Reviewerly/exHarmony huggingface dataset:
A hybrid low-light image enhancement method using Retinex decomposition and deep light curve estimation

Generally, images acquired under low-light conditions suffer from contrast loss, blurred scene details and colour distortion, resulting in poor visibility. It conceals certain features helpful to computer vision tasks. Hence, a low-light image enhancement is vital in improving the quality of such images. Inspired by Retinex theory, different low-light image enhancement methods implement light correction and deterioration minimization independently in the illumination and reflectance components produced by decomposing the given low-light image. Besides, Zero-DCE is a recent low-light image enhancement method that utilizes deep light curve estimation with the DCE-Net model. In this paper, we propose a hybrid low-light image enhancement method that combines the Zero-DCE method and Retinex decomposition for better performance. It employs deep light curve estimation of the Zero-DCE method on the illuminance component obtained by the Joint intrinsic-extrinsic prior model for Retinex decomposition. To validate, we conducted experiments on the SICE dataset and compared the results obtained with different methods qualitatively and quantitatively. It is evident from the results that our proposed method performed superior when compared to the state-of-the-art methods.
Edge-Enabled: A Scalable and Decentralized Data Aggregation Scheme for IoT

The data aggregation technique has been widely adopted in the Internet of Things (IoT) to protect data privacy while ensuring data availability. Homomorphic encryption is a typical technique that guarantees accurate computing results. However, it brings heavy computation overhead for edge nodes and exposes the aggregated results to the central server, which significantly threatens the confidentiality of results. This article gets rid of the server-centric style existing in most data aggregation schemes and proposes a scalable and decentralized data aggregation scheme for edge-enabled IoT. In the proposed scheme, edge nodes can freely form, join, and exit from the data aggregation group to aggregate data correctly, securely, and efficiently. Besides, two structure-based data aggregation methods are proposed to reduce the aggregation overhead to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$O(n\sqrt{n})$</tex-math></inline-formula> with constant rounds, as opposed to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$O(n\log n)$</tex-math></inline-formula> with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$O(n)$</tex-math></inline-formula> round. Symmetric encryption and online/offline signature computation are adopted to mitigate the online computation burden. Moreover, the proposed scheme can rigorously defend against forgery attack, eavesdropping attack, and collusion attack. The performance evaluation and experiment results show that the proposed scheme improves the efficiency of communication with affordable computation costs for edge nodes.
Challenges and Opportunities in Approximate Bayesian Deep Learning for Intelligent IoT Systems

Approximate Bayesian deep learning methods hold significant promise for addressing several issues that occur when deploying deep learning components in intelligent systems, including mitigating the occurrence of over-confident errors and providing enhanced robustness to out of distribution examples. However, the computational requirements of existing approxi-mate Bayesian inference methods can make them ill-suited for deployment in intelligent IoT systems that include lower-powered edge devices. In this paper, we present a range of approximate Bayesian inference methods for supervised deep learning and highlight the challenges and opportunities when applying these methods on current edge hardware. We highlight several potential solutions to decreasing model storage requirements and improving computational scalability, including model pruning and distillation methods.
Domain-Informed Neural Networks for Interaction Localization Within Astroparticle Experiments

This work proposes a domain-informed neural network architecture for experimental particle physics, using particle interaction localization with the time-projection chamber (TPC) technology for dark matter research as an example application. A key feature of the signals generated within the TPC is that they allow localization of particle interactions through a process called reconstruction (i.e., inverse-problem regression). While multilayer perceptrons (MLPs) have emerged as a leading contender for reconstruction in TPCs, such a black-box approach does not reflect prior knowledge of the underlying scientific processes. This paper looks anew at neural network-based interaction localization and encodes prior detector knowledge, in terms of both signal characteristics and detector geometry, into the feature encoding and the output layers of a multilayer (deep) neural network. The resulting neural network, termed Domain-informed Neural Network (DiNN), limits the receptive fields of the neurons in the initial feature encoding layers in order to account for the spatially localized nature of the signals produced within the TPC. This aspect of the DiNN, which has similarities with the emerging area of graph neural networks in that the neurons in the initial layers only connect to a handful of neurons in their succeeding layer, significantly reduces the number of parameters in the network in comparison to an MLP. In addition, in order to account for the detector geometry, the output layers of the network are modified using two geometric transformations to ensure the DiNN produces localizations within the interior of the detector. The end result is a neural network architecture that has 60% fewer parameters than an MLP, but that still achieves similar localization performance and provides a path to future architectural developments with improved performance because of their ability to encode additional domain knowledge into the architecture.
CRPC-DB a Discourse Bank for Portuguese

We present a new resource for discourse studies in Portuguese, the CRPC Discourse Bank (CRPC-DB). CRPC-DB follows the Penn Discourse Treebank style of annotation. The annotation is performed on the PAROLE corpus, a free subset of the Reference Corpus of Contemporary Portuguese (CRPC) that includes news, fiction and didactic/scientific texts. The discourse bank covers explicit and implicit relations at intra and inter-sentential levels, and includes for now a total of 14,436 discourse relations. We present the main guidelines of our annotation and discuss specific cases. An experiment in inter-annotator agreement was performed and holds results of 0.88 F1-score for discourse relation identification, 0.71 Cohen’s K for the classification of discourse relation types, and 0,75 for top-level sense classification. The CRPC-DB will be distributed free of charge through the PORTULAN CLARIN infrastructure.
Transforming Hand Drawn Wireframes into Front-End Code with Deep Learning

The way towards generating a website front end involves a designer settling on an idea for what kind of layout they want the website to have, then proceeding to plan and implement each aspect one by one until they have converted what they initially laid out into its Html front end form, this process can take a considerable time, especially considering the first draft of the design is traditionally never the final one.This process can take up a large amount of resource real estate, and as we have laid out in this paper, by using a Model consisting of various Neural Networks trained on a custom dataset.It can be automated into assisting designers, allowing them to focus on the other more complicated parts of the system they are designing by quickly generating what would rather be straightforward busywork.Over the past 20 years, the boom in how much the internet is used and the sheer volume of pages on it demands a high level of work and time to create them.For the efficiency of the process, we proposed a multi-model-based architecture on image captioning, consisting of Convolutional neural network (CNN) and Long short-term memory (LSTM) models.Our proposed approach trained on our custom-made database can be automated into assisting designers, allowing them to focus on the other more complicated part of the system.We trained our model in several batches over a custom-made dataset consisting of over 6300 files and were finally able to achieve a Bilingual Evaluation Understudy (BLEU) score for a batch of 50 hand-drawn images at 87.86%
DeepAIA: An Automatic Image Annotation Model Based on Generative Adversarial Networks and Transfer Learning

Automatic image annotation (AIA) has been adopted in different applications such as image retrieval and classification.Deep Learning is used in AIA to extract image features and then convert these features into text descriptions and labels.However, conventional AIA models that employ deep learning methods suffer from various shortcomings, such as poor annotation performance.This work proposes an AIA model based on convolutional neural networks (CNNs), generative adversarial networks (GANs), and transfer learning.GANs have attracted a lot of interest because of its ability to generate data without explicitly using probability density.Thus, it has proven its usefulness in image annotation and image augmentation.In this work, an Auxiliary classifier-GAN (ACGAN) has been used, where the discriminator predicts the class of an image rather than taking it as a given input; therefore, the stabilization of the training stage is ensured, and the generation of high-quality images is provided.Transfer learning is also used to enhance the performance of the classification.The proposed model outperforms the best state-of-the-art models in terms of MiAP, F-measure and error rate using ImageClef, ESPGame and IAPR-TC12 datasets.
Generalization Performance of Pure Accuracy and its Application in Selective Ensemble Learning

The pure accuracy measure is used to eliminate random consistency from the accuracy measure. Biases to both majority and minority classes in the pure accuracy are lower than that in the accuracy measure. In this paper, we demonstrate that compared with the accuracy measure and F-measure, the pure accuracy measure is class distribution insensitive and discriminative for good classifiers. The advantages make the pure accuracy measure suitable for traditional classification. Further, we mainly focus on two points: exploring a tighter generalization bound on pure accuracy based learning paradigm and designing a learning algorithm based on the pure accuracy measure. Particularly, with the self-bounding property, we build an algorithm-independent generalization bound on the pure accuracy measure, which is tighter than the existing bound of an order O(1/√N) (N is the number of instances). The proposed bound is free from making a smoothness or convex assumption on the hypothesis functions. In addition, we design a learning algorithm optimizing the pure accuracy measure and use it in the selective ensemble learning setting. The experiments on sixteen benchmark data sets and four image data sets demonstrate that the proposed method statistically performs better than the other eight representative benchmark algorithms.
Distribution Augmentation for Low-Resource Expressive Text-To-Speech

This paper presents a novel data augmentation technique for text-to-speech (TTS), that allows to generate new (text, audio) training examples without requiring any additional data. Our goal is to in-crease diversity of text conditionings available during training. This helps to reduce overfitting, especially in low-resource settings. Our method relies on substituting text and audio fragments in a way that preserves syntactical correctness. We take additional measures to ensure that synthesized speech does not contain artifacts caused by combining inconsistent audio samples. The perceptual evaluations show that our method improves speech quality over a number of datasets, speakers, and TTS architectures. We also demonstrate that it greatly improves robustness of attention-based TTS models.
Are translated Chinese <i>Wuxia</i> fiction and western heroic literature similar? A stylometric analysis based on stylistic panoramas

Abstract This study investigates the extent to which English translations of Chinese Wuxia fiction and Western heroic literature in modern English are stylistically similar through stylometric analyses. It adds to literary translation research by highlighting possible stylistic connections between heroic literature in the East and that in the West, clues that may help understand the current reception of Wuxia translations. It also contributes to stylometric studies by introducing the stylistic panorama, a novel concept proposed to describe the stylistic picture of a (translated) text in a relatively holistic and functional way. Examining six English translations of Wuxia novels and twelve chivalric stories and heroic fantasies in modern English, the study finds that the Wuxia translations differ from the two Western subgenres in stylistic panoramas built by formal features (dispersion of word lengths and average sentence length), as well as the most frequent words (MFW) and the MFW-sequences. Such differences have foregrounded the unique stylistic features (richer Wuxia-specific vocabularies and shorter paragraph lengths) of these translations, which has contributed in part to their favorable reception among English-speaking readers. It is hoped that this study will encourage new applications for the concept of stylistic panoramas in future stylometric studies.
Homography-Based Loss Function for Camera Pose Regression

Some recent visual-based relocalization algorithms rely on deep learning methods to perform camera pose regression from image data. This paper focuses on the loss functions that embed the error between two poses to perform deep learning based camera pose regression. Existing loss functions are either difficult-to-tune multi-objective functions or present unstable reprojection errors that rely on ground truth 3D scene points and require a two-step training. To deal with these issues, we introduce a novel loss function which is based on a multiplane homography integration. This new function does not require prior initialization and only depends on physically interpretable hyperparameters. Furthermore, the experiments carried out on well established relocalization datasets show that it minimizes best the mean square reprojection error during training when compared with existing loss functions.
On Utility and Privacy in Synthetic Genomic Data

The availability of genomic data is essential to progress in biomedical research, personalized medicine, etc.However, its extreme sensitivity makes it problematic, if not outright impossible, to publish or share it.As a result, several initiatives have been launched to experiment with synthetic genomic data, e.g., using generative models to learn the underlying distribution of the real data and generate artificial datasets that preserve its salient characteristics without exposing it.This paper provides the first evaluation of both utility and privacy protection of six state-of-the-art models for generating synthetic genomic data.We assess the performance of the synthetic data on several common tasks, such as allele population statistics and linkage disequilibrium.We then measure privacy through the lens of membership inference attacks, i.e., inferring whether a record was part of the training data.Our experiments show that no single approach to generate synthetic genomic data yields both high utility and strong privacy across the board.Also, the size and nature of the training dataset matter.Moreover, while some combinations of datasets and models produce synthetic data with distributions close to the real data, there often are target data points that are vulnerable to membership inference.Looking forward, our techniques can be used by practitioners to assess the risks of deploying synthetic genomic data in the wild and serve as a benchmark for future work.
The effectiveness of the augmented reality application in foreign language teaching in higher school

In this study paper we consider the application of the augmented reality for teaching a foreign language and its influence on the education environment throughout the world since educators are trying to find the relevant methods of teaching to better face the time challenges connected with the expansion of the computer technologies. The given paper aims at validating the idea of the effectiveness of the application of the augmented reality techniques in teaching a foreign language at the University. The survey conducted by the authors comprises 64 participants of the Rostov State Transport University of the HR Department; the scrutiny endorses the proposed hypothesis.
Recommendation Engine of Learning Contents and Activities Based on Learning Analytics

Recommendation engines are being increasingly deployed into the e-learning systems. This paper proposes a software architecture for recommending learning content and learning activities, which has been validated by means of a case study. The main goal of that architecture is to achieve better recommendations of learning content and learning activities not only in systems, but also in similar e-learning environments.
Deadline and Reliability Aware Multiserver Configuration Optimization for Maximizing Profit

