Reviewerly/exHarmony huggingface dataset:
Federated Learning for Privacy Preserving On-Device Speaker Recognition

State-of-the-art speaker recognition systems are usually trained on a single computer using speech data collected from multiple users.However, these speech samples may contain private information which users are not willing to share.To overcome such potential breaches of privacy, we investigate the use of federated learning in speaker recognition.Distributed learning methods such as federated learning enable us to train a shared model without sharing the private data by training the models on edge devices where the data resides.In the proposed system, each edge device trains an individual model which is subsequently sent to a secure aggregator.To provide contrasting data without the need for transmitting data, we use a generative adversarial network (GAN) to generate impostor data at the edge.Afterwards, the secure aggregator merges the individual models, builds a global model and transmits the global model to the edge devices through a main server.Experimental results on the Voxceleb-1 dataset show that the use of federated learning for speaker recognition system provides two advantages.Firstly, it retains privacy since the raw data does not leave the edge devices.Secondly, experimental results show that the aggregated model provides better average equal error rate than the individual models.
Edge Enhancement Based Active Contour Model for Segmentation of Brain Tumor in MRI Images

Tumor segmentation is one of the most significant tasks in brain image analysis due to the significant information obtained by the tumor region. Therefore, many methods have been proposed during the last two decades for segmenting the tumor in MRI images. In this paper, an automated method is proposed using an active contour model with an initial contour creation using edge sharpening, thresholding, and morphological operations. Four methods of edge detection are utilized in the edge sharpening process (Sobel, Roberts, Prewitt, and Canny) and their performance was investigated in terms of Dice, Jaccard, and F1 score. The experiments were implemented on BRATS datasets with both HGG and LGG images. The study indicates that sharpening the edges using edge detection is essential to improve the segmentation of the tumor region especially when it is used with an active contour model. The achieved results show the effectiveness of the proposed method and it outperformed some recent existing methods.
Blockchain Digital Test Certificates for COVID-19

COVID-19 is a pandemic outbreak for each country worldwide. Each government needs to monitor every citizen and the COVID-19 test becomes an essential evidence for people who are travelling. This gives rise to the necessity of disruptive technologies such as Blockchain. In this paper, we provide an overview of the Hyperledger and Ethereum platforms and present how healthcare organizations can control and monitor digital health test certificates with citizens or other stakeholders. We also present a smart contract structure and implementation for COVID-19 test certificates in both blockchain platforms.
Discourse and unaccusativity

This paper brings together syntactic analysis with corpus results to argue that a subset of unaccusative verb phrases, those with verbs that typically denote motion or manner of motion (e.g. arrive, walk in) share a discourse property with there BE sentences - that of establishing a new discourse referent. The theoretical motivation for the corpus experiment is an analysis in which verbs like arrive and walk in occur in an “existential unaccusative” structure, a structure in which part of the denotation of the VP is McNally’s (1992, 1997) function INSTANTIATE, which triggers the establishment of a discourse referent in existential sentences (see also McCloskey 2014). Results from a corpus experiment are presented in which the ratio of indefinite/definite subjects of existential unaccusative sentences is compared to that of unergative sentences. The comparison of these ratios was found to be statistically significant, where the comparatively greater number of indefinite subjects of existential unaccusative sentences was not the result of chance. The utility and limits of corpus experiments for syntactic theory is discussed. It is argued that corpus data can inform syntactic theory, though the limits of corpus data for supporting or refuting specific syntactic models is acknowledged
A Narrative Review of Virtual Reality Applications for the Treatment of Post-Traumatic Stress Disorder

Virtual reality (VR) technologies allow for the creation of 3D environments that can be exploited at the human level, maximizing humans’ use of perceptual skills through their sensory channels, and enabling them to actively influence the course of events that take place in the virtual environment (VE). As such, they constitute a significant asset in the treatment of post-traumatic stress disorder (PTSD) via exposure therapy. In this article, we review the VR tools that have been developed to date for the treatment of PTSD. The article aims to analyze how VR technologies can be exploited from a sensorimotor and interactive perspective. The findings from this analysis suggest a significant emphasis on sensory stimulation to the detriment of interaction. Finally, we propose new ideas regarding the more successful integration of sensorimotor activities and interaction into VR exposure therapy for PTSD.
Chaotic Butterfly Optimization with Optimal Multi-key Image Encryption Technique for Wireless Sensor Networks

