Reviewerly/exHarmony huggingface dataset:
Multi-level Feature Extraction and Edge Reconstruction Fused Generative Adversarial Networks for Image Super Resolution

At present, the image super-resolution method based on convolutional neural network has achieved a very high PSNR, but the high-frequency information obtained by using the mean square error as the loss function is not sufficient, and when the scale factor is large, the detail texture of the restored image is blurred, and it is not completely consistent with the human visual perception. Therefore, this paper proposes an image super-resolution algorithm based on GAN. We modify the residual block of the original SRGAN generator network into three modules: Edge-Reconstruction network, Low-Frequency feature (LF-feature) extraction module and Residual network. The Edge-Reconstruction network reconstructs the edge of SR image, and the LF-feature extraction module extracts the low-frequency information of the image. After that, the two parts of information are fused and transmitted to Residual network to extract the high-frequency information of the image, and then the SR image is reconstructed and enlarged. And use skip connection in the network to increase the network depth. The training results show that our network has better performance in both objective evaluation indicators and subjective vision. Even with a large-scale factor, our network can recover fine texture information.
FASST: Few-Shot Abstractive Summarization for Style Transfer

Unsupervised text style transfer methods aim to transfer the style of the text without affecting its fundamental meaning using non-parallel data. Although previous work has explored few-shot learning for this task, incorporating few-shot abstractive summarization and its benefits have not yet been explored. Hence, we propose a novel unsupervised text style transfer approach using few-shot abstractive summarization. In our method, we infer a vector space embedding for the corpora and align the source-target embeddings using their vector space centroids. A set of nearest neighbors is retrieved for every source text unit from the target style based on their semantic similarity in the aligned vector space. Multiple subsets of nearest neighbors are extracted and summarized using a language model with a reranking procedure to optimize the style transfer quality, which achieves state-of-the-art results on automatic evaluation metrics.
Multilingual BERT Cross-Lingual Transferability with Pre-trained Representations on Tangut: A Survey

Natural Language Processing (NLP) systems have three main components including tokenization, embedding, and model architectures (top deep learning models such as BERT, GPT-2, or GPT-3). In this paper, the authors attempt to explore and sum up possible ways of fine-tuning the Multilingual BERT (mBERT) model and feeding it with effective encodings of Tangut characters. Tangut is an extinct low-resource language. We expect to introduce a tailored embedding layer into Tangut as part of the fine-tuning procedure without altering mBERT internal structure. The initial work is listed on. By reviewing existing State of the Art (SOTA) approaches, we hope to further analyze the performance boost of mBERT when applied to low-resource languages.
A Survey of Speech Recognition Based on Deep Learning

Artificial intelligence is the vane leading the world’s scientific and technological development and future lifestyle change in the 21st century, and speech recognition, as one of the indispensable technical means, is inevitably the focus of human attention. There are two problems in traditional speech recognition: first, speech recognition technology cannot be significantly improved, and second, speech recognition systems cannot accurately extract data and features. In order to solve these problems, this paper first compares the traditional speech recognition GMM-HMM model and establishes a DNN-HMM model, which proposes a method to improve the speed of speech recognition and greatly improves the recognition rate. However, DNN-HMM lacks the ability to use historical information to assist in the current task, and a second model is proposed on the basis of this problem, that is, the LSTM model is used to solve the problem of insufficient contextual information, which further improves the speech recognition ability. Then, in order to solve the problem of long memory loss and speed up training, the Transformer model is cited, and in order to solve the problem that the traditional language model can only predict the next word in one direction, the BERT model, which has a bidirectional language model, is invoked.
What are You Posing: A gesture description dataset based on coarse-grained semantics

At present, algorithms for human pose estimation and image caption are prosperous but have disadvantages. The current mainstream algorithms of pose estimation only present the information of key nodes as a scalar but lacks semantics, while in most of algorithms for human image captioning, more attention is paid to the relationship between human bodies and the background, without understanding the human body semantics, which can not meet the need of deep visual understanding.In this paper, to fill in imperfection in previous studies, we provide a novel data set of the caption of human pose estimation for the deep understanding of image semantics. Moreover, we use the pose estimation system to extract posture figures and then we utilize the encoder-decoder to generate the captions of human poses in single picture, to produce deeper understanding of the original image. Lastly, we use Bert to carry out the next step of reasoning and get a further understanding. Our data set is open source.
A Lightweight Human Pose Estimation Algorithm Based on High Resolution Network

