Reviewerly/exHarmony huggingface dataset:
Efficient Human-Quality Kannada TTS using Transfer Learning on NVIDIA's Tacotron2

Very good quality, speech synthesis systems exist for languages like English and Chinese. However, only in the recent past, increased attention has been paid for developing TTS for Indian languages. There have been several reasons for the same in the past: 1) lack of adequate market, 2) non-availability of quality training data. In this work, we have developed a human-like quality Kannada text-to-speech conversion system using about 44.8 hours of training data recorded from a studio from a Kannada teacher with good diction. We have used the transfer learning technique to continue training over the Tacotron2 and WaveGlow checkpoints pre-trained on English. Evaluation by thirty five Kannada natives resulted in an overall MOS of 4.51±0.52, whereas the original speech of the speaker was given an MOS of 4.62 ± 0.53. In another independent testing, where another set of 25 human evaluators were given ten pairs of the original utterances of the speaker and the synthesized speech of the same sentences, some of the synthesized speech samples were judged to be better than the original! In a final round of evaluation, five sentences were synthesized by our TTS, Google's Wavenet TTS and also Nuance's TTS. Kannada natives were presented these outputs in a random order and asked to choose one of them as their most preferred output. Based on 55 human evaluators, RaGaVeRa's Kannada TTS obtained a mean preference score of 78.2%, whereas Google's and Nuance's TTS got scores of 13.1% and 5.1%, respectively. Thus, to the best of the knowledge of the authors, this is the best quality TTS that has ever been achieved for Kannada so far.
Multi-label text classification with an ensemble feature space

Multi-label text classification aims at assigning more than one class to a given text document, which makes the task more ambiguous and challenging at the same time. The ambiguities come from the fact that often several labels in the prescribed label set are semantically close to each other, making clear demarcation between them difficult. As a consequence, any Machine Learning based approach for developing multi-label classification scheme needs to define its feature space by choosing features beyond linguistic or semi-linguistic features, so that the semantic closeness between the labels is also taken into account. The present work describes a scheme of feature extraction where the training document set and the prescribed label set are intertwined in a novel way to capture the ambiguity in a meaningful way. In particular, experiments were conducted using Topic Modeling and Fuzzy C-Means clustering which aim at measuring the underlying uncertainty using probability and membership based measures, respectively. Several Nonparametric hypothesis tests establish the effectiveness of the features obtained through Fuzzy C-Means clustering in multi-label classification. A new algorithm has been proposed for training the system for multi-label classification using the above set of features.
Review on the Application of Eye-tracking Technology in Usability Evaluation of E-government Apps

With the development of “E-government service”, the construction of government app has become necessary to deliver government service to citizens, and at the same time, it is of positive significance to find the shortcomings, so as to improve the service quality. Eye tracking technology has been increasingly used in the field of usability research; therefore, this study aims to analyze the state-of-art of the eye-tracking application in the field of usability evaluation of E-government App. For the purpose, content analysis was applied, and it is expected to explore the applicability of physiological experiment in government App evaluation and provide references for related experiment organization.
Privacy Preserving Text Data Encoding and Topic Modelling

Textual data, such as clinical notes, product or movie reviews in online stores, transcripts, chat records, and business documents, are widely collected nowadays and can be used to support a large spectrum of Big Data applications. At the same time, textual data, collected about individuals or from individuals, can be susceptible to inference attacks that may leak private and/or sensitive information about individuals.The increasing concerns of privacy risks in textual data preclude sharing or exchanging textual data across different parties/organizations for various applications such as record linkage, similar entity matching, natural language processing (NLP), or machine learning on large collections of textual data. This has led to the development of privacy preserving techniques for applying matching, machine learning or NLP techniques on textual data that contain personal and sensitive information about individuals. While cryptographic techniques are highly secure and accurate, they incur significant amount of computational cost for encoding and matching data – especially textual data – due to the complex nature of text.In this paper, we propose an efficient textual data encoding and matching algorithm using probabilistic techniques based on counting Bloom filters combined with Differential privacy. We apply our algorithm to a popular use case scenario that involves privacy preserving topic modeling – a widely used NLP technique – in order to identify common or collective topics in texts across multiple parties without learning the individual topics of each party, and show its effectiveness in supporting this application. Finally, through extensive experimental evaluation on three large text datasets against a state-of-the-art probabilistic encoding algorithm for privacy preserving LDA topic modelling, we show that our method provides a better privacy-utility trade-off at the cost of more computation complexity and memory space, while still being computationally efficient (log-linear complexity in the size of documents) for Big data compared to cryptographic techniques that have quadratic complexity.
Hiding Messages in Audio Files Using Direct Spread Spectrum

