Reviewerly/exHarmony huggingface dataset:
Adversarial Divergence Training for Universal Cross-Scene Classification

Cross-scene classification has recently gained increasing interest, which improves the classification performance on label-scarce domains by transferring knowledge learned from label-rich domains. Domain adaptation (DA) attempts to solve the domain gap problem and it is widely used in the cross-scene applications. The priori knowledge, that the label space between the source and target domains is identical, is a key requirement for existing cross-scene approaches to work successfully. However, label sets of different domains can always be different in real applications. That is to say, there will be common as well as private categories for different domains. Universal domain adaptation (UniDA) has been proposed to deal with the above difficulty by relaxing all constraints on the label sets. In order to complete more general remote sensing cross-scene classification tasks regardless of label sets, we propose a UniDA cross-scene classification approach, Adversarial Divergence Training (ADT), to simultaneously classify the target common categories and detect the target private categories based on the divergence of different classifiers. ADT attempts to train the classifier and feature extractor against each other (in adversarial) in order to extract more domain-invariant and discriminative features. At the same time, divergence optimization of different classifiers is used to distinguish the target private class. The former makes it capable of cross-scene tasks, while the latter weakens the effect of the label set on the performance of the algorithm. Experiments show that ADT outperforms baselines in the UniDA setting and even in other settings.
Model-Agnostic Meta-Learning for Fast Text-Dependent Speaker Embedding Adaptation

By constraining the lexical content of input speech, text-dependent speaker verification (TD-SV) offers more reliable performance than text-independent speaker verification (TI-SV) when dealing with short utterances. Because speech with constrained lexical content is harder to collect, often TD models are fine-tuned from a TI model using a small target phrase dataset. However, sometimes the target phrase dataset is too tiny for fine-tuning, which is the main obstacle for deploying TD-SV. One solution is to fine-tune the model using medium-size multi-phrase TD data and then deploy the model on the target phrase. Although this strategy does help in some cases, the performance is still sub-optimal because the model is not optimized for the target phrase. Inspired by the recent progress in meta-learning, we propose a three-stage pipeline for adapting a TI model to a TD model for the target phrase. Firstly, a TI model is trained using a large amount of speech data. Then, we use a multi-phrase TD dataset to tune the TI model via model-agnostic meta-learning. Finally, we perform fast adaptation using a small target phrase dataset. Results show that the three-stage pipeline consistently outperforms multi-phrase and target phrase fine-tuning.
Multi-hypergraph Neural Networks for Emotion Recognition in Multi-party Conversations

Emotion recognition in multi-party conversations (ERMC) is becoming increasingly popular as an emerging research topic in natural language processing. Although previous work exploited inter-dependency and self-dependency among participants, they paid more attention to the use of specific-speaker contexts. Specific-speaker context modeling can well consider the speaker’s self-dependency, but inter-dependency has not been fully utilized. In this paper, two hypergraphs are designed to model specific-speaker context and non-specific-speaker context respectively, so as to deal with self-dependency and inter-dependency among participants. To this end, we design a multi-hypergraph neural network for ERMC, namely ERMC-MHGNN. In particular, we combine average aggregation and attention aggregation to generate hyperedge features, which can make better use of utterance information. Extensive experiments are conducted on two ERC benchmarks with state-of-the-art models employed as baselines for comparison. The empirical results demonstrate the superiority of this new model and confirm that further exploiting inter-dependency is of great value for ERMC. In addition, we also achieved good results on the emotional shift issue.
Pre-training Techniques for Improving Text-to-Speech Synthesis by Automatic Speech Recognition Based Data Enhancement

As the development of deep learning, neural network (NN) based text-to-speech (TTS) that adopts deep neural networks as the model backbone for speech synthesis, has now become the mainstream technology for TTS. Compared to the previous TTS systems based on concatenative synthesis and statistical parametric synthesis, the NN based speech synthesis shows conspicuous advantages. It needs less requirement on human pre-processing and feature development, and brings high-quality voice in terms of both intelligibility and naturalness. However, robust NN based speech synthesis model typically requires a sizable set of high-quality data for training, which is expensive to collect especially in low-resource scenarios. It is worth investigating how to take advantage of low-quality material such as automatic speech recognition (ASR) data which can be easily obtained compared with high-quality TTS material. In this paper, we propose a pre-training technique framework to improve the performance of low-resource speech synthesis. The idea is to extend the training material of TTS model by using ASR based data augmentation method. Specifically, we first build a frame-wise phoneme classification network on the ASR dataset and extract the semi-supervised <linguistic features, audio> paired data from large-scale speech corpora. We then pre-train the NN based TTS acoustic model by using the semi-supervised <linguistic features, audio> pairs. Finally, we fine-tune the model with a small amount of available paired data. Experimental results show that our proposed framework enables the TTS model to generate more intelligible and natural speech with the same amount of paired training data.
VC-AUG: Voice Conversion Based Data Augmentation for Text-Dependent Speaker Verification

In this paper, we focus on improving the performance of the text-dependent speaker verification system in the scenario of limited training data. The deep learning based text-dependent speaker verification system generally needs a large-scale text-dependent training data set which could be both labor and cost expensive, especially for customized new wake-up words. In recent studies, voice conversion systems that can generate high quality synthesized speech of seen and unseen speakers have been proposed. Inspired by those works, we adopt two different voice conversion methods as well as the very simple re-sampling approach to generate new text-dependent speech samples for data augmentation purposes. Experimental results show that the proposed method significantly improves the Equal Error Rate performance from 6.51% to 4.48% in the scenario of limited training data. In addition, we also explore the out-of-set and unseen speaker voice conversion based data augmentation.
Automatic Stress Annotation and Prediction for Expressive Mandarin TTS

The current text-to-speech technique has developed to a close-to-human state, and more research interest has been paid to highly expressive and more controllable speech synthesis. Stress detection and modeling in the Mandarin TTS(Text-to-speech) system have been verified to be an efficient and direct way to enhance the rhythm and prosody performance in previous studies. But labeling stress in training data manually needs linguistic knowledge and is also time-consuming. In this paper, an automatic syllable-level stress annotation mechanism is proposed. Then based on the automatically annotated stress labels, a transformer-based ALBERT front-end module is built for stress label prediction from the text. In the experiment part, a DurIAN-based expressive text-to-speech system is built with the proposed automatic stress annotation and prediction module. Experiments show the proposed method can consistently predict stress from linguistic context input, and speech synthesis systems with proposed stress annotation and prediction components outperform baseline systems.
WMS: Wearables-Based Multisensor System for In-Home Fitness Guidance

Human activity recognition (HAR) is now a powerful in-home fitness assistive technology. This article presents a wearables-based multisensor system (WMS), which not only supports conventional functionalities, such as motion evaluation based on multidimensional information about the user’s body (movement speed, angle, muscle states, etc.), but also provides advanced services, including assessing training fatigue and providing real time, elastic, and professional training advice. The proposed WMS is experimentally validated by yielding 90.11% accuracy of motion evaluation with 36% and 23% improvement of fitness effect on bicep girth and muscular endurance, indicating its feasibility to prompt the development of HAR in the in-home fitness training domain.
Violence Detection Through Fusing Visual Information to Auditory Scene

In the field of audio and video detection, violence detection is a crucial task with significant theoretical and practical implications. In order to solve the present issue of the lack of violent audio datasets, we first created our own audio violent dataset named VioAudio. Then, we proposed a CNN-ConvLSTM network model for audio violence detection, which obtained an accuracy of 91.5% on VioAudio and a MAP value of 16.47% on the MediaEval 2015 dataset. Meanwhile, this paper integrated self-attention mechanisms and visual information into CNN-ConvLSTM network in order to address the issue of modality singularity in violence detection, and then confirmed them on MediaEval2015 dataset. The experimental results demonstrate that after fusing visual and auditory information, the CNN-LSTM network model greatly enhanced recognition accuracy, attaining a 31.25% MAP value, which is 1.94% higher than the best result. The method proposed in this paper considerably increased the accuracy of violence detection and offered fresh perspectives on how to integrate multimodal information to identify violence.
Scheme iLearning DApps for Education using Blockchain-Based

In the era of the digital revolution as it is today, technology is widely applied as the main thing in all fields of science. However, the current problem is that it is difficult to get information with the development of technology. Because many operations are performed manually, searching for customer information, entering inventory items, and payment transactions, the data display time required is very long for the user. As well as to record reports including paper, notes, receipts, pens, etc. When an error occurs during input, the device is often used simultaneously. This research aims to help overcome the problem of using Bitcoin cryptocurrency technology in payment systems for education in Indonesia. This research method uses Agile Software Development method with the Scrum type and DLCS (Digital Learning Chain Scheme) use a DApps Blockchain approach to build strategies, and use Google Forms to collect information through digital observations and questionnaires. Based on the characteristics of the system, the Slovin formula was used to analyze different aspects of the excellent design, and the result 0.840 > 0.6 was used for processing; if alpha > 0.6, then the system can be classified as "reliable." Reliability test to show that the assessment is based on the questionnaire provided. This research hopes to establish new normality in implementing bitcoin and provides a rather complicated technical so that the effect can be achieved if applied to the education sector. Novelty in this research system will store information about all member providers in the network to validate transactions.
Integration of Renewable Energy Technology in Waste Recycling Utilization

