Reviewerly/exHarmony huggingface dataset:
AccentSpeech: Learning Accent from Crowd-sourced Data for Target Speaker TTS with Accents

Learning accent from crowd-sourced data is a feasible way to achieve a target speaker TTS system that can synthesize accent speech. To this end, there are two challenging problems to be solved. First, direct use of the poor acoustic quality crowdsourced data and the target speaker data in accent transfer will apparently lead to synthetic speech with degraded quality. To mitigate this problem, we take a bottleneck feature (BN) based TTS approach, in which TTS is decomposed into a Text-to-BN (T2BN) module to learn accent and a BN-to-Mel (BN2Me1) module to learn speaker timbre, where neural network based BN feature serves as the intermediate representation that are robust to noise interference. Second, direct training T2BN using the crowd-sourced data in the two-stage system will produce accent speech of target speaker with poor prosody. This is because the the crowd-sourced recordings are contributed from the ordinary unprofessional speakers. To tackle this problem, we update the two-stage approach to a novel three-stage approach, where T2BN and BN2Me1 are trained using the high-quality target speaker data and a new BN-to-BN module is plugged in between the two modules to perform accent transfer. To train the BN2BN module, the parallel unaccented and accented BN features are obtained by a proposed data augmentation procedure. Finally the proposed three-stage approach manages to produce accent speech for the target speaker with good prosody, as the prosody pattern is inherited from the professional target speaker and accent transfer is achieved by the BN2BN module at the same time. The proposed approach, named as AccentSpeech, is validated in a Mandarin TTS accent transfer task.
Blockwise Spectral Analysis for Deepfake Detection in High-fidelity Videos

Deepfakes have gained widespread attention as they may give rise to a series of risks ranging from personal reputation damages to national security breaches. A mainstream approach to generating deepfakes is based on Generative Adversarial Networks (GAN). Various methods have been proposed to detect GAN-generated fake content. However, most of them only target a specific GAN and do not generalize well to other unseen GAN architectures. Moreover, many existing methods show poor performance on deepfakes that are imperceptible to the human eye as they heavily rely on visual artifacts, such as unblinking eyes and asymmetric faces. In this work, we exploit the spectral artifacts left by up-sampling operations that are universally used in GAN architectures for detecting high-fidelity deepfake videos. We first divide video frames into blocks containing the most informative areas, e.g., face, eyes, and mouth areas, and use their spectrum to train a ResNet-based classifier to detect GAN-generated images. Experimental results on public datasets show that our method is effective in detecting high-fidelity deepfakes and generalizes well across GANs with the same or similar up-sampling operations.
GDEGAN: Graphical Discriminative Embedding GAN for tabular data

While generative models achieve remarkable success in recent years, applying them to model tabular data is still challenging. The first problem of tabular data is the categorical encoding scheme, in which each categorical value is represented as a one-hot vector. It leads to the problem of very sparse high dimensional input space, especially when the cardinality of values of each attribute is high. This is problematic to GAN training since a trivial discriminator can simply distinguish real and fake data by checking the distributions sparseness. The second problem in tabular data is its hard constraint and discrete signals property which is challenging for neural networks. The modelling of discrete features is often associated with counting problem where gradient signals are not well-prepared for. As a result, current GAN methods might overlook and ignore these discrete features causing the mode-collapse in tabular data modelling. In this paper, we propose a unified framework to solve these two problems: (i) we propose to embed the raw data into the highlevel features and train GAN these features instead to avoid the trivial sparseness detection by the discriminator (ii) we propose the graphical-conditional vector to encourage GAN to learn to generate the structure information across multiple attributes. The experimental results show that our proposed methods perform much better than the current state-of-the-art method on most tabular benchmark datasets. Source code is available at: https://github.com/dungdinhanh/GDEGAN
Reducing Missingness in a Stream through Cost-Aware Active Feature Acquisition