Direct spread spectrum technology is traditionally used in radio communication systems with multiple access. It ensures high security and reliability of information transfer. In addition, direct sequence spreading of spectrum makes the transmitted signals noise alike. We are researching this technology for other uses. The present paper explores the technique of hiding information in multimedia files, in particular, audio containers. The paper investigates various methods of forming wideband signals (chip codes), and studies the possibility of their usage for hiding information by using direct spread spectrum technology. We conduct experimental studies, in particular, we estimate the bit error rate (BER) of information data, as well as the distortion of the audio container by mean squared error (MSE) and peak signal-to-noise ratio (PSNR). We show which method of forming chip codes is preferable for reliable and safe information hiding.
No-Reference Metric Optimization-Based Perceptually Invisible Image Enhancement

This article presents a novel algorithm to enhance the contrast and extract hidden information from perceptually invisible images and overenhanced images with dark regions. The proposed novel algorithm extracts the most significant patch of an image, trains high-boost-emphasis filters on this patch, and based on the optimized trained coefficients enhances the whole image. First, we propose a novel algorithm based on the fusion of simple linear iterative clustering (SLIC) and the Canny edge detection to extract the most informative segment of an image, termed the patch. Second, a contrast enhancement-specific optimization algorithm is designed. The algorithm optimizes no-reference image quality assessment (IQA) performance metrics on the extracted patch, to obtain the most adept filter and its coefficients for the best possible enhancement. The optimized filter coefficients then enhance the illumination and reflectance component of the input image. Extensive evaluation of quantitative performance metrics on three publicly available datasets demonstrates the sublime performance of the proposed algorithm. The enhanced image provides superior perception quality and the best mean value of quantitative parameters compared with state-of-the-art methods.
Realizing Forward Defense in the Cyber Domain

With the recognition of cyberspace as an operating domain, concerted effort is now being placed on addressing it in the whole-of-domain manner found in land, sea, undersea, air, and space domains. Among the first steps in this effort is applying the standard supporting concepts of security, defense, and deterrence to the cyber domain. This paper presents an architecture that helps realize forward defense in cyberspace, wherein adversarial actions are repulsed as close to the origin as possible. However, substantial work remains in making the architecture an operational reality including furthering fundamental research cyber science, conducting design trade-off analysis, and developing appropriate public policy frameworks.
SLR: A Systematic Literature Review on Blockchain Applications in Healthcare

In a very less time from its advent, blockchain technology has entered into mainstream research all over the world. The Bitcoin cryptocurrency is the first open-source implementation of blockchain technology. The application of blockchain technology is not restricted to financial services only; several other sectors especially healthcare is increasingly focusing their attention to grab potential and unique features of blockchain technology like immutability, security, and trust. This paper aims to review the exploration of blockchain technology in the healthcare domain in recent years and study how these emerging technologies help healthcare to speed up treatment procedures and mitigate the effects of the COVID-19 pandemic.
A novel grasshopper optimization algorithm based on swarm state difference and its application

The grasshopper optimization algorithm (GOA) has received extensive attention from scholars in various real applications in recent years because it has a high local optima avoidance mechanism compared to other meta-heuristic algorithms. However, the small step moves of grasshopper lead to slow convergence. When solving larger-scale optimization problems, this shortcoming needs to be solved. In this paper, an enhanced grasshopper optimization algorithm based on solitarious and gregarious states difference is proposed. The algorithm consists of three stages: the first stage simulates the behavior of solitarious population learning from gregarious population; the second stage merges the learned population into the gregarious population and updates each grasshopper; and the third stage introduces a local operator to the best position of the current generation. Experiments on the benchmark function show that the proposed algorithm is better than the four representative GOAs and other metaheuristic algorithms in more cases. Experiments on the ontology matching problem show that the proposed algorithm outperforms all metaheuristic-based method and beats more the state-of-the-art systems.
Classification of the Insureds Using Integrated Machine Learning Algorithms: A Comparative Study

With the growing number of insurance purchasers, the sophisticated claim analysis system has become an imperative must for any insurance firm. Claims Analysis can be utilized to better understand the customer strata and incorporate the findings throughout the insurance policy enrollment, including the underwriting and approval or rejection stages. In recent years machine learning (ML) technologies are increasingly being used to claims Analysis. However, choosing the optimal techniques, whether the features selection techniques, feature discretization techniques, resampling mechanisms, and ML classifiers for insurance decision assistance, is difficult and can harm the quality of claim suggestions. This study aims to develop appropriate decision models by combining binary classification, feature selection, feature discretization, and data resampling techniques. We did Extensive tests on three different datasets to evaluate the viability of the selected models. We used multiple assessment metrics besides the statistical significance test from The ANOVA test and the Friedman test to evaluate the ML models. The findings show that the models perform highly better after applying the feature discretization technique, reducing dimensionality using feature selection methods and solving the unbalanced data problem with resampling methods.
Structural Knowledge Organization and Transfer for Class-Incremental Learning

