images are subjected to Autoregressive WWO-based local polynomial approximation and intersection of confidence intervals (LPA-ICI) filter, and Deep Convolution neural network (Deep CNN) in a concurrent manner. The filter coefficients are obtained from the proposed Autoregressive WWO-based LPA-ICI filter and the residual image is obtained from Deep CNN. In order to create the demosaiced image, these two outputs are combined using an entropy measure. The proposed method offered superior performance with the highest Peak signal to noise ratio (PSNR) of 40.049dB, the highest Second derivative measure of enhancement (SDME) of 50.168dB, and highest Structural Index Similarity (SSIM) of 0.9056.KEYWORDS: Image demosaicingentropycolour filter arraydeep convolution neural networkfusion processLPA-ICI filterWWOCAViaR AcknowledgementsI would like to convey my sincere gratitude to the co-authors of this publication for their insightful advice and support throughout the conception and planning of this research project. All authors have made substantial contributions to conception and design, revising the manuscript, and the final approval of the version to be published. Also, all authors agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementMultispectral Image Database: Stuff, ‘https://www.cs.columbia.edu/CAVE/databases/multispectral/stuff/’ Accessed on April 2021.
Chinese personalised text‐to‐speech synthesis for robot human–machine interaction

Abstract Speech interaction is an important means of robot interaction. With the rapid development of deep learning, end‐to‐end speech synthesis methods based on this technique have gradually become mainstream. Chinese deep learning‐based speech synthesis techniques suffer from problems such as unstable synthesised speech, poor naturalness and poor personalised speech synthesis, which do not satisfy some practical application scenarios. Hence, an F‐MelGAN model is adopted to improve the performance of Chinese speech synthesis. A post‐processing network is used to refine the Mel‐spectrum predicted by the decoder and alleviate the Mel‐spectrum distortion phenomenon. A phoneme‐level and sentence‐level combined module is proposed to model the personalised style of speakers. A combination of an acoustic conditioning network, speaker encoder network GCNet and feedback‐constrained training is proposed to solve the problem of poor personalised speech synthesis and achieve personalised speech customisation in Chinese. Experimental results show that the whole model can generate high‐quality speech with high speaker similarity for both speakers that appear in the training process and speakers that never appear in the training process.
Pix2PixSSR: Spatial super‐resolution synthesis and visualization for time‐varying volumetric data

Abstract Upscaling of the time‐varying volume data is significant, since it can be used in in situ visualization to help scientists fast analyse complex simulations relevant to time‐varying volume data. A novel deep learning method called Pix2Pix spatial super‐resolution (Pix2PixSSR), which can be used to generate spatial super‐resolution of the time‐varying volume data is proposed here. It consists of two main components: One is a variant UNet‐like generator that takes the low resolution volume sequence as input and generates the high resolution counterparts; one is a PatchGAN discriminator that takes both low and high resolution volume sequences as input and predicts their realness. To validate its advantages, we qualitatively and quantitatively compare it with the state‐of‐the‐art upscaling techniques. More specifically, two experiments are performed. The first experiment uses the same variable of a time‐varying volume dataset for training and inference, while the second experiment uses different variables for training and inference. The experimental results show that for most cases, the Pix2PixSSR can generate the most similar super‐resolution to the ground truth, compared to the state‐of‐the‐art techniques.
Document verification system at college level using Blockchain

Blockchain technology promises to be hugely trending and empowering in education domain computing applications. The digital is becoming an integral part of modern life. So as the use of the digital world increases there are more chances of decrease is the security level. So more the use of digitization more the frauds and less the security. In some cases of personal data, leakage has brought back into the focus the security issues with the different identity sharing mechanisms. A customer is expected to provide his identity for authentication by different agencies. So the document verification process deals with the identification of the user. And in turn, provides the required security. The document verification procedures which are used by the colleges are completely dependent on the encryption. This system can be efficient by using Blockchain technology, which has the potential to automate a lot of manual processes and it is also resistant to hacks of any sort. The immutable blockchain block and its distributed ledger is the perfect complement to the process of document verification. With the addition of smart contacts, fraud detection can be automated..
A Fault Detection and Protection Scheme for a 200 MVA Transformer using Fuzzy Logic

The paper elucidates a fault detection and protection scheme for a 200MVA transformer using Fuzzy logic. Fuzzy logic (FL) tool box in MATLAB/ SIMULINK software was utilized in the simulation system that diagnosed transformer faults and also monitors its operating conditions. Current and rate of change of current with time have been identified as the input variables, duly represented in the programme as “Error” and “Error-Dot”. These variables have their universe of discourse from -1.5 to 1.5 and from -10 to 10 respectively. Fuzzy logic sensor is designed to monitor the current (i) conditions of the transformer at both ambient and full load. The results from the research show that whenever the output response is zero the current in transformer is normal. This is obtained when input values of [0] and [0] are injected into the system to produce a response of “6e-017” which is approximately zero. Whereas if the output response is greater than zero it implies that the transformer current is rising beyond normal and protection scheme should be alerted. This condition is achieved when input values of [-1.5] and [5] are used on the system to give a response of “+5”. However, if the response is less than zero then the transformer current is below normal, hence the protection scheme should be alerted. To investigate this, input values of [1.5] and [-5] gives a response of “-5”.
Semi-Supervised Classification with A*: A Case Study on Electronic Invoicing