The increase in the production of municipal solid waste around the world will be expected to continue to increase. Even though developed countries already have recycling technology to convert waste into products that can be reused, such as generating electricity, heat energy, composting fertilizers, and biofuels, there are still many challenges created by waste because in developing countries the countermeasures themselves are still so things are difficult starting from the inadequate collection, transportation, and disposal of waste. This research will solve the problem of evaluate various waste-to-energy technologies that have been developed since the invention that was able to convert recycled waste into renewable energy until now. This research can be divided into four groups. Namely starting from waste treatment thermally, biologically, biorefinery, and utilization of landfill gas. The novelty of this research for waste-to-energy technology with an integrated waste management system, it has been safely understood and tested feasible for energy renewal which can already show several examples from around the world. The method used to solve the unresolved waste problem in this scientific paper is to use WTE and ISWM-S technology which can certainly be a brilliant solution in converting urban solid waste into renewable energy for a clean world from waste to waste and also provides a future. Where people no longer need to worry about the limitations of limited natural resources to be processed into energy.
Application Fitting Room using Virtual Technology: A Systematic Literature Review

Due to the pandemic of 2020, the e-commerce industry has grown significantly as people have become more open to online shopping. According to eMarketer, retail e-commerce sales increased by 25.7%. One of the industries with the most growth is Fashion. Although according to many reports, the e-commerce fashion industry is expected to grow, there are problems in this online fashion industry where it’s hard for customers to decide on buying the apparel that may fit them. One of the solutions for this problem is through fitting room application using virtual technology. A virtual fitting room can give novelty to customers a good experience where customers can virtually try clothes without actually wearing them. This research aims to review the procedure and algorithm involved in a virtual fitting room application. The research method uses a systematic literature review using the PRISMA framework. Based on our study, we can conclude that Virtual Fitting Room applications involve finding the lean body of the target image, then inserting the input image (clothes) into the target image using a Neural Network Algorithm to make an impact to fit the clothes realistically. One of the most used frameworks is the GAN and VTON framework in creating models for contribution to the Virtual Fitting Room Application.
A Bibliometric Study for Blockchain Technology in Academic Journals

The Blockchain has become a revolutionary pawn in various sectors, including education. A Blockchain can also liaison between computer networks that run in a centralized and distributed manner. The use of bibliometric technology acts as an indicator of research status. This supports applying statistical methods to specific scientific research topics and manages the profile of publications on those topics. This study evaluates the development of research publications in the implementation process and future trends of Blockchain Technology. This study contribute to assist researchers in creating a theoretical framework and to provide an initial reference source for mapping research in this area. All research publications related to the implementation and future trends of Blockchain technology in the Scopus database are analyzed using several bibliometric indicators. By using bibliometric indicators, the analysis of this study will advance Blockchain technology research in general. Author, number of citations, journal source, publisher, institution, country, year of publication, category, and author keywords were analyzed. The main novelty of this research is the innovation of this analysis to support and develop the application of statistics to certain scientific research topics based on the current bibliometric ecosystem. For future research, the authors recommend that detailed specifications governing the expansion of scholarly journals be established.
Opportunities and Conditions in Education on Blockchain Gamification Technology

In this disruptive 4.0 era, many generations are competing in creating a new innovation, it makes technological growth increase sharply, one of which is blockchain technology. This technology has ledgers that are simultaneously decentralized and distributed. There is a node that functions to record all transaction histories on the Blockchain with all different networks. Blockchain This is certainly a very practical and safe technology for the world of education, one of which is Gamification. Gamification itself means a concept that has layers of features like a game that is connected or integrated with the blockchain. Gamification provides opportunities for the world of education by creating game elements that focus on the orientation of creating new educational spaces. This study uses the analisis Gamifikasi method and SWOT analysis to determine the overall use of the system starting from the strategies, opportunities, strengths and weaknesses. With this research, it is hoped that gamification in the blockchain system can run optimally in improving the implementation of Blockchain technology in the world of education. The novelty of this research is to apply a concept that has a feature layer such as a game that is connected or integrated with a block chain.
Overview of the Integration Between Electric Vehicles and Smart Grid Technology

