Reviewerly/exHarmony huggingface dataset:
Minimizing Residuals for Native-Nonnative Voice Conversion in a Sparse, Anchor-Based Representation of Speech

We present a dictionary-learning algorithm for reducing the sparse coding residual of an exemplar-based method for native-to-nonnative voice conversion (VC). The proposed algorithm iteratively updates the source and target speaker dictionaries to reduce both the residual and voice conversion error, thereby increasing synthesis quality. We evaluate the method on speech from the ARCTIC and L2-ARCTIC corpora and compare it to a baseline exemplar-based VC algorithm. The proposed algorithm significantly improves synthesis quality to more than double that of the baseline system while using two orders of magnitude fewer atoms. Additionally, the proposed algorithm significantly reduces both the VC error and the residual magnitude. We discuss the implications of the algorithm for broad exemplar-based VC systems.
Adaptive Max-Margin One-Class Classifier for SAR Target Discrimination in Complex Scenes

Synthetic aperture radar (SAR) target discrimination is an important stage that distinguishes targets from clutters in the radar automatic target recognition field. However, in complex SAR scenes, the performance of some traditional discriminators will degrade. As an effective tool for one-class classification (OCC), the max-margin one-class classifier has attracted much attention for SAR target discrimination, as it can effectively reduce the impact of multiple clutters. However, the performance of the max-margin one-class classifier is very sensitive to the values of kernel parameters. To solve the problem, this paper proposes an adaptive max-margin one-class classifier for SAR target discrimination in complex scenes. In a max-margin one-class classifier with a suitable kernel parameter, the distance between a sample and classification boundary satisfies a certain geometric relationship, i.e., edge samples in input space are transformed to the region in the kernel space close to boundary, while interior samples in input space are transformed to the region in the kernel space far away from boundary. Therefore, we define the information entropy of samples in the kernel space to measure the distance between samples and classification boundary. To automatically obtain the optimal kernel parameter of the max-margin one-class classifier, the edge and interior samples in the input space are first selected, and then the parameter optimization is performed by minimizing information entropy of interior samples and simultaneously maximizing the information entropy of edge samples. Experimental results of the synthetic datasets and measured synthetic aperture radar (SAR) datasets validate the effectiveness of our method.
An Improved Kernel Correlation Filter Tracking Combined with Mobilenet SSD

&lt;p&gt;This article mainly solves the problems that exist when using the Kernel Correlation Filter (KCF) for tracking in complex scenarios. To make the algorithm suitable for target tracking under complex conditions such as scale changes, similar interference, and occlusion, a MobileNet SSD (Single Shot Detection) target detection combined with an improved KCF target tracking algorithm is proposed. Firstly, the MobileNet SSD is used to locate the target in the initial frame, and the location is sent to KCF for training. Secondly, aiming at the problem of scale changes, a Binary-Tree scale search strategy is proposed. In this strategy, the scale value is searched in a tree shape according to the response size, which reduces the number of scale searches. Finally, the average peak correlation energy is used for occlusion determination, and the model update strategy is improved, thereby enhancing the algorithm’s ability to track occluded targets. The results of experimental evaluation and comparison on the OTB100 and UAV123 data sets show that when the target has complex conditions such as scale changes, similar interference, occlusion, etc., the proposed algorithm performs well in mainstream related filtering algorithms. Through the quantitative and qualitative analysis of the experimental results, the effectiveness of the proposed algorithm is verified.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Student-Based Disaster Management: Alternative Solutions to Build a Disaster-Resilient City

Disasters that have occurred in the past should serve as a valuable lesson for those who will come after them. This is the most important factor in determining how prepared a community is. According to common knowledge the city of Palu is one of the cities that is particularly vulnerable to natural disasters due to its geographical location, which is situated between the confluence of two tectonic plates of the Pacific Ocean and Asia, necessitating the need for residents who live in the area to be prepared for such events. As a result, the purpose of this study is to determine the value of studying disaster preparedness from a young age in order to reduce the impact of disasters generated through the use of a spatial education strategy. The descriptive method is used with a qualitative approach in this investigation. The findings indicate that preparedness education should begin at a young age in order to instill a sense of vigilance in kids, rather than a sense of terror. Education in geography, and specifically geography-based disaster preparedness learning materials, is critical to establishing a solid foundation in disaster preparedness learning from a physical and geographical perspective. Keywords: Learning, Preparedness, Geography Education, Students, Disaster.
[The design and practice of Molecular Biology Experiment teaching from the perspective of first-class courses].