Wireless sensor network (WSN) comprises a set of sensor nodes, mainly used for data collection and tracking process. The imaging sensors in WSN captures the images from the target environment, which needs to be securely transmitted to the base station (BS). Since data transmission in WSN takes place through wireless links, security is a major challenging issue involved in the design of WSN. Image encryption is a commonly available solution to securely transmit the images to destination without comprising security. Therefore, this study designs a novel Chaotic Butterfly Optimization with Optimal Multi-key Image Encryption (CBO-OMKIE) technique for WSN. The goal of the CBO-OMKIE technique is to securely encrypt the images in WSN. The proposed CBO-OMKIE technique involves the design of multi-key based image encryption technique to accomplish security in WSN. In addition, the CBO algorithm is applied to determine the optimal keys involved in the encryption process and it helps for improving the security level to a maximum extent. The performance validation of the CBO-OMKIE technique takes place using benchmark test images and the outcomes were examined under several aspects. The simulation outcome pointed out the enhanced security analysis of the CBO-OMKIE technique over the other techniques.
Air Quality Index Analysis Using Single-Valued Neutrosophic Plithogenic Graph for Multi-Decision Process

Recently, Plithogenic set and its graphical structure visualization is stuide for dealing with multi-attribute data set. In this pocess a problem arises while characterization of uncertainty in tue, false and uncertain regions, independently. One of the suitable examples is Air Quality Index and its impact on human health contains multi-valued attributes. In this case the conflict may arise among two experts about acceptation, rejection and uncertain impact of AQI for human health. To resolve this issue a single-valued neutrosophic Plithogenic set and its graphical strcutue visualization is discussed in this paper with an illustrative example.
Selection of Key Frames for 3D Reconstruction in Real Time

Cameras play a prominent role in the context of 3D data, as they can be designed to be very cheap and small and can therefore be used in many 3D reconstruction systems. Typical cameras capture video at 20 to 60 frames per second, resulting in a high number of frames to select from for 3D reconstruction. Many frames are unsuited for reconstruction as they suffer from motion blur or show too little variation compared to other frames. The camera used within this work has built-in inertial sensors. What if one could use the built-in inertial sensors to select a set of key frames well-suited for 3D reconstruction, free from motion blur and redundancy, in real time? A random forest classifier (RF) is trained by inertial data to determine frames without motion blur and to reduce redundancy. Frames are analyzed by the fast Fourier transformation and Lucas–Kanade method to detect motion blur and moving features in frames to label those correctly to train the RF. We achieve a classifier that omits successfully redundant frames and preserves frames with the required quality but exhibits an unsatisfied performance with respect to ideal frames. A 3D reconstruction by Meshroom shows a better result with selected key frames by the classifier. By extracting frames from video, one can comfortably scan objects and scenes without taking single pictures. Our proposed method automatically extracts the best frames in real time without using complex image-processing algorithms.
Blockchain Analysis of the Bitcoin Market

In this paper, we provide detailed analyses of the Bitcoin network and its main participants. We build a novel database using a large number of public and proprietary sources to link Bitcoin addresses to real entities and develop an extensive suite of algorithms to extract information about the behavior of the main market participants. We conduct three major pieces of analysis of the Bitcoin eco-system. First, we analyze the transaction volume and network structure of the main participants on the blockchain. Second, we document the concentration and regional composition of the miners which are the backbone of the verification protocol and ensure the integrity of the blockchain ledger. Finally, we analyze the ownership concentration of the largest holders of Bitcoin.Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.
Shadow Sequences of Integers: From Fibonacci to Markov and Back

The following general idea looks crazy. What if another integer sequence follows each integer sequence like a shadow? I will demonstrate that this is indeed the case, perhaps not for every integer sequence, but for many of them.
Intelligent Image Detection System Based on Internet of Things and Cloud Computing

Images are the most intuitive way for humans to perceive and obtain information, and they are one of the most important sources of information. With the development of information technology, the use of digital image processing methods to locate and identify targets is widely used, so it is particularly important to detect the targets of interest quickly and accurately in the image. The traditional image detection system has the problems of low detection accuracy, long time consumption, and poor stability. Therefore, this paper proposes the design and research of artificial intelligence image detection system based on Internet of Things and cloud computing. The system designed in this article mainly includes three links, namely: image processing analysis design link in cloud computing environment, image feature collection module design link, and image integration detection link. The main technologies used in image processing and analysis in the cloud computing environment are virtualization technology, distributed massive data storage, and distributed computing. In the image feature collection module, before the image is input to the neural network, it is necessary to perform preprocessing operations on the distorted image and perform perspective correction; then use the deep residual network in deep learning to extract features. Finally, there is the image integration detection link. First, the target category judgment and position correction are performed on the regions generated by the candidate region generation network, and then the integrated image detection is performed through the improved target detection method based on the frame difference method. Through simulation experiments, compared with the traditional image detection system, the speed advantage of the artificial intelligence image detection system designed in this paper is obvious in the case of a large increase in the number of images. On images at different pixel levels, the accuracy of the image detection system proposed in this paper is always higher than that of traditional image detection systems, and the CPU usage and memory usage are at a lower level. In addition, within three months, the stability is also at a relatively high level of 0.9.
Customer-Oriented Aggregators of Massive Open Online Courses: Opportunities and Prospects

