Reviewerly/exHarmony huggingface dataset:
Towards end-to-end F0 voice conversion based on Dual-GAN with convolutional wavelet kernels

This paper presents a end-to-end framework for the F0 transformation in the context of expressive voice conversion. A single neural network is proposed, in which a first module is used to learn F0 representation over different temporal scales and a second adversarial module is used to learn the transformation from one emotion to another. The first module is composed of a convolution layer with wavelet kernels so that the various temporal scales of F0 variations can be efficiently encoded. The single decomposition/transformation network allows to learn in a end-to-end manner the F0 decomposition that are optimal with respect to the transformation, directly from the raw F0 signal.
Medical Image Denoising Techniques against Hazardous Noises: An IQA Metrics Based Comparative Analysis

Medical imaging has become a vital part of the early detection, diagnosis, and treatment of many diseases.That's why image denoising is considered as a crucial pre-processing step in medical imaging to restore the original image from its noisy circumstance without losing image features, such as edges, corners, and other sharp structures.Ultrasound (US), Computed Tomography (CT), and Magnetic Resonance (MR) are the most widely used medical imaging techniques that are often corrupted by hazardous noises, namely, speckle, salt and pepper, Poisson, and Gaussian.To remove noises from medical images, researchers have proposed several denoising methods.Each method has its assumptions, merits, and demerits.In this paper, a detailed comparative analysis of different denoising filtering techniques, for example, median, Wiener, mean, hybrid median, Gaussian, bilateral, non-local means, and anisotropic diffusion are performed based on four widely-used image quality assessment (IQA) metrics, such as Root Mean Squared Error (RMSE), Peak Signal to Noise Ratio (PSNR), Mean Absolute Error (MAE), and Structural Similarity Index (SSIM).The results obtained in this present work reveal that Gaussian, median, anisotropic diffusion, and nonlocal means filtering methods perform extraordinarily to denoise speckle, salt and pepper, Poisson, and Gaussian noises, respectively, from all US, CT, and MR images.
Quasi-inversion of quantum and classical channels in finite dimensions

Abstract We introduce the concept of quasi-inverse of quantum and classical channels, prove general properties of these inverses and determine them for a large class of channels acting in an arbitrary finite dimension. Therefore we extend the previous results of Karimipour et al (2020 Phys. Rev. A 101 032109) to arbitrary dimensional channels and to the classical domain. We demonstrate how application of the proposed scheme can increase on the average the fidelity between a given random pure state and its image transformed by the quantum channel followed by its quasi-inversion.
Multi-UAV coordinated path planning based on many-objective optimization

With the increase in the application areas of unmanned aerial vehicles (UAVs), the problem of coordinated path planning for multi-UAVs becomes more and more significant. However, most of the existing methods optimize this problem by converting multi-objective weighting into a single-objective problem. To reduce the subjectivity of multi-objective weighting, a coordinated path planning model based on many-objective optimization is proposed to optimize the multi-UAVs track distance cost, track threat cost, track energy cost, and coordination performance. In the many-objective evolutionary algorithm (MaOEA), the convergence and diversity of the individuals are both important indicators to evaluate the performance of individuals. To solve the problem that individuals in the algorithm are not easy to evaluate, an NSGA-III algorithm based on the individual evaluation mating strategy (NSGAIII-ICO) is proposed, which can comprehensively evaluate the performance of individuals according to the generation of the algorithm and guide the mating operation. Simulation results show that this model can effectively provide coordinated tracks for multi-UAVs, and by comparing it with other MaOEAs, it can be proved that this proposed algorithm can effectively improve the performance of coordinated path planning for multi-UAVs.
On the Construction of Teachers’ Professional Quality-oriented English Practice Teaching System—Exemplified with the English Major of Sichuan University of Arts and Science

Professional quality is the core element of training talents. Developing elegant professional qualities is the thirsty demand for students and the great mission of higher education. With the main purpose to train would-be qualified English teachers, English major is supposed to build an English practice teaching system directed by teachers’ professional quality on the basis of Certification Standard of Secondary Education, so that students can gradually follow professional principles, understand teaching methodology, start educational task and seek self-improvement in practice teaching and learning activities, subsequently helping to lay a solid foundation for students’ future professional ability development.
<i>If</i>-conditionals: Corpus-based classification and frequency distribution

Sciendo provides publishing services and solutions to academic and professional organizations and individual authors. We publish journals, books, conference proceedings and a variety of other publications.
Manipulation Attacks in Local Differential Privacy