This paper presents a large-scale study and evaluation of the most recent discoveries and advances of electric vehicle communication with a brilliant grid technology describing a future electric powertrain model. The goal of the smart matrix along with future electric vehicle layouts addresses various challenges related to the platform, suitability and control of the electrical system. After a survey focusing on advanced measurement and matching frameworks, the methodology of electric vehicles in the grid is introduced. Other nifty advances in electric vehicle charging are also extensively analyzed in terms of their potential capabilities, effects and limitations in grid-connected vehicle conditions. In addition, the strong penetration of sustainable energy sources (wind and solar photovoltaic) is creating momentum as part of the force. Their discontinuous performance of force causes many difficulties in the organization, operation and control of force organizations. Again, the placement of electric vehicles in the energy market can compensate for changes in the power grid. In these particular cases, a written assessment of the most recent possible and environmentally friendly energy regulation using electric vehicles with knowledge of promising exploration holes Appointments that need to be concealed will be checked. In addition, the rationale of shredding facilities at the grid is also discussed in full. In this paper, the communication of electric vehicles with the shiny grid as a future energy framework model is discussed extensively using technology, and a research gap is discovered for potential arrangements.
Blockchain Technology Based Credibility Verification in E-Journal Entities

In the industrial era 4.0, it has surpassed increasingly complex technological advances in the information system that required a very high infrastructure and facilities and prevented fraud. Counterfeiting is a procedure following an entity, statistics, or documents (observe forged documents) with plans to deceive. Falsification of extensive information certificates is exacerbated by the current economic situation in Indonesia, which is currently occurring due to the widespread epidemic, namely COVID-19. The spread of the COVID-19 outbreak has had a significant impact and changes in all sectors, especially certificate forgery in Indonesia. The imitation of busy certificates is the main focus of this journal, focusing on journal publications combined with Blockchain technology. The sophistication of blockchain technology as authentication is comparing two or more items or additional tests to ensure the accuracy, correctness, or correctness of the information. This method uses qualitative methods with data sources based on case studies in controlled supervision with the basic concept of cryptography as the basis of analysis. From the decisions made, it is hoped that this will minimize the level of forgery of certificates widely measured during the COVID-19 era.
New Business Search Model using Patent Landscape in Japan

In recent years, intellectual property departments in Japanese companies have been required to analyze patent information to propose new businesses to management and business unit managers that can capitalize on their company’s strengths. A model (Mirror method) was proposed in a previous study to search for new businesses suitable for the company by analyzing patent information, beginning with the company’s strengths. In a previous study, it was reported that the Miller method can be used as a pilot study to search the cosmetics business, a new business of Fujifilm in the 2000s, but this is the only case where the method was used, and the effectiveness of the Miller method has not been verified for other industries, products, or periods. Therefore, in this study, we used the Miller method on a different case than in previous studies to verify if the method is effective. Specifically, we took on the AminoIndex business, which evaluates disease risk from blood samples, which was launched by Ajinomoto Co. in the 2010s. It was confirmed as a result of the verification that the Miller method can be used to extract business related to investigation and analysis of biological materials such as blood and urine using chromatographic methods, which includes Ajinomoto’s new business in the 2010s, screening disease risk from blood.
Implementation of the Tableau Application to Determine Earthquake Prone Areas with Geolocation Features

Earthquakes are natural disasters that can cause a lot of material losses and even fatalities. Geographically, Earthquakes often occur in the Ring of Fire area, which is a location that frequently experiences earthquakes and volcanic explosions. United States Geological Survey (USGS), Meteorology Climatology and Geophysics Agency (BMKG), and National Regional Disaster Management Agency (BNPB) are agencies that have data on disasters that occur in large sizes. Until the data can be processed into more valuable information, an application must visualize data to display information in disaster-prone areas and be safe from disasters, especially earthquakes. This research aims to utilize Tableau applications, such as the collection and grouping of seismicity data and classification of earthquake disasters that occur in each province, especially in Indonesia, and displaying its visualization on the map with geolocation feature using earthquake seismicity data. Deployment of applications using Tableau and methods Very Business Intelligence suitable for this case to classify disasters, especially earthquakes in every province in particular in Indonesia, to make it easier for people to get information about the earthquake disaster that occurred based on the facts.