In recent years, the number of online courses known as massive open online courses (MOOC) has increased significantly. Such courses are available on online providers’ sites, such as edX and Coursera. As a result, there is a notable reduction of clarity in terms of various course offerings, and this causes a need to change the MOOC description schemes to help inform potential applicants. The article is aimed at identifying the capabilities of customer-oriented MOOC aggregators and the prospects for their further improvement. The article presents approaches to the concept of customer focus, based on which the definition of a customer-oriented aggregator is formulated. The article analyses course descriptions used by MOOC providers and aggregators for their potential students. Based on this analysis, the authors develop a set of categories describing MOOC, used for the creation of a new MOOC description according to the customer-oriented criteria. The authors conclude that the optimal descriptive MOOC scheme on the MOOC aggregator should satisfy both the academic environment and the customer-oriented criteria that correspond to students’ interests when choosing a MOOC.
Augmented Reality: A Boon for the Teaching and Learning Process

This chapter explains how augmented reality (AR) improves teaching and learning by using different applications of AR. It describes the history, recent developments, elements, place, and benefits of AR as well as experiences in AR classrooms, the role of AR in the teaching and learning process, the impact of AR, using AR for learners’ development, applications of AR and the need for resources in the classroom, the future of AR in education, and three-dimensional learning models. All the educational applications for enriching the teaching and learning process are vividly explained in the chapter. Simplifying learning and teaching by using AR is proved by this chapter. With the advancement of technology, AR applications, training, and learning are becoming easy, effective, and efficient. AR technology ensures the teaching and learning process is available any place, at any time, from primary education to higher education.
Upaya Guru dalam Meningkatkan Kemampuan Berbahasa Jawa Siswa Kelas V melalui Pembiasaan Berbahasa

Language ability is the ability of an individual to make words or sounds that are combined into a complete speech or sentence that can be understood by himself and others. Where individuals can understand speech / language conveyed by others and are able to show or pronounce the language of others. The importance of a teacher's efforts to improve the language skills of students or learners in learning, especially Javanese language learning, must really be done in order to create a quality generation and of course can cultivate the language that becomes their identity. The effort of a teacher in this case is to use the habituation method. This study aims to determine what efforts are made by the teacher inimproving the Javanese language skills of grade V students at MI Miftahul Ulum Pelangwot and to find out what are the supporting factors in improving the Javanese language skills of grade V students at MI Miftahul Ulum Pelangwot. This research uses qualitative methods, with data collection techniques used are: observation, interviews and documentation. The author uses data analysis techniques in the form of qualitative descriptive. Based on the analysis of the data obtained, the researcher can conclude that the efforts of Javanese language teachers in improving students 'language skills are indeed very important to do. The efforts of Javanese language subject teachers to improve students' language skills are through habituation activities, a teacher always provides an example that can be applied by students, as an effort to improve the language skills of students. In addition, the habits that are always applied by the teacher are by carrying out activities related to language skills, such as language habituation in every lesson, holding competitions that are related to the language skills of students.Pos-el :khubnimaghfirotun@stitaf.ac.id1),Muhammad_robik@gmail.com2)
fairseq S^2: A Scalable and Integrable Speech Synthesis Toolkit

This paper presents fairseq Sˆ2, a fairseq extension for speech synthesis. We implement a number of autoregressive (AR) and non-AR text-to-speech models, and their multi-speaker variants. To enable training speech synthesis models with less curated data, a number of preprocessing tools are built and their importance is shown empirically. To facilitate faster iteration of development and analysis, a suite of automatic metrics is included. Apart from the features added specifically for this extension, fairseq Sˆ2 also benefits from the scalability offered by fairseq and can be easily integrated with other state-of-the-art systems provided in this framework. The code, documentation, and pre-trained models will be made available at https://github.com/pytorch/fairseq/tree/master/examples/speech_synthesis.
Application of Empirical Mode Decomposition and Extreme Learning Machine Algorithms on Prediction of the Surface Vibration Signal