Human pose estimation is an important research direction in the field of computer vision. At present, the mainstream human pose estimation algorithms have high complexity, large amount of calculation, and cannot be run on resource-constrained devices such as mobile terminals, which severely limits the popularization and application of this technology. Aiming at the problem of increased network model parameters and computational complexity, based on the High-Resolution Network (HRNet), a lightweight human pose estimation network incorporating Ghost module and attention mechanism is proposed. Replaced with Ghost convolution, and added the attention mechanism Concurrent Spatial and Channel Squeeze and Channel Excitation Net module on this basis to ensure the prediction accuracy of the network. Under the same image resolution and environment configuration, the experimental results on the COCO dataset show that the improved network model reduces the number of parameters by 98.3% compared to the high-resolution network model, and reduces the computational complexity by 67.6%. The experimental results show that the improved network model can effectively reduce the amount of network parameters and reduce the computational complexity while maintaining a certain prediction accuracy.
Research based on improved SSD target detection algorithm

In view of the problem of missed detection and false detection in complex environment, especially at night environment and false target environment, the detection ability of target detection is poor. With the development of deep learning, an improved SSD-based target detection algorithm is proposed, and the attention mechanism and function fusion module are added on the basis of SSD, which is integrated into the original network. Secondly, FPN module is a kind of shallow network, which is used to integrate deep network and shallow network to improve the representation ability of semantic information. Experiments were carried out on VOC2007 data set, pseudo target detection data set and night target detection data set. The results show that the detection accuracy of this method is up to 92.1%, which is verified by the camouflage data set and the night target detection data set. Compared with SSD andMobile-V2-SSD, the detection accuracy of this method is improved by 16.3% and 4.8%, respectively, and it has better robustness and real-time detection ability in complex environments.
Generalization Algorithm of Multimodal Pre-Training Model Based on Graph-Text Self-Supervised Training

Recently, a large number of studies have shown that the introduction of visual information can effectively improve the effect of neural machine translation (NMT). Its effectiveness largely depends on the availability of a large number of bilingual parallel sentence pairs and manual image annotation. The lack of images and the effectiveness of images have been difficult to solve. In this paper, a multimodal pre-training generalization algorithm for self-supervised training is proposed, which overcomes the lack of visual information and inaccuracy, and thus extends the applicability of images on NMT. Specifically, we will search for many pictures from the existing sentences through the search engine, and then through the relationship between visual information and text, do the self-supervised training task of graphics and text to obtain more effective visual information for text. We show that when the filtered information is used as multimodal machine translation for fine-tuning, the effect of translation in the global voice dataset is 0.5 BLEU higher than the baseline.
Implementation of Node Classification Algorithm Based on Graph Neural Network

With the research and development of Graph Neural Networks (GNNs), GNN has shown very good results in link prediction, node classification, social network and other applications. In this paper, the node classification algorithm based on GNN is implemented by software, and the neural network models that need hardware acceleration are selected and trained. The comparative experiments are conducted on Cora, CiteSeer and PubMed citation network datasets respectively. Through the model training of the combination of different aggregation update functions, the comprehensive analysis of the experimental results shows that the combination of message passing layer functions used in this paper has the best effect, and the test accuracy in three data sets reaches 77%, 59% and 75% respectively. In order to better deploy the network model on the hardware, the symmetric quantization operation is carried out to reduce the parameters, so as to achieve the acceleration of the software part. The experimental results show that the accuracy of the quantized model is almost unchanged.
ASSESSMENT OF READING LITERACY

The article describes the experience of developing measuring materials in the framework of functional literacy monitoring and describes the conceptual foundations for assessing reading skills in PIRLS and PISA in the context of international studies. It gives an idea of the role and place of modern assessment tools for measuring the educational skills of primary school students, describes the specifics of a set of tools developed by students to assess and form modern educational skills, provides examples of tasks for assessing reading literacy, describes the features of tasks developed by the authors to assess the educational skills of students in grades 5 and 7, 8. The purpose of this article is to develop conceptual approaches to assessing the reading literacy of primary school students, developed at the Center for Educational Quality Assessment of the Institute of Educational Development Strategy of RAO, and appropriate tools: tasks, systems for evaluating these tasks, processing and interpreting the results obtained. As part of the monitoring of functional literacy, the need to study the issue of the formation and evaluation of reading literacy as an integral part of functional literacy, a broad discussion of issues identified by specialists of other branches of pedagogical sciences was noted.
UAV Path Planning for Area Coverage and Energy Consumption in Oil and Gas Exploration Environment

This paper proposes a model for unmanned aerial vehicles (UAV) grid-based coverage path planning, considering coverage completeness and energy consumption in complex environments with multiple obstacles. The work is inspired by the need for more efficient approaches to oil and gas exploration, but other application areas where UAVs can be used to explore unknown environments can also benefit from this work. An energy consumption model is proposed that considers acceleration, deceleration, and turning manoeuvres, as well as the distance to obstacles, to more accurately simulate the UAV’s movement in different environments. Three different environments are modelled: desert, forest, and jungle. The energy-aware coverage path planning algorithm implemented seeks to reduce the energy consