Reviewerly/exHarmony huggingface dataset:
Latent linguistic embedding for cross-lingual text-to-speech and voice conversion

As the recently proposed voice cloning system, NAUTILUS, is capable of cloning unseen voices using untranscribed speech, we investigate the feasibility of using it to develop a unified cross-lingual TTS/VC system. Cross-lingual speech generation is the scenario in which speech utterances are generated with the voices of target speakers in a language not spoken by them originally. This type of system is not simply cloning the voice of the target speaker, but essentially creating a new voice that can be considered better than the original under a specific framing. By using a well-trained English latent linguistic embedding to create a cross-lingual TTS and VC system for several German, Finnish, and Mandarin speakers included in the Voice Conversion Challenge 2020, we show that our method not only creates cross-lingual VC with high speaker similarity but also can be seamlessly used for cross-lingual TTS without having to perform any extra steps. However, the subjective evaluations of perceived naturalness seemed to vary between target speakers, which is one aspect for future improvement.
Voice Conversion by Cascading Automatic Speech Recognition and Text-to-Speech Synthesis with Prosody Transfer

With the development of automatic speech recognition (ASR) and text-to-speech synthesis (TTS) technique, it's intuitive to construct a voice conversion system by cascading an ASR and TTS system. In this paper, we present a ASR-TTS method for voice conversion, which used iFLYTEK ASR engine to transcribe the source speech into text and a Transformer TTS model with WaveNet vocoder to synthesize the converted speech from the decoded text. For the TTS model, we proposed to use a prosody code to describe the prosody information other than text and speaker information contained in speech. A prosody encoder is used to extract the prosody code. During conversion, the source prosody is transferred to converted speech by conditioning the Transformer TTS model with its code. Experiments were conducted to demonstrate the effectiveness of our proposed method. Our system also obtained the best naturalness and similarity in the mono-lingual task of Voice Conversion Challenge 2020.
Study on Mathematical Concept Teaching in High School Based on 5E Instructional Model

Mathematics knowledge system centers around mathematical concepts. The ultimate goal of mathematical concept teaching is to stimulate students’ innovative thinking and mathematical capabilities through their exploration and deep comprehension of the essence of concepts. 5E instructional model is a constructivism-based explorative teaching model, emphasizing the exploration of independently constructing knowledge by students, which is in line with the requirements of the curriculum standards on concept teaching. Taken the concept teaching of trigonometric functions as an example, this paper designs the 5E instructional model of mathematical concept teaching in high school. Moreover, the study provides educational researchers evidence and reference that can be developed to improve teaching strategies in the process of implementing 5E instructional model.
Healthy and sustainable development of sports economy based on artificial intelligence and mental model

In recent years, sports have achieved rapid development worldwide, and the global economy has been significantly improved and improved. With the in-depth development of the two, the connection between sports and the economy has also become closer. Sports economy is a new type of economic form bred by specialization of sports organization, participation in consumerization, and profit-oriented operation under the condition of market economy. And the development of sports economy cannot be developed at once; it needs healthy and sustainable development. In order to find a better way to study the healthy and sustainable development of sports economy, this paper uses deep learning network algorithm and supports vector machine learning algorithm to build a mental model. It then uses the model to analyze various indicators of the sports industry in a province in China. This article is looking for information and summarizes the province’s sports data from 2017 to 2021. The sports indicators of this experiment include regional GDP, total output of sports industry, sports practitioners, local financial sports expenditures, the number of policies, the number of people participating in physical exercise, and fitness venues and facilities. The realization results show that these variables develop at a relatively small rate under normal conditions, and then predict the data in the next few years under the healthy and sustainable development of the next few years through the mental model. The growth rates of various indicators of the sports economy have increased significantly, and they have been optimized by about 20% compared with the normal development.
Anatomical mining method of cervical nerve root syndrome under visual sensing technology

INTRODUCTION: The gray resolution of anatomical image of cervical nerve root syndrome is low, that can not be mined accurately. OBJECTIVES: Aiming at the defect of low gray resolution of anatomical images, an image mining method using visual perception technology was studied. METHODS: According to the visual perception technology, the internal parameter matrix and external parameter matrix of binocular visual camera were determined by coordinate transformation, and the anatomical images of cervical nerve root syndrome were collected. The collected images are smoothed and enhanced by nonlinear smoothing algorithm and multi-scale nonlinear contrast enhancement method. The directional binary simple descriptor method is selected to extract the features of the enhanced image; Using K-means clustering algorithm, the anatomical image mining of cervical nerve root syndrome is completed by obtaining the initial clustering center and image mining. RESULTS: Experimental results show that the information entropy of the images mined by the proposed method is higher than 5, the average gradient is greater than 7, the edge information retention is greater than 0.7, the peak signal-to-noise ratio is higher than 30 dB, and the similarity of the same category of images is greater than 0.9. CONCLUSIONS: This method can effectively mine the anatomical images of cervical nerve root syndrome and provide an important basis for the diagnosis and treatment of cervical nerve root syndrome.
Design and implementation of transfer learned deep CNN with feature fusion for automated mammogram classification

Radiologists frequently struggle to define mammography mass lesions, resulting in unneeded breast biopsies to eliminate suspicions, which adds exorbitant costs to an already overburdened patient and medical system..Existing models have limited capability for feature extraction and representation, as well as cancer classification. Therefore, we built deep Convolution neural networks based Computer-aided Diagnosis system to assist radiologists in classifying mammography mass lesions. Here, Two-view LASSO regression feature fusion and fine-tuned transfer learning network model VGG16 were applied for identification of mammogram cancer. First, two independent CNN branches are utilized to extract mammography characteristics from two different perspectives. Feature Extraction is performed by fine-tuning pre-trained deep network models VGG16 which extracts deep convolutional features. Second, the features of the VGG16 models are serially fused using LASSO regression. Lastly, the fused features are entered into the Fully Connected Layer for mammogram classification. The high accuracy of 95.24, senstitivity of 96.11% and AUC score of 97.95% of the proposed approach revealed that it should be used to enhance clinical decision-making.
Circulate Matrix and Compression Sensing Based Multi-Level Image Encryption

Digital data security is a broad research area in the field of science and technology. A lot of research was focused on information security-based mechanism for secure communication. This paper presents a novel image encryption as well as compression based on measurement matrix, pixel exchange and logistic cat map, which includes the permutation, compression, and diffusion processes. Initially the image is divided into four equal sizes of blocks and then each block is transformed into horizontal and vertical low and high frequency band. Then a random matrix multiplication function is applied to achieve an encrypted and scrambling frequency component and apply inverse DWT procedure to get first level of scrambled blocks, and further we apply the second level of security mechanism. Here each adjacent block pixel is exchanged by using the random matrices. For providing the high level of compression we design measurement matrices in compressive sensing by utilizing the circulate matrices and controlling the original column vectors of the circulate matrices with Arnold cat map. With the help of measurement matrix again the blocks are encrypted. Experimental results and performance analyses validate the good compression performance and high security of the given algorithm.
Face-to-Face and Online English Lounges: A Comparative Analysis

The research was conducted to examine how an English lounge program at a university affected students’ speaking ability and their attitude toward their foreign teachers and to determine if there were significant differences in the effects between the program delivered face-to-face and online as perceived by the two groups of respondents – students and teachers. This study is a descriptive-comparative survey research. Frequency analysis, mean comparison analysis, t-test, correlation analysis, and regression analysis were the statistical tools used. The computed mean values show that as perceived by the participants, the program, implemented face-to-face and online; improved the students’ speaking ability and their attitude toward their foreign teachers to a great extent. Results have shown that as perceived by the participants, both versions of the program improve the students’ speaking ability and their attitude toward their foreign teachers and that students’ attitude toward foreign teachers is strongly correlated to (and significantly influences) the development of their speaking ability. The findings convey the importance of establishing rapport between students and teachers in the development of the speaking ability of students. It also reveals that online English lounges are as effective as when they’re implemented face-to-face.
InterFaceGAN: Interpreting the Disentangled Face Representation Learned by GANs

Although Generative Adversarial Networks (GANs) have made significant progress in face synthesis, there lacks enough understanding of what GANs have learned in the latent representation to map a random code to a photo-realistic image. In this work, we propose a framework called InterFaceGAN to interpret the disentangled face representation learned by the state-of-the-art GAN models and study the properties of the facial semantics encoded in the latent space. We first find that GANs learn various semantics in some linear subspaces of the latent space. After identifying these subspaces, we can realistically manipulate the corresponding facial attributes without retraining the model. We then conduct a detailed study on the correlation between different semantics and manage to better disentangle them via subspace projection, resulting in more precise control of the attribute manipulation. Besides manipulating the gender, age, expression, and presence of eyeglasses, we can even alter the face pose and fix the artifacts accidentally made by GANs. Furthermore, we perform an in-depth face identity analysis and a layer-wise analysis to evaluate the editing results quantitatively. Finally, we apply our approach to real face editing by employing GAN inversion approaches and explicitly training feed-forward models based on the synthetic data established by InterFaceGAN. Extensive experimental results suggest that learning to synthesize faces spontaneously brings a disentangled and controllable face representation.
Knowledge Base Completion: Baseline strikes back (Again)

Knowledge Base Completion (KBC) has been a very active area lately. Several recent KBCpapers propose architectural changes, new training methods, or even new formulations. KBC systems are usually evaluated on standard benchmark datasets: FB15k, FB15k-237, WN18, WN18RR, and Yago3-10. Most existing methods train with a small number of negative samples for each positive instance in these datasets to save computational costs. This paper discusses how recent developments allow us to use all available negative samples for training. We show that Complex, when trained using all available negative samples, gives near state-of-the-art performance on all the datasets. We call this approach COMPLEX-V2. We also highlight how various multiplicative KBC methods, recently proposed in the literature, benefit from this train-ing regime and become indistinguishable in terms of performance on most datasets. Our work calls for a reassessment of their individual value, in light of these findings.
Depth Selection for Deep ReLU Nets in Feature Extraction and Generalization

Deep learning is recognized to be capable of discovering deep features for representation learning and pattern recognition without requiring elegant feature engineering techniques by taking advantage of human ingenuity and prior knowledge. Thus it has triggered enormous research activities in machine learning and pattern recognition. One of the most important challenge of deep learning is to figure out relations between a feature and the depth of deep neural networks (deep nets for short) to reflect the necessity of depth. Our purpose is to quantify this feature-depth correspondence in feature extraction and generalization. We present the adaptivity of features to depths and vice-verse via showing a depth-parameter trade-off in extracting both single feature and composite features. Based on these results, we prove that implementing the classical empirical risk minimization on deep nets can achieve the optimal generalization performance for numerous learning tasks. Our theoretical results are verified by a series of numerical experiments including toy simulations and a real application of earthquake seismic intensity prediction.
Supervised Categorical Metric Learning with Schatten p-Norms

Metric learning has been successful in learning new metrics adapted to numerical datasets. However, its development on categorical data still needs further exploration. In this paper, we propose a method, called CPML for \emph{categorical projected metric learning}, that tries to efficiently~(i.e. less computational time and better prediction accuracy) address the problem of metric learning in categorical data. We make use of the Value Distance Metric to represent our data and propose new distances based on this representation. We then show how to efficiently learn new metrics. We also generalize several previous regularizers through the Schatten $p$-norm and provides a generalization bound for it that complements the standard generalization bound for metric learning. Experimental results show that our method provides
PERANCANGAN BUKU KOMIK BAHAYA NARKOBA TERHADAP ANAK

Drug abuse has an adverse effect which can lead to addiction (addiction) which results in dependency. Drug abuse in Indonesia is increasingly happening in some circles ranging from people with low and high economics.The purpose of designing this comic is to inform children about the dangers of drugs and as a precautionary measure so as not to increase drug abuse. The chosen media is comic books because comic books have a lot of pictures so it is interesting for children.This design produced a comic book as the main media, and was also supported by several other media, namely: Poster, Sticker, Totebag, X-banner, Tshirt, Key Chain, and Backdrop as supporting the main media and as a promotional media.Keywords: Comics, Drugs.
PERANCANGAN USER INTERFACE APLIKASI TOKO ONLINE “MORMO STORE” BERBASIS MOBILE APPLICATION

The design of the user interface page of the mobile application page of the online shop "Mormo Store" aims to develop and increase sales of Mormo products online so that it can expand the reach of product marketing places outside the city of Padang. Buyers are also made easy to make product purchases without having to come directly to an offline store, so as to save the time and effort of buyers.The glass box method is a method that thinks logically and rationally used in the user interface design of the mobile application page of the online store "Mormo Store". In the process ofsolving problems using 5W + 1H analysis (what, who, where, when, why, how) to find solutions to the problems encountered. In compiling the flow of interactions with the application an observation is made of the user's behavior and desires, then the results of the study and observations are arranged in a layout that is as simple as possible.The main media produced in this design is the user interface application page online store mormo store. To support the main media, supporting media were created, namely motion graphics, x-banners, plastic bags, label packaging, notebooks, tumblers, and acrylic display stands. In order for the resulting design to have good results, a designfeasibility test was carried out on fifteen samples. Design tests conducted include the appearance, convenience, and comfort of the user in using the application. Based on the feasibility test that has been done, it can be concluded that the user interface of the mormo store online store application page is very well judged from the appearance, ease and comfort of the user in operating the application page.Keywords: mobile application, online store, mormo store, user interface
Digital Criminal Evidence in Traditional Crime

Evidence is that the crime was committed and attributed to the perpetrator and this is done through evidence that is examined and verified by the Public Prosecutor's Office in preparation for submission to the courts. Originally, different kinds of crimes may be proved by all means of evidence, except by special provision. The law confers on the criminal judge extensive power to choose the methods leading to the disclosure of the truth and to assess the evidence in order to investigate whether or not the crimes are established. Evidentiary methods have been affected by the evolution resulting from the information and technology revolution the compatibility between the evidence nature and the crime nature from which the development of a new type of evidence in line with the nature of information technology offences was generated, This is known as digital evidence, because traditional physical evidence has fallen short of the requirements for detecting crimes created. This has driven the search for a new type of evidence that is compatible with cyberspace. And this has led many evidence to legislate electronic evidence. The latter joined the list of evidence materials to support it and supplement it as a criminal means of evidence. The Digital evidence is often adopted either to establish an informational crime, or for an offence for which information means have been adopted This study examines the position of Lebanese legislators on digital evidence, particularly through Law No. 81/2018 on electronic transactions and personal data, and compares it with the experience of Egyptian legislators in the light of Law No. 175 of 2018 on combating IT crimes, addressing the latest judicial decisions issued by the Court of Cassation in Egypt.
Systemic deviantology: conceptualization of the problem

The article attempts to present systemic deviantology from ontological and epistemological points of view with the general goal to put an end to the protracted conceptual timelessness and to stop the degradation of the academic thought. The author thrives to recreate the deviantological science on the basis of the system theory. Inorder to accomplish that the author have developed a unified systemic thesaurus and characterized the most important elements of the deviantological knowledgefrom a unified theoretical standpoint, thereby filling the theoretical vacuum in science.
Chain-structure time-delay reservoir computing for synchronizing chaotic signal and an application to secure communication

Abstract In this work, a chain-structure time-delay reservoir (CSTDR) computing, as a new kind of machine learning-based recurrent neural network, is proposed for synchronizing chaotic signals. Compared with the single time-delay reservoir, our proposed CSTDR computing shows excellent performance in synchronizing chaotic signal achieving an order of magnitude higher accuracy. Noise consideration and optimal parameter setting of the model are discussed. Taking the CSTDR computing as the core, a novel scheme of secure communication is further designed, in which the “smart” receiver is different from the traditional in that it can synchronize to the chaotic signal used for encryption in an adaptive manner. The scheme can solve the issues such as design constrains for identical dynamical systems and couplings between transmitter and receiver in conventional settings. To further manifest the practical significance of the scheme, the digital implementation using field-programmable gate array is conducted and tested experimentally with real-world examples including image and video transmission. The work sheds light on developing machine learning-based signal processing and communication applications.
Wi-Fi And Bluetooth Contact Tracing Without User Intervention

&lt;p&gt;Previous contact tracing systems required the users to perform many manual actions, such as installing smartphone applications, joining wireless networks, or carrying custom user devices. This increases the barrier to entry and lowers the user adoption rate. As a result, the contact tracing effectiveness is reduced. Unlike the systems above, we propose a new privacy preserving Wi-Fi and Bluetooth (BLE) contact tracing system that does not require smartphone applications, joining wireless networks, or custom user devices. Our specially built routers seamlessly track smartphones, laptops, smartwatches, BLE headphones, and tablets without any user action, but do not trace user identity. Mapping between devices and users is only carried out for confirmed cases and suspected contacts. Moreover, we can track the absolute positions of user devices within 1.0 m due to using bidirectional long short-term memory neural networks that are trained with data pre-collected by an autonomous robot. This allows public health authorities to track indirect droplet and surface transmissions that other contact tracing systems often overlook.&lt;/p&gt;