This paper addresses the time-intensive task of assigning accurate account labels to invoice entries within corporate bookkeeping. Despite the advent of electronic invoicing, many software solutions still rely on rule-based approaches that fail to address the multifaceted nature of this challenge. While machine learning holds promise for such repetitive tasks, the presence of low-quality training data often poses a hurdle. Frequently, labels pertain to invoice rows at a group level rather than an individual level, leading to the exclusion of numerous records during preprocessing. To enhance the efficiency of an invoice entry classifier within a semi-supervised context, this study proposes an innovative approach that combines the classifier with the A* graph search algorithm. Through experimentation across various classifiers, the results consistently demonstrated a noteworthy increase in accuracy, ranging between 1% and 4%. This improvement is primarily attributed to a marked reduction in the discard rate of data, which decreased from 39% to 14%. This paper contributes to the literature by presenting a method that leverages the synergy of a classifier and A* graph search to overcome challenges posed by limited and group-level label information in the realm of electronic invoicing classification.
Accelerated Maximum Entropy Method for Time Series Models Estimation

The work is devoted to the development of a maximum entropy estimation method with soft randomization for restoring the parameters of probabilistic mathematical models from the available observations. Soft randomization refers to the technique of adding regularization to the functional of information entropy in order to simplify the optimization problem and speed up the learning process compared to the classical maximum entropy method. Entropic estimation makes it possible to restore probability distribution functions for model parameters without introducing additional assumptions about the likelihood function; thus, this estimation method can be used in problems with an unspecified type of measurement noise, such as analysis and forecasting of time series.
Promoting Open Access in Research-Performing Organizations: Spheres of Activity, Challenges, and Future Action Areas

Open access (OA) has become a critical issue in science policy and affects a wide range of activities in universities and research labs. Research-performing organizations (RPOs), defined as publicly funded universities and research institutions, face significant challenges in shaping the OA transformation. This article examines the spheres of activity available to RPOs for shaping the OA transformation, using a categorization of 22 spheres of activity related to OA. These spheres of activity include strategy and communication, services and infrastructures, business relationships with publishers, and collaborations. Current challenges and future action areas in promoting OA are also described, providing support for RPOs in handling OA and highlighting key issues. The categorization can serve as a tool for systematically assessing OA activities at RPOs and shows that OA is a cross-cutting issue in these organizations. Collaboration on OA activities, both within and beyond organizations, presents a challenge. To effectively promote OA, it is crucial to strengthen the interaction between funding agencies and RPOs. Libraries are critical stakeholders, playing a vital role in advancing OA at the local, national, and international levels in partnership with RPO management and other partners in faculty, administration, and information technology.
Using Noisy Evaluation to Accelerate Parameter Optimization of Medical Image Segmentation Ensembles

An important concern with regard to the ensembles of algorithms is that using the individually optimal parameter settings of the members does not necessarily maximize the performance of the ensemble itself. In this paper, we propose a novel evaluation method for simulated annealing that combines dataset sampling and image downscaling to accelerate the parameter optimization of medical image segmentation ensembles. The scaling levels and sample sizes required to maintain the convergence of the search are theoretically determined by adapting previous results for simulated annealing with imprecise energy measurements. To demonstrate the efficiency of the proposed method, we optimize the parameters of an ensemble for lung segmentation in CT scans. Our experimental results show that the proposed method can maintain the solution quality of the base method with significantly lower runtime. In our problem, optimization with simulated annealing yielded an F1 score of 0.9397 and an associated MCC of 0.7757. Our proposed method maintained the solution quality with an F1 score of 0.9395 and MCC of 0.7755 while exhibiting a 42.01% reduction in runtime. It was also shown that the proposed method is more efficient than simulated annealing with only sampling-based evaluation when the dataset size is below a problem-specific threshold.
Research on Key Technology of Ship Re-Identification Based on the USV-UAV Collaboration

Distinguishing ship identities is critical in ensuring the safety and supervision of the marine agriculture and transportation industry. In this paper, we present a comprehensive investigation and validation of the progression of ship re-identification technology within a cooperative framework predominantly governed by UAVs. Our research revolves around the creation of a ship ReID dataset, the creation of a ship ReID dataset, the development of a feature extraction network, ranking optimization, and the establishment of a ship identity re-identification system built upon the collaboration of unmanned surface vehicles (USVs) and unmanned aerial vehicles (UAVs). We introduce a ship ReID dataset named VesselID-700, comprising 56,069 images covering seven classes of typical ships. We also simulated the multi-angle acquisition state of UAVs to categorize the ship orientations within this dataset. To address the challenge of distinguishing between ships with small inter-class differences and large intra-class variations, we propose a fine-grained feature extraction network called FGFN. FGFN enhances the ResNet architecture with a self-attentive mechanism and generalized mean pooling. We also introduce a multi-task loss function that combines classification and triplet loss, incorporating hard sample mining. Ablation experiments on the VesselID-700 dataset demonstrate that the FGFN network achieves outstanding performance, with a Rank-1 accuracy of 89.78% and mAP of 65.72% at a state-of-the-art level. Generalization experiments on pedestrian and vehicle ReID datasets reveal that FGFN excels in recognizing other rigid body targets and diverse viewpoints. Furthermore, to further enhance the advantages of UAV-USV synergy in ship ReID performance, we propose a ranking optimization method based on the homologous fusion of multi-angle UAVs and heterologous fusion of USV-UAV collaborative architecture. This optimization leads to a significant 3% improvement in Rank-1 performance, accompanied by a 73% reduction in retrieval time cost.