Deep models are vulnerable to catastrophic forgetting when fine-tuned on new data. Popular distillation-based methods usually neglect the relations between data samples and may eventually forget essential structural knowledge. To solve these shortcomings, we propose a structural graph knowledge distillation based incremental learning framework to preserve both the positions of samples and their relations. Firstly, a memory knowledge graph (MKG) is generated to fully characterize the structural knowledge of historical tasks. Secondly, we develop a graph interpolation mechanism to enrich the domain of knowledge and alleviate the inter-class sample imbalance issue. Thirdly, we introduce structural graph knowledge distillation to transfer the knowledge of historical tasks. Comprehensive experiments on three datasets validate the proposed method.
Machine translation of English speech: Comparison of multiple algorithms

Abstract In order to improve the efficiency of the English translation, machine translation is gradually and widely used. This study briefly introduces the neural network algorithm for speech recognition. Long short-term memory (LSTM), instead of traditional recurrent neural network (RNN), was used as the encoding algorithm for the encoder, and RNN as the decoding algorithm for the decoder. Then, simulation experiments were carried out on the machine translation algorithm, and it was compared with two other machine translation algorithms. The results showed that the back-propagation (BP) neural network had a lower word error rate and spent less recognition time than artificial recognition in recognizing the speech; the LSTM–RNN algorithm had a lower word error rate than BP–RNN and RNN–RNN algorithms in recognizing the test samples. In the actual speech translation test, as the length of speech increased, the LSTM–RNN algorithm had the least changes in the translation score and word error rate, and it had the highest translation score and the lowest word error rate under the same speech length.
Comparative Semantics of Fuzzy Argumentation under the &amp;amp;#321;ukasiewicz t-Norm

Dung's theory of argumentation frameworks (AF) has been applied in many fields of artificial intelligence.The arguments and attack relation are generally partly believed due to the uncertainty in the process of mining them.Fuzzy AFs catch uncertainty in AFs by associating fuzzy degrees with the arguments or the attacks.Among the various semantics of fuzzy AFs, the comparative semantics develops and defines Dung's extensions in the form of fuzzy sets.However, the comparative semantic system only puts forward some basic concepts, and has not been deeply studied in terms of algorithms and properties.This paper studies the comparative semantics of fuzzy AFs based on the Łukasiewicz t-norm in a more in-depth and comprehensive manner.This work is not only a supplement and improvement to comparative semantic in theory, but also beneficial to the calculation and fast identification of its various extensions (based on the Łukasiewicz t-norm).
An Interactive Scholarly Collaborative Network Based on Academic Relationships and Research Collaborations

In this era of digital transformation, when the amount of scholarly literature is rapidly growing, hundreds of papers are published online daily with regard to different fields, especially in relation to academic subjects. Therefore, it difficult to find an expert/author to collaborate with from a specific research area. This is thought to be one of the most challenging activities in academia, and few people have considered authors’ multi-factors as an enhanced method to find potential collaborators or to identify the expert among them; consequently, this research aims to propose a novel model to improve the process of recommending authors. This is based on the authors’ similarity measurements by extracting their explicit and implicit topics of interest from their academic literature. The proposed model mainly consists of three factors: author-selected keywords, the extraction of a topic’s distribution from their publications, and their publication-based statistics. Furthermore, an enhanced approach for identifying expert authors by extracting evidence of expertise has been proposed based on the topic-modeling principle. Subsequently, an interactive network has been constructed that represents the predicted authors’ collaborative relationship, including the top-k potential collaborators for each individual. Three experiments have been conducted on the collected data; they demonstrated that the most influential factor for accurately recommending a collaborator was the topic’s distribution, which had an accuracy rate of 88.4%. Future work could involve building a heterogeneous co-collaboration network that includes both the authors with their affiliations and computing their similarities. In addition, the recommendations would be improved if potential and real collaborations were combined in a single network.
A high-accuracy, low-budget Sensor Glove for Trajectory Model Learning