Teaching quality is directly related to the performance of universities in fostering talents. Being innovative, high-level, and challenging (IHC) is the basic goal of course reform at universities in the new era. It is essential to reform the contents and teaching mode to improve the IHC properties of the existing courses. We first designed the three-dimensional goals of Molecular Biology Experiment teaching and the contents to support these goals. Then, we pinpointed the common points shared by blended teaching and experiment course, and designed the ways of blended teaching for the course. The reformed course contents and teaching mode have enhanced its IHC properties, and achieved good teaching performance. This paper provides a reference for the reform of experiment courses in universities.
ASE4Games 2021 Workshop Summary

The first edition of the workshop on Automated Software Engineering For Games (ASE4Games 2021) was held virtually on November 14th, 2021, co-located with the 36v IEEE/ACM International Conference on Automated Software Engineering (ASE 2021). Five papers from all over the world were submitted, two full-papers and two short-papers were accepted. The program also featured a keynote by Mathieu Nayrolles, researcher at Ubisoft, entitled Automated Software Engineering for AAA Games.
SISTEM PAKAR MENDIAGNOSA PENYAKIT COVID-19 DENGAN MENGGUNAKAN METODE CERTAINTY FACTOR

COVID-19 is an infectious disease caused by a newly discovered type of coronavirus. The WHO (World Health Organization) reported that this virus first appeared on December 31, 2019 and the country that was first confirmed was China, precisely in the city of Wuhan. Indonesia became one of the confirmed countries after President Jokowi and Minister of Health Terawan Agus Putranto on Monday, March 2, 2020. Most people who are exposed to COVID-19 experience symptoms such as: fever, respiratory tract infection, loss of sense of smell, coughing runny nose, headaches, sore throats, loss of sense of taste, and nausea.. Previous research is a science to find comparisons and results to find new inspiration for research. Research methodology is a scientific process or method to obtain data to be used for research purposes. Methodology is also a theoretical analysis of a method or method, research is a systematic investigation to increase a number of knowledge. Based on the results of the CF calculation, the value obtained for the Covid-19 disease from the calculation results above can be seen the level of confidence from the results of the diagnosis of the Covid-19 disease, which is 0.97 x 100%, which is 97% with the results obtained, the system identifies that the patient is Covid-19 negative. Based on the results of the analysis and design that have been achieved, it can be applied to apply an expert system application to diagnose Covid-19 disease, where in this application the user can enter and find out the types of symptoms of the Covid-19 disease that the user has and can find out the right handling solution to help dealing with Covid-19.
Research on Online and Offline Mixed Education Mode in Post Epidemic Era Based on Fuzzy Neural Network-Taking Introduction of Petrochemical Equipment Management as an Example

&lt;p&gt;In the post epidemic era, the online offline mixed teaching mode is in the development period, and college teachers and students are gradually familiar with this teaching mode. Taking the course introduction to petrochemical equipment management as an example, this paper deeply analyzes the development path of online offline mixed teaching mode. First of all, this paper explores the bottleneck of online teaching in the epidemic era, deeply analyzes the relationship between online teaching and offline teaching, and puts forward the possibility of integration of offline teaching and online teaching. Based on the above analysis results, this paper summarizes the opportunities of Blended Teaching in the post epidemic era. This paper puts forward the practice path of the online and offline mixed teaching mode of &amp;ldquo;Introduction to petrochemical equipment management&amp;rdquo;. Finally, the online and offline mixed teaching quality evaluation model of &amp;ldquo;Introduction to petrochemical equipment management&amp;rdquo; is constructed, the mixed teaching quality evaluation index system is determined, and the evaluation model based on improved firefly algorithm optimization fuzzy neural network is constructed, and the example analysis is carried out, and the online and offline mixed teaching quality of &amp;ldquo;Introduction to petrochemical equipment management&amp;rdquo; is determined as good, It provides a favorable theoretical basis for the reform strategy of mixed teaching mode, and also verifies the effectiveness of the evaluation model.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Missing Data Interpolation with Variational Bayesian Inference for Socio-economic Statistics Applications

&lt;p&gt;The information integrity is needed to solving socio-economic statistical problems. However, the information integrity is destroyed by missing data which is caused by various subjective and objective reasons. So the missing data interpolation is used to supplement missing data. In this paper, missing data interpolation with variational Bayesian inference is proposed. This method is combined with Gaussian model to approximate the posterior distribution to obtain complete data. The experiments include two datasets (artificial dataset and actual dataset) based on three missing ratios separately. The missing data interpolation performance of variational Bayesian method is compared with that which is obtained by mean interpolation and K-nearest neighbor interpolation methods separately in MSE (Mean Square Error) and MAPE (Mean Absolute Percentage Error). The experimental results show that the proposed variational Bayesian method is better in MSE and MAPE.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Learning to Win Lottery Tickets in BERT Transfer via Task-agnostic Mask Training

Recent studies on the lottery ticket hypothesis (LTH) show that pre-trained language models (PLMs) like BERT contain matching subnetworks that have similar transfer learning performance as the original PLM.These subnetworks are found using magnitude-based pruning.In this paper, we find that the BERT subnetworks have even more potential than these studies have shown.Firstly, we discover that the success of magnitude pruning can be attributed to the preserved pre-training performance, which correlates with the downstream transferability.Inspired by this, we propose to directly optimize the subnetwork structure towards the pre-training objectives, which can better preserve the pre-training performance.Specifically, we train binary masks over model weights on the pre-training tasks, with the aim of preserving the universal transferability of the subnetwork, which is agnostic to any specific downstream tasks.We then fine-tune the subnetworks on the GLUE benchmark and the SQuAD dataset.The results show that, compared with magnitude pruning, mask training can effectively find BERT subnetworks with improved overall performance on downstream tasks.Moreover, our method is also more efficient in searching subnetworks and more advantageous when fine-tuning within a certain range of data scarcity.
<i>Gems of Hiddenite, North Carolina: Mining History, Geology, and Mineralogy</i> by Mark Ivan Jacobson and Wade Edward Speer. McFarland &amp; Company, www.mcfarlandpub.com. 217 pages; 2021; $39.95 plus shipping and handling (softbound).

"Gems of Hiddenite, North Carolina: Mining History, Geology, and Mineralogy by Mark Ivan Jacobson and Wade Edward Speer. McFarland & Company, www.mcfarlandpub.com. 217 pages; 2021; $39.95 plus shipping and handling (softbound).." Rocks & Minerals, 97(3), pp. 290–291
Analysis and Research on the Effect of Rosenthal Effect on College Students' Volleyball Training

In this study, 40 undergraduates from Ludong university were selected as the research sample, and the methods of documentation, experiment and mathematical statistics were used to carry out the normal training by rosenthal effect groups, it was found that the students trained by the rosenthal effect was higher than that of the students not trained by the rosenthal effect. Not only could the atmosphere of the volleyball training be harmonized, but also the training effect was good, this shows that the rosenthal effect plays an important role in volleyball training.
The Effect of Digital Trade Competence and the Use of the 4th Industrial Revolution Technologies on Export Performance of Korean Exporting Firms

본 연구는 급변하는 디지털무역 환경에서 한국 수출제조기업의 내부역량인 디지털무역 역량을 결정하는 주요 요인으로 기업의 혁신역량, 해외마케팅역량, 디지털역량을 도출하였고, 기술변화와 경쟁강도를 외부환경요인으로 설정하여 이러한 요인들이 기업의 4차산업혁명기술 활용정도와 수출성과에 미치는 영향을 150개의 수출제조기업에 대한 설문조사를 통해 실증 분석하였다. 연구가설을 SmartPLS(v.3)분석방법을 사용하여 검정한 결과 기업의 디지털무역 역량인 혁신역량과 해외마케팅역량, 디지털역량은 기업의 4차산업혁명기술의 활용정도와 수출성과에 긍정적 영향을 주는 것으로 나타났다. 반면, 외부환경인 시장의 기술변화와 경쟁강도는 기업의 4차산업혁명기술 활용에 직접적으로 유의적인 영향을 미치지 않는 것으로 나타났다. 따라서 기업이 4차산업혁명기술을 채택하고 활용하는 것은 모든 기업들에게 공통적인 영향을 미치는 외부환경요인보다는 기업의 내부자원인 디지털무역 역량의 확보 및 활용의 중요성이 확인되었고, 4차산업혁명기술의 활용이 기업의 수출성과에도 긍정적인 영향을 미치는 것으로 분석되었다.This study empirically analyzed the relationship between digital trade capabilities of Korean export firms and export performance according to the degree of use of the 4th Industrial Revolution Technology in a rapidly changing digital trade environment. Based on resource-based theory and industrial organization theory, surveys were conducted on 150 companies using the 4th Industrial Revolution Technology, and statistical analysis was conducted through Smart PLS(v.3). As a result, it was found that among the digital trade capabilities of a company, innovation capabilities, overseas marketing capabilities, and digital capabilities have a positive effect on the degree of use and export performance of the company s 4th industrial revolution technology. On the other side, it was found that the technological change and competition intensity of the market, which is an external environment, did not directly affect the use of the 4th industrial revolution technology by companies. Therefore, the importance of securing and utilizing the company s internal resources, digital trade capabilities, rather than external environmental factors that have a common impact on all companies was confirmed, and the use of the 4th industrial revolution technology also had a positive effect on the export performance.
Quantum Machine Learning and Big Data for Real-Time Applications

Massive amounts of data in many forms, including text, images, video, and more, are referred to as big data. The storage and processing capabilities of a conventional computer can’t keep up with the demands of big data. As a result, it’s tough to spot patterns in large amounts of data. Large quantities of data take a typical computer a long time to process. Using quantum computing, data and information may be calculated quickly. Using big data with quantum machine learning to detect data patterns for a range of applications is the subject of this review research. When compared to traditional computers, quantum-guided and unsupervised machine learning have distinct benefits. The challenges, future scope, and techniques of quantum computing with machine learning algorithms are also addressed. Some real-world applications have been suggested, even if quantum machine learning is still an emerging field. This means that machine learning is being considered for industrial applications, and several studies have cited machine learning as one of the key enablers to move traditional production systems up to today’s high-tech “Industry 4.0” standards. Professionals in this sector will be able to gain a better understanding of the subject matter through the presentation and organization of this complete review.
Research on Demonstration and Validation of Space Rapid Response Launch System based on Data Link

Abstract The demonstration and verification of space rapid response launch system based on data link mainly combines with the process, style and characteristics of space rapid response launch, through construction the typical mission scenarios by simulation, carries out design and verification of wide-area maneuver anti-interference networking technology system between platforms such as space and ground data link nodes, container launcher and micro-nano satellite systems, research and design of formatting message standard system, etc., to promote the integrated application of data link and space rapid response launch equipment, so as to enhance command and control, situation sharing and tactical coordination capabilities.
When do Contrastive Word Alignments Improve Many-to-many Neural Machine Translation?

Word alignment has proven to benefit many-tomany neural machine translation (NMT).However, high-quality ground-truth bilingual dictionaries were used for pre-editing in previous methods, which are unavailable for most language pairs.Meanwhile, the contrastive objective can implicitly utilize automatically learned word alignment, which has not been explored in many-to-many NMT.This work proposes a word-level contrastive objective to leverage word alignments for many-to-many NMT.Empirical results show that this leads to 0.8 BLEU gains for several language pairs.Analyses reveal that in many-to-many NMT, the encoder's sentence retrieval performance highly correlates with the translation quality, which explains when the proposed method impacts translation.This motivates future exploration for many-to-many NMT to improve the encoder's sentence retrieval performance.
Investigative approach for consequences of computational delay of cloud data centers towards eco-friendly green computing paradigm

Green technology is the emerging new sustainable technology which paves the way for lowering the raise in the earth’s temperature. The increase in toxic waste from the electronic devices in every level of usage increases the global warming and thus the need of green technology is inevitable. The contributions towards green computing in global community will save the environment by minimizing the energy consumption. Emerging technologies in Internet of things, pervasive applications, cloud computing provides effective consumer, scientific and business domains. Due to the massive data storage and computation of cloud computing, there is an exponential increment in the demand of cloud computing. The growth of enormous amount of data brings a huge challenge to the cloud computing environment. Data centers are the main module of the cloud computing environment, but the major issue is the management of power computing in those data centers. Green data centers are established with limited power consumption and carbon-di-oxide emission. The research work mainly focusses on the delay time of the data centers in Internet cloud which in turn increases the usage of power consumption in data centers. The analysis is simulated with Internet cloud and data centers.
Locally Aggregated Feature Attribution on Natural Language Model Understanding

With the growing popularity of deep-learning models, model understanding becomes more important.Much effort has been devoted to demystify deep neural networks for better interpretability.Some feature attribution methods have shown promising results in computer vision, especially the gradient-based methods where effectively smoothing the gradients with reference data is key to a robust and faithful result.However, direct application of these gradient-based methods to NLP tasks is not trivial due to the fact that the input consists of discrete tokens and the "reference" tokens are not explicitly defined.In this work, we propose Locally Aggregated Feature Attribution (LAFA), a novel gradient-based feature attribution method for NLP models.Instead of relying on obscure reference tokens, it smooths gradients by aggregating similar reference texts derived from language model embeddings.For evaluation purpose, we also design experiments on different NLP tasks including Entity Recognition and Sentiment Analysis on public datasets as well as key feature detection on a constructed Amazon catalogue dataset.The superior performance of the proposed method is demonstrated through experiments.
Smart Agriculture Suggestion System

Abstract: Agriculture is a career that has been performed for many years. However, as time passes, people's confidence in this profession is eroding. One of the reasons for this is that agriculture is practiced in an unscientific manner. Traditional approaches are getting less successful as time passes. To address this, the method described here analyses the current environmental and soil parameters and predicts the best crop for the scenario. This technology is intended to assist farmers by serving as a gateway between them and all accessible information. The project's main goal is to gather information from many sources on the internet and display it to the user in a user-friendly and understandable manner. This site will serve as a resource for farmers around the country, as 3well as a platform for them to connect and share their important resources and expertise with one another. We also want to dedicate a part of this programme solely to crop, equipment, and labour management. We want to make resource collection and farm management considerably more efficient. Keywords: Agriculture, Management, Sustainable Web, Android