Accurately predicting surface vibration signals of diesel engines is the key to evaluating the operation quality of diesel engines. Based on an improved empirical mode decomposition and extreme learning machine algorithm, the characteristics of diesel engine surface vibration signal were detected, predicted, and analyzed. First, the surface vibration signal was decomposed into a series of signal components by an improved empirical mode decomposition algorithm. Then, the extreme learning machine algorithm was applied to each signal component to obtain the predicted value of the corresponding signal component and determine the characteristics of the ground vibration signal. Compared with the empirical mode decomposition–extremum learning machine algorithm and the extremum learning machine algorithm, the results show that the improved empirical mode decomposition–extremum learning machine algorithm is feasible and effective.
Sentiment Analysis on Government Performance in Tourism During The COVID-19 Pandemic Period With Lexicon Based

The COVID-19 pandemic impact has affected all industries in Indonesia and even the world, including the tourism industry. Researchers have a role in researching to answer the needs of the tourism industry, especially in making tourism and business destination management programs and carrying out activities oriented to meet the needs of the tourism industry. Meanwhile, the government has a role in making policies, especially in the roadmap, for developing the tourism industry. This study aims to track trending topics in social media Instagram since COVID-19 hit. The results of trending topics will be classified by sentiment analysis using a Lexicon-based and Naive Bayes Classifier. Based on Instagram data taken since January 2020, it shows the five highest topics in the tourism sector, namely health protocols, hotels, homes, streets, and beaches. Of the five topics, sentiment analysis was carried out with the Lexicon-based and Naive Bayes classifier, showing that beaches get an incredibly positive sentiment, namely 80.87%, and hotels provide the highest negative sentiment 57.89%. The accuracy of the Confusion matrix's sentiment results shows that the accuracy, precision, and recall are 82.53%, 86.99%, and 83.43%, respectively.
Generalization Equations for Machine Learners Based on Physical and Abstract Laws

The physical and abstract laws derived from the first principles have been recently exploited to customize and sharpen machine learning (ML) methods and also derive their generalization equations. These laws often encapsulate knowledge that complements datasets and ML models. We present a generic framework that uses these laws to provide ML codes that are transferable across multiple areas, including data transport infrastructures and thermal hydraulics analytics of nuclear reactors. By anchoring on datasets from these areas and the statistical generalization theory, we present a rigorous approach to co-develop ML solutions and the generalization equations that characterize them, by exploiting the structure and constraints from the laws. We present illustrative examples using practical problems from existing literature on the performance characterization of data transport infrastructures, and the sensor error and power level estimation in nuclear reactor systems using sensor measurements of primary and secondary coolant systems, respectively.
An Automatic Assessment Method for Spoken English Based on Multimodal Feature Fusion

This paper provides a comprehensive explanation of the theoretical foundations of multimodal discourse analysis theory as applied to speaking instructional design. The specific application of multimodal theory in the teaching of elementary English speaking classrooms is explored through the teaching design of elementary English speaking classrooms, the teaching implementation of multimodal teaching design is carried out, and the effect of the teaching practice of elementary English speaking guided by multimodal discourse analysis theory is comprehensively evaluated through classroom observation method, questionnaire survey method, and interview method, combined with the teaching evaluation and teaching implementation effect, which is the multimodal teaching design. The paper also summarizes the findings and shortcomings of the study. Through the teaching design and implementation, the advantages of multimodal teaching are obvious; it can combine with modern advanced teaching techniques to create more realistic communicative situations in the classroom, gather and present various modal resources and information, and ensure rich and diverse language input; students can receive various sensory stimuli in the classroom, deepen their memory and experience of language, increase the interest of classroom teaching, and improve students’ participation. It also increases the interest of the classroom and enhances students’ participation and motivation. Based on multimodal theory, the author designed a multimodal teaching framework for a semester-long speaking course in the speaking classroom for reference. The fuzzy measures were constructed based on subsets of language segments containing 10 phonemes belonging to the same HDP set. Finally, linguistic scores are given by the Surgeon integral model based on the plausibility of the system and the fuzzy measures. The experimental results based on Sphinx-4 show that the evaluation model yields plausible and stable evaluation results for the 3 test sets at an average correct recognition rate of 84.7% of phonemes.