Maximizing profit is a key goal for cloud service providers in the modern cloud business market. Service revenue and business cost are two major factors in determining profit and highly depend on multiserver configuration. Understanding the relationship between multiserver configuration and profit is important to service providers. Although existing articles have explored this issue, few of them consider deadline miss rate and soft error reliability of cloud services in multiserver configuration for profit maximization. Since deadline misses violate cloud services’ real-time requirements and soft error prevents successful processing of cloud services, it is necessary to consider the impact of deadline miss rate and soft error reliability on service providers’ profits when configuring the multiserver. This article introduces a deadline miss rate and soft error reliability aware multiserver configuration scheme for maximizing cloud service providers’ profit. Specifically, we derive the deadline miss rate considering the heterogeneity of cloud service requests, and propose an analytical method to compute the soft error reliability of multiserver systems. Based on the new deadline miss rate and soft error reliability models, we formulate the multiserver configuration optimization problem and introduce an augmented Lagrange multiplier-based iterative method to find the optimal multiserver configuration. Extensive experiments evaluate the efficacy of the proposed multiserver configuration approach. Compared with the two state-of-the-art methods, the profit gained by our scheme can be up to 11.92% higher.
A Neuromorphic Model With Delay-Based Reservoir for Continuous Ventricular Heartbeat Detection

There is a growing interest in neuromorphic hardware since it offers a more intuitive way to achieve bio-inspired algorithms. This paper presents a neuromorphic model for intelligently processing continuous electrocardiogram (ECG) signal. This model aims to develop a hardware-based signal processing model and avoid employing digitally intensive operations, such as signal segmentation and feature extraction, which are not desired in an analogue neuromorphic system. We apply delay-based reservoir computing as the information processing core, along with a novel training and labelling method. Different from the conventional ECG classification techniques, this computation model is a end-to-end dynamic system that mimics the real-time signal flow in neuromorphic hardware. The input is the raw ECG stream, while the amplitude of the output represents the risk factor of a ventricular ectopic heartbeat. The intrinsic memristive property of the reservoir empowers the system to retain the historical ECG information for high-dimensional mapping. This model was evaluated with the MIT-BIH database under the inter-patient paradigm and yields 81% sensitivity and 98% accuracy. Under this architecture, the minimum size of memory required in the inference process can be as low as 3.1 MegaByte(MB) because the majority of the computation takes place in the analogue domain. Such computational modelling boosts memory efficiency by simplifying the computing procedure and minimizing the required memory for future wearable devices.
Semi-automatic marking system for robot competition based on PyQT5

In RoboCup kid-size group competition, it’s necessary to label field information, but the debugging time for each team before the competition is limited. In addition, a certain amount of time needs to be set aside for model training, which leads to the time of field labeling is not enough. Most teams use manual labeling tools to label the game field information, which is often inefficient and takes a lot of time. This paper aims to reduce the time required for field information labeling before the competition. We present a semi-automatic labeling system for robot competitions, which provides manual and semi-automatic labeling methods. The client interface is designed by PyQT5 framework, which cooperates with the back-end SiamRPN algorithm to provide a semi-automatic labeling function. In order to decouple the client interface and the back-end algorithm, the RPyC framework is used to connect the front and back-end calling interfaces. After testing, the labeling speed of RoboCup competition field in the system is greatly improved and the debugging time for other robot modules is more abundant.
Virtual temporal phase-shifting phase extraction using generative adversarial networks

In this paper, a virtual temporal phase-shifting method based on generative adversarial networks (GANs) is proposed to realize dynamic measurement, which is referred to as PSNet. The proposed PSNet can produce the virtual phase-shifting fringe patterns from the single-frame fringe pattern, and the standard n-step phase-shifting method is employed to obtain the wrapped phase from the virtual fringe patterns. The wrapped phase can further be unwrapped by the unwrapping algorithm. Simulation analysis shows that the PSNet can produce the virtual phase-shifting fringe patterns without noise, and thus, the denoising process is eliminated. The performance of the method is verified from the captured fringe in the interferometer, which demonstrates the practicability of the proposed method.
Multi Tasking Synthetic Speech Detection on Indian Languages

Anti-spoofing research plays an important role in audio forensics. It has found a lot of traction in several languages around the world. With that in mind, the purpose of this work is to assess the impact of several synthetic spoofing detection approaches on a multilingual, low-constrained Indian language set. This paper aims at a multitasking spoofing detection by identifying real/spoof utterance identification as well as the regional language spoofing attack vector. To accomplish this, the features and the classifiers that are best candidate for the synthetic spoofing detection and language identification are appropriately chosen. Our methodology compares the performances of three main different classifiers GMM, SVM, DNN on the vector formulated from the accumulation of MFCC features. Hindi, Malayalam, Tamil, Telugu are the four languages which are taken into account for the comparison. Out of these classifiers, SVM and DNN are found to give the best results with EER rates of 1.98 % and 1.19 % respectively.