Missing features can negatively impact the performance of machine learning solutions and past research has been focused on how to acquire the most predictive features in static scenarios. Active Feature Acquisition (AFA) for data streams extends the conventional static paradigm by taking into account that the importance of a feature may change as the stream drifts.In this study, we propose an AFA method that takes the cost of the features for each arriving instance into account and, at the same time, allows multiple features to be acquired at once. This reflects the fact that labels often depend on multiple features, so that acquiring many low-cost features may result in higher improvement than acquiring a single feature, as has been proposed in our earlier work [1]. We evaluated our approach on 7 real and 8 synthetic data sets. We investigated three different budget sizes and three different feature cost configurations for 7 different percentages of missing data on each of our data sets. We use the results of our experiments to elaborate on when the acquisition of multiple features is more or less beneficial than acquiring a single feature. Finally, we show the need for more sophisticated metrics to estimate a feature’s predictive quality.
Maximal gSpan: Multi-Document Summarization through Frequent Subgraph Mining

Multi-document Summarization(MDS) involves ex-traction of salient information from multiple documents to represent it in a compressed yet comprehensible form. Existing approaches towards MDS utilize deep learning models. These approaches are data hungry and employ the complete search space of input documents for generating summaries. On the other hand, frequent subgraph mining(FSM) can be utilized as an unsupervised approach to reduce the search space where only the frequent subgraphs are considered as representative of documents. gSpan is a state-of-the-art FSM algorithm. The problem with using gSpan for generating subgraphs towards summarization is that the resultant subgraphs contain repetitive words that affects summary. To cater this problem, we propose Maximal gSpan, an extension of gSpan that mines maximal frequent subgraphs. These subgraphs contain diverse words hence resulting in better document coverage for summarization. The sentences of the summary are selected from these subgraphs. The proposed approach achieves better results in terms of sum-marization evaluation metric, i.e., ROUGE scores, compared with TextRank, which is another unsupervised graph-based extractive summarization technique.
The Influence of Metacognitive Strategies Training on the Writing Performance in United Arab Emirate

Writing is one of the most difficult components of learning a foreign language. Writing is the most difficult and time-consuming component of the language system. Writing necessitates a combination of orthographic, graphomotor, and other language abilities, as well as semantics, grammar, spelling, and writing standards, which are not limited to the aforementioned talents. The advancement of cognitive psychology, particularly metacognition, has attracted the attention of a growing number of scholars and prepared the way for new dimensions in EFL writing, notably in the area of writing achievement. This study seeks to evaluate the impact of applying metacognitive methods on United Arab Emirate EFL learners’ writing performance due to the method's high executive aptness, which includes formulation, supervision, and assessment. Sixty-eight students were chosen at random from secondary school to participate in the study's experimental control. The experimental group was given metacognitive strategies-based writing training, whereas the control group was given merely regular writing instruction. Both groups were post-tested after five weeks of teaching, and the students were given another post-test at the end of the twelve-week programme. The Wilcoxon Signed-Rank test was used to analyse the data after the independent Mann–Whitney U test. The experimental group's writing performance improved as a result of the study's findings. This study's findings have consequences for both education and future research.
Classification Thyroid Disease Using Multinomial Logistic Regressions (LR)

Classifying using Multinomial Logistic Regression is one of the techniques used in the statistical model. Binomial logistic regression is a form of regression that is used when the dependent is a dichotomy and the independents are continuous variables, categorical variables, or both. Multinomial Logistic Regression Model is used to handle the case of dependent variables with more classes and suitable for non-linear data by using Maximum Likelihood Estimation and Wald Statistics. This experiment aims to predict the Thyroid disease Dataset by using the Multinomial Logistic Regression model. In this experiment, SPSS software is used to run the Multinomial Logistic Regression by Thyroid disease data supplied by Randolf Werner, which was adopted from the UCI Repository of Machine Learning Database.
An investigation of causes and effects of trust in Boundary Artefacts

Boundary Artefacts (BAs) support software development activities in many aspects because it carries lots of information in the same object that can be used and interpreted by several social groups within an organisation. When the BAs are inconsistent regarding their content, such as many meanings or lack of contextual information, their efficiency is reduced because stakeholders won’t trust them. This study aimed to understand the implications of differences in the perception of trust on software projects and their influence on stakeholders’ behaviour. We conducted an exploratory case study to observe the creation and utilisation of one specific BA and the implications of differences in trust and their influence on stakeholders’ behaviour. : Our investigation has shown that practitioners adding and adjusting existing content do not entirely understand the stakeholders’ needs. Together with the partial management of the content, trust is impacted. When the content of BAs does not meet the trust factors, specifically reliability and predictability, the stakeholders can’t execute their tasks appropriately, and several implications affect the software development project. Additionally, they create workarounds to supply their needs. The differences in trust in BAs affect software projects in different areas of the organisation and interfere with the task execution of various stakeholders. The decrease in trust results from inconsistencies in the content associated with the lack of management of the BA. A structured strategy for representing and managing a BA’s content seems appropriate to increase trust levels and efficiency.
Single Remote Sensing Image Dehazing Using Robust Light-Dark Prior

Haze, generated by floaters (semitransparent clouds, fog, snow, etc.) in the atmosphere, can significantly degrade the utilization of remote sensing images (RSIs). However, the existing techniques for single image dehazing rarely consider that the haze is superimposed by floaters and shadow, and they often aggravate the degree of the haze shadow and dark region. In this paper, a single RSI dehazing method based on robust light-dark prior (RLDP) is proposed, which utilizes the proposed hybrid model and is robust to outlier pixels. In the proposed RLDP method, the haze is first removed by a robust dark channel prior (RDCP). Then, the shadow is removed with a robust light channel prior (RLCP). Further, a cube root mean enhancement (CRME)-based stable state search criterion is proposed for solving the difficult problem of patch size setting. The experiment results on benchmark and Landsat 8 RSIs demonstrate that the RLDP method could effectively remove haze.
Detection of Colon Cancer Using Image Processing

AbstractImage processing is one of the most widely used techniques in the medical field for the early diagnosis of diseases. The most common application of image processing in medicine is the detection of cancer. Colon cancer is one of the most typical cancers worldwide. Early disease detection using image processing, which is more accurate. Early cancer detection can save lives because it enables more efficient medical care for those who are affected. The suggested approach involves gathering a set of histopathological images of the colon and using extraction, filtering, and classification to distinguish between normal and abnormal images. A grayscale version of the photo is created. Filters are used to reduce image noise. Procedure for feature extraction Using K-Mean, FCM, and FFT, the values are recorded. For storage of values, it is normalized. For each calculation, the mean, variance, standard deviation, kurtosis, and deviation are determined. Through the use of a classifier, nonlinear data is transformed into linear data.Keywords: FCMFFTGrayscale versionImage ProcessingK-Mean
The Issues with Journal Issues: Let Journals Be Digital Libraries

Science depends on a communication system, and today, that is largely provided by digital technologies such as the internet and web. Despite the fact that digital technologies provide the infrastructure for this communication system, peer-reviewed journals continue to mimic workflows and processes from the print era. This paper focuses on one artifact from the print era, the journal issue, and describes how this artifact has been detrimental to the communication of science, and therefore, to science itself. To replace the journal issue, this paper argues that scholarly publishing and journals could more fully embrace digital technologies by creating digital libraries to present and organize scholarly output.
Secure public administration using wireless blockchain technology with efficient routing policy

Summary Current research works are majorly focused on the security importance and prevention measures in wireless sensor network (WSN). The survey of existing papers states that there are no efficient protocols for achieving secure and reliable peer authentication that ensures trust and confidentiality of the end‐users, as public administration worldwide invokes many maximized resources demanding transparency with security. Nowadays, a number of nations have started implementing smart city ideas; new technology should be used to safeguard various public administration services against corruption, intruders rendering an active part, and public transparency. By integrating blockchain (BC) technology with wireless‐based public administration process (WBPAP), the challenge of gaining trust, and eliminating corruption can be attained. Auto‐metric graph neural network (AGNN) approach is proposed for eliminating data tampering and malicious attack in the peer network. Trust, confidentiality, and integrity can be achieved using the proposed BC‐AGNN‐WBPAP‐WSN approach. The simulation of this model is activated in MATLAB, and its performance is examined using some performance metrics. Finally, the attained outcomes prove that the proposed method is the first wireless‐based public blockchain experimental method for public administration analyzed with theoretical methods.
Automatic synthesis of light-processing functions for programmable photonics: theory and realization