Sensor gloves are gaining importance in tracking hand and finger movements in virtual reality applications as well as in scientific research. They introduce an unrestricted way of capturing motion without the dependence on direct line of sight as for visual tracking systems. With such sensor gloves, data of complex motion tasks can be recorded and used for modeling probabilistic trajectories or teleoperation of robotic arms. While a multitude of sensor glove designs relying on different functional principles exist, these approaches require either sensitive calibration and sensor fusion methods or complex manufacturing processes. In this paper, we propose a low-budget, yet accurate sensor glove system that uses flex sensors for fast and efficient motion tracking. We evaluate the performance of our sensor glove, such as accuracy and latency, and demonstrate the functionality by recording motion data for learning probabilistic movement models.
Sentimental Analysis On Voice Based Reviews Using Fuzzy Logic

E-commerce websites becoming the future of the market, manufacturers have to keep their products updated to compete among different brands. Improvements according to their customer’s needs help them to stay active in the competition. To improve their product, reviews are an important medium in achieving it. Customers also prefer to buy products online depending on the ratings and reviews of a product. Reviews that are written may not deliver all the emotions a customer needs to convey but in the case of a speech review, it almost delivers the information that the customer needs to convey. In this process, the customer submits the review as an audio file which is then converted into a text file using Natural language Processing. The user-driven reviews of a product are processed using sentimental analysis. The proposed system involves fuzzy logic for classifying the reviews into positive, negative, and neutral reviews and for assigning weightage to the words.
Aspects of writing knowledge and EFL students' writing quality

Writing knowledge pertaining to process, system, content, and genre plays an essential role to produce an intelligible composition. The purpose of the present study is twofold – to investigate the correlation between aspects of writing knowledge and quality of writing, and to investigate the contribution of the overall and individual aspects of writing knowledge to writing quality. The participants were 54 second-year ELT undergraduate students of a university in Indonesia. A writing test and multiple-choice test on writing knowledge were used as the instruments of data collection. The analysis employed Pearson correlation coefficient and multiple regressions. The result revealed a significant positive correlation between writing knowledge and writing quality. As a unit, the knowledge of process, system, content, and genre, indicates a significant contribution to the writing quality. Individually, however, only process knowledge significantly contributes to the writing quality. It provides additional pieces of evidence that process knowledge be given special attention, thus writing instruction should allow students to learn writing knowledge explicitly and implicitly.
ANGELA: HTTP Adaptive Streaming and Edge Computing Simulator

In this work, we present ANGELA, HTTP Adaptive Streaming (HAS) and Edge Computing Simulator. ANGELA was designed to test edge mechanisms that support HAS, as it offers: realistic radio layer simulation, different multimedia content configurations, access to radio and player metrics at the edge, and a wide variety of metrics to evaluate the video streaming session performance. The ANGELA architecture is flexible and can support adaptive bitrate (ABR) algorithms located at different points of the network. Moreover, we show the possibilities of ANGELA by evaluating different ABR algorithms.
Automatic text summary generation method based on hybrid model DNM

With the rapid increase of text data generated by the Internet, the problem of text information overload is becoming more and more serious. Automatic text summarization provides a good method to simplify text information. Traditional methods are mainly divided into extractive and abstractive methods. However most extractive methods do not have too much context connection, which leads to unsmooth abstracts. Abstractive method is the mainstream method, but it also deviates from the text content and has the problem of poor readability. In this paper, a hybrid automatic text summarization method is proposed based on deep learning and a rapid self-attention mechanism. This mechanism is used to obtain a hybrid model DNM (Dilated Neural Random Attention with Minimal Risk Loss) by optimizing the network structure and combining it with a specific loss function. The ROUGE score of our model is compared with the extractive Neural Document Summarization (NEUSUM), the abstractive Graph-Based Attentional (GBA) and the hybrid model CopyNet on the LCSTS dataset so as to achieve more accurate and reasonable automatic text summarization.
Cryptojacking

The cybersecurity community never ceases to amaze or disappoint; it continually adds words to the English dictionary. Here’s another: “cryptojacking.” It’s not carjacking or hijacking, but similar. It is a word that you might wish to know more about.
Cooperative Light-Field Image Super-Resolution Based on Multi-Modality Embedding and Fusion With Frequency Attention

Light field (LF) imaging is an advanced visual perception system, which can record the intensity and direction information of light rays and provide multi-viewpoint images from a single capture. However, there is a trade-off between spatial and angular resolutions due to the restricted sensor size, which limits the wide applications of LF cameras. To address this problem, we propose a cooperative network to super-resolve LF sub-aperture images based on the multi-modality fusion. Specifically, in order to fully explore the LF information, we adopt various modalities and extract corresponding features to emphasise diverse LF characteristics. Then, we design a multi-scale fusion module to effectively integrate global and local LF features and apply frequency-aware attention mechanism to adaptively reinforce fused features. Extensive experiments demonstrate the superiority of our method on both qualitative and quantitative evaluations, with competitive execution efficiency.