Local differential privacy is a widely studied restriction on distributed algorithms that collect aggregates about sensitive user data, and is now deployed in several large systems. We initiate a systematic study of a fundamental limitation of locally differentially private protocols: they are highly vulnerable to adversarial manipulation. While any algorithm can be manipulated by adversaries who lie about their inputs, we show that any noninteractive locally differentially private protocol can be manipulated to a much greater extent---when the privacy level is high, or the domain size is large, a small fraction of users in the protocol can completely obscure the distribution of the honest users' input. We also construct protocols that are optimally robust to manipulation for a variety of common tasks in local differential privacy. Finally, we give simple experiments validating our theoretical results, and demonstrating that protocols that are optimal without manipulation can have dramatically different levels of robustness to manipulation. Our results suggest caution when deploying local differential privacy and reinforce the importance of efficient cryptographic techniques for the distributed emulation of centrally differentially private mechanisms.
Information Broadcasting Concerning Publicity and Public Participation in Development Plan

Malaysian Law stipulates the public society should be involved in development planning to assure thorough and comprehensive planning is made to fulfill the needs and aspirations of the society coming from multiracial and ethnicities. However, their knowledge about public participation in development planning is still in question. The participation is in fact a great opportunity for the society to play a part in shaping and influencing the objective and direction of future developments. Public participation is required in every stages of development planning such as Local Plan to ensure effectiveness of the planning system. Therefore it is crucial for the information concerning the publicity and public participation to reach targetted group through accessible broadcasting method that matched with present-day. This paper reviews their knowledge towards public participation programs. This quantitative study also discovers respondents' views on the method of information broadcasting through social media. The result shows their knowledge about public participation in planning process is considerately very low. Additionally, the study discovers that social media is highly influential among respondents where it should be used to increase public participation in development planning.
Unmasking the Mask – Evaluating Social Biases in Masked Language Models

Masked Language Models (MLMs) have shown superior performances in numerous downstream Natural Language Processing (NLP) tasks. Unfortunately, MLMs also demonstrate significantly worrying levels of social biases. We show that the previously proposed evaluation metrics for quantifying the social biases in MLMs are problematic due to the following reasons: (1) prediction accuracy of the masked tokens itself tend to be low in some MLMs, which leads to unreliable evaluation metrics, and (2) in most downstream NLP tasks, masks are not used; therefore prediction of the mask is not directly related to them, and (3) high-frequency words in the training data are masked more often, introducing noise due to this selection bias in the test cases. Therefore, we propose All Unmasked Likelihood (AUL), a bias evaluation measure that predicts all tokens in a test case given the MLM embedding of the unmasked input and AUL with Attention weights (AULA) to evaluate tokens based on their importance in a sentence. Our experimental results show that the proposed bias evaluation measures accurately detect different types of biases in MLMs, and unlike AUL and AULA, previously proposed measures for MLMs systematically overestimate the measured biases and are heavily influenced by the unmasked tokens in the context.
Joint Learning of Hyperbolic Label Embeddings for Hierarchical Multi-label Classification

We consider the problem of multi-label classification, where the labels lie in a hierarchy.However, unlike most existing works in hierarchical multi-label classification, we do not assume that the label-hierarchy is known.Encouraged by the recent success of hyperbolic embeddings in capturing hierarchical relations, we propose to jointly learn the classifier parameters as well as the label embeddings.Such a joint learning is expected to provide a twofold advantage: i) the classifier generalises better as it leverages the prior knowledge of existence of a hierarchy over the labels, and ii) in addition to the label co-occurrence information, the label-embedding may benefit from the manifold structure of the input datapoints, leading to embeddings that are more faithful to the label hierarchy.We propose a novel formulation for the joint learning and empirically evaluate its efficacy.The results show that the joint learning improves over the baseline that employs label co-occurrence based pre-trained hyperbolic embeddings.Moreover, the proposed classifiers achieve state-of-the-art generalization on standard benchmarks.We also present evaluation of the hyperbolic embeddings obtained by joint learning and show that they represent the hierarchy more accurately than the other alternatives.The source code of the paper is available here.
An Interactive Recommendation System for Decision Making Based on the Characterization of Cognitive Tasks

The decision-making process can be complex and underestimated, where mismanagement could lead to poor results and excessive spending. This situation appears in highly complex multi-criteria problems such as the project portfolio selection (PPS) problem. Therefore, a recommender system becomes crucial to guide the solution search process. To our knowledge, most recommender systems that use argumentation theory are not proposed for multi-criteria optimization problems. Besides, most of the current recommender systems focused on PPS problems do not attempt to justify their recommendations. This work studies the characterization of cognitive tasks involved in the decision-aiding process to propose a framework for the Decision Aid Interactive Recommender System (DAIRS). The proposed system focuses on a user-system interaction that guides the search towards the best solution considering a decision-maker’s preferences. The developed framework uses argumentation theory supported by argumentation schemes, dialogue games, proof standards, and two state transition diagrams (STD) to generate and explain its recommendations to the user. This work presents a prototype of DAIRS to evaluate the user experience on multiple real-life case simulations through a usability measurement. The prototype and both STDs received a satisfying score and mostly overall acceptance by the test users.
An exponent one-fifth algorithm for deterministic integer factorisation

Hittmeir recently presented a deterministic algorithm that provably computes the prime factorisation of a positive integer $N$ in $N^{2/9+o(1)}$ bit operations. Prior to this breakthrough, the best known complexity bound for this problem was $N^{1/4+o(1)}$, a result going back to the 1970s. In this paper we push Hittmeirâs techniques further, obtaining a rigorous, deterministic factoring algorithm with complexity $N^{1/5+o(1)}$.
Convolutional Neural Network Based Advertisement Classification Models for Online English Newspapers

Image processing for knowledge management and effective information extraction is the key element for steering towards society 5.0. There has been a substantial research and progress in the area of image recognition and classification in the recent years but at the same time, there is a lack of significant work in the field of advertisement image classification from online English newspapers. This research paper analyses and compares various popular image classification techniques to find out the most suitable technique for advertisement image classification problem. Automatic feature extraction without any prior knowledge of features makes Convolutional Neural Networks (CNN) the most suitable technique for advertisement image classification. This paper further explores and implements three different CNN-based image classification models that can classify advertisement images from online English newspapers into four pre-defined categories including Admission-notices, Job-advertisements, Sales and Promotional advertisements and Tenders. These models are trained and tested on an advertisement image dataset collected from four different online English newspapers over a time frame of 15 months. Fine-tuned ResNet50 Model using ‘Transfer-learning’ is found to be the most suitable model for this advertisement image classification task with results exhibiting around 74% accuracy. This CNN-model based automated classification of advertisement images will help newspaper readers in performing exhaustive advertisement search in a category of their own interest, saving the time and efforts of sequential manual search across a range of multiple newspapers. Also, the proposed research will help in performing advertisement analysis and studies.
Topic extraction from extremely short texts with variational manifold regularization

With the emerging of massive short texts, e.g., social media posts and question titles from Q&A systems, discovering valuable information from them is increasingly significant for many real-world applications of content analysis. The family of topic modeling can effectively explore the hidden structures of documents through the assumptions of latent topics. However, due to the sparseness of short texts, the existing topic models, e.g., latent Dirichlet allocation, lose effectiveness on them. To this end, an effective solution, namely Dirichlet multinomial mixture (DMM), supposing that each short text is only associated with a single topic, indirectly enriches document-level word co-occurrences. However, DMM is sensitive to noisy words, where it often learns inaccurate topic representations at the document level. To address this problem, we extend DMM to a novel Laplacian Dirichlet Multinomial Mixture (LapDMM) topic model for short texts. The basic idea of LapDMM is to preserve local neighborhood structures of short texts, enabling to spread topical signals among neighboring documents, so as to modify the inaccurate topic representations. This is achieved by incorporating the variational manifold regularization into the variational objective of DMM, constraining the close short texts with similar variational topic representations. To find nearest neighbors of short texts, before model inference, we construct an offline document graph, where the distances of short texts can be computed by the word mover’s distance. We further develop an online version of LapDMM, namely Online LapDMM, to achieve inference speedup on massive short texts. Carrying this implications, we exploit the spirit of stochastic optimization with mini-batches and an up-to-date document graph that can efficiently find approximate nearest neighbors instead. To evaluate our models, we compare against the state-of-the-art short text topic models on several traditional tasks, i.e., topic quality, document clustering and classification. The empirical results demonstrate that our models achieve very significant performance gains over the baseline models.
Korean Grammatical Error Correction Based on Transformer with Copying Mechanisms and Grammatical Noise Implantation Methods

Grammatical Error Correction (GEC) is the task of detecting and correcting various grammatical errors in texts. Many previous approaches to the GEC have used various mechanisms including rules, statistics, and their combinations. Recently, the performance of the GEC in English has been drastically enhanced due to the vigorous applications of deep neural networks and pretrained language models. Following the promising results of the English GEC tasks, we apply the Transformer with Copying Mechanism into the Korean GEC task by introducing novel and effective noising methods for constructing Korean GEC datasets. Our comparative experiments showed that the proposed system outperforms two commercial grammar check and other NMT-based models.
PunKtuator: A Multilingual Punctuation Restoration System for Spoken and Written Text

Text transcripts without punctuation or sentence boundaries are hard to comprehend for both humans and machines.Punctuation marks play a vital role by providing meaning to the sentence and incorrect use or placement of punctuation marks can often alter it.This can impact downstream tasks such as language translation and understanding, pronoun resolution, text summarization, etc. for humans and machines.An automated punctuation restoration (APR) system with minimal human intervention can improve comprehension of text and help users write better.In this paper we describe a multitask modeling approach as a system to restore punctuation in multiple high resource -Germanic (English and German), Romanic (French)-and low resource languages -Indo-Aryan (Hindi) Dravidian (Tamil) -that does not require extensive knowledge of grammar or syntax of a given language for both spoken and written form of text.For German language and the given Indic based languages this is the first towards restoring punctuation and can serve as a baseline for future work.
Analysis of PV Subsidy Schemes, Installed Capacity and Their Electricity Generation in Japan

Solar PV capacity growth in Japan has been facilitated by a number of government schemes that have been implemented since 1994. Publicly available capacity data are provided by a number of agencies and organisations at various resolutions and at different stages within these schemes. This study provides a comprehensive review of solar PV data sources in Japan between 1994 and 2019, as well as an introduction to the subsidy schemes and organisations involved in scheme management and data collection in Japan. As a result, the authors produced their own dataset of installed capacities and generation across time for the various regions. Lastly, this study provides insights and recommendations to policy makers regarding opportunities for improving the accessibility and quality of data from a user perspective and to enhance Japan’s presence in international research.
BART-TL: Weakly-Supervised Topic Label Generation

We propose a novel solution for assigning labels to topic models by using multiple weak labelers.The method leverages generative transformers to learn accurate representations of the most important topic terms and candidate labels.This is achieved by fine-tuning pretrained BART models on a large number of potential labels generated by state of the art nonneural models for topic labeling, enriched with different techniques.The proposed BART-TL model is able to generate valuable and novel labels in a weakly-supervised manner and can be improved by adding other weak labelers or distant supervision on similar tasks.
Subband Adaptive Image Deblocking Using Wavelet Based Convolutional Neural Networks

In this paper, we propose subband adaptive image deblocking using wavelet based convolutional neural networks (CNNs). We build wavelet based CNNs for image deblocking to achieve subband adaptive reconstruction. First, we perform subband adaptive processing after the discrete wavelet transform (DWT) on the input image. For low frequency subband (LL), we use a simple and effective shallow CNN to restore the low frequency component, while for high frequency subbands (LH, HL, and HH) we utilize multi-kernel convolution to capture multiscale features and restore sparse high frequency components. Then, we conduct mixed convolution of dilated convolution and standard convolution to expand the receptive field while introducing channel and spatial attentions to adjust the proportion of different subbands and spatial coordinates. Various experiments on Classic5 and LIVE1 datasets show that the proposed method successfully recovers sharp edges and clear textures in highly compressed images while removing compression artifacts such as blocking and banding. Moreover, the proposed method achieves comparable state-of-the-art performance on compression artifact removal in terms of both visual quality and quantitative measurements.
Multijob Associated Task Scheduling for Cloud Computing Based on Task Duplication and Insertion

With the emergence and development of various computer technologies, many jobs processed in cloud computing systems consist of multiple associated tasks which follow the constraint of execution order. The task of each job can be assigned to different nodes for execution, and the relevant data are transmitted between nodes to complete the job processing. The computing or communication capabilities of each node may be different due to processor heterogeneity, and hence, a task scheduling algorithm is of great significance for job processing performance. An efficient task scheduling algorithm can make full use of resources and improve the performance of job processing. The performance of existing research on associated task scheduling for multiple jobs needs to be improved. Therefore, this paper studies the problem of multijob associated task scheduling with the goal of minimizing the jobs’ makespan. This paper proposes a task Duplication and Insertion algorithm based on List Scheduling (DILS) which incorporates dynamic finish time prediction, task replication, and task insertion. The algorithm dynamically schedules tasks by predicting the completion time of tasks according to the scheduling of previously scheduled tasks, replicates tasks on different nodes, reduces transmission time, and inserts tasks into idle time slots to speed up task execution. Experimental results demonstrate that our algorithm can effectively reduce the jobs’ makespan.