Linear light-processing functions (e.g., routing, splitting, filtering) are key functions requiring configuration to implement on a programmable photonic integrated circuit (PPIC). In recirculating waveguide meshes (which include loop-backs), this is usually done manually. Some previous results describe explorations to perform this task automatically, but their efficiency or applicability is still limited. In this paper, we propose an efficient method that can automatically realize configurations for many light-processing functions on a square-mesh PPIC. At its heart is an automatic differentiation subroutine built upon analytical expressions of scattering matrices that enables gradient descent optimization for functional circuit synthesis. Similar to the state-of-the-art synthesis techniques, our method can realize configurations for a wide range of light-processing functions, and multiple functions on the same PPIC simultaneously. However, we do not need to separate the functions spatially into different subdomains of the mesh, and the resulting optimum can have multiple functions using the same part of the mesh. Furthermore, compared to nongradient- or numerical differentiation-based methods, our proposed approach achieves 3× time reduction in computational cost.
Occupational Hazards: Individual and Professional Factors of Why Journalists Become Victims of Online Hate Speech

ABSTRACTJournalists are regularly exposed to online hate speech their profession. Because discrimination often harms targets and can prompt self-censorship in journalistic content, undermining journalism’s public duty, it is essential to understand factors explaining why journalists become victims of online hate speech. Using Routine Activity Theory, an online survey of journalists in Germany (N = 497) revealed that conceptions of their roles as interpreters or adversaries were associated with more frequently being targets of online hate speech. Moreover, women journalists and journalists with migration background were additionally targeted by respectively sexist and racist online hate speech. Participation in active content moderation, a presumed destructive motivation, and audiences’ weak trust in media also raised journalists’ likelihood of being targets of hate speech online. Newsroom support, however, was positively related to such victimization, possibly as a result of sharing past experiences.KEYWORDS: Online victimizationroutine activity theoryonline hate speechjournalistsprofessional victimizationuncivil online communication AcknowledgementsThe author would like to thank Michaela Hofbauer for her valuable input in conceptualizing and implementing the study. Further thanks go to the group of students, who have dealt with the topic of online hate speech against journalists in the course of a graduate seminar, for their valued support in conducting the study.Disclosure StatementNo potential conflict of interest was reported by the author(s).Notes1 The study is part of a larger project on online hate speech against journalists. Further findings can be found in Obermaier, Hofbauer, and Reinemann (Citation2018).2 To assure a common understanding, online hate speech was defined at beginning of the survey as “any verbal expression of hatred against journalists, editors, and the news media in general, who in the context of their journalistic work are slandered, degraded, intimidated and/or threatened.”
The Current Crisis of Higher Education and the <i>Necessary University</i> Project (NUP): A Case Study in Evolutionary Systems Design

Higher Education is at an epochal turning point. And so are universities, which have been, for centuries, the core of higher education. In this article, we will argue that the university we need most for “surfing” this epochal crisis is one able to develop organic structures to cope with the dynamic and evolving systemic challenges that the crisis poses to higher education and, at large, humanity. Accordingly, in the following, we will present a case study in evolutionary systems design at the University of Ibagué. We will describe how, beginning with a project seed of a Necessary University, this project has evolved over the last ten years into an organic structure better fit to cope with the dynamics of the crisis. Moreover, this evolutionary approach to systems design allows the university board to have a more systemic comprehension of the crisis and better management of some of the current epochal challenges to Higher Education.
The Study of AI-Mediated Communication and Socio-Cultural Language-Related Variables: Gmail Reply Suggestions

The current research describes the use of e-mail reply suggestions from a socio-cultural perspective of some language-related variables. It also correlates how users perceive the use of e-mail reply suggestions to some variables: job, specialization, age, and gender. In this descriptive-inferential survey design, a self-developed questionnaire was distributed online to collect the data from a convenient sample of e-mail users. The analysis shows the study sample perceived medially the use of e-mail reply suggestions. There are statistically significant differences in the respondents' means about using e-mail reply suggestions according to the variable of job for education, specialization for humanities, and gender for females. Also, ages of less than 30 and above 50 years are of significance in using e-mail reply suggestions. The study reveals a weak inverse correlation, however, statistical between using e-mail reply suggestions and the language of those suggestions. In addition, a statistically significant positive direct correlation is found between usability and culture and society of e-mail reply suggestions. In light of the findings, the study recommends the better implementation of artificial intelligence-mediated communication in e-mail reply suggestions in association with some sociolinguistic factors. Also, future related studies are suggested.
Quilë: C++ genetic algorithms scientific library

This work discusses a general-purpose genetic algorithms (Holland, 1975) scientific header-only library named Quilë.The software is written in C++20 and has been released under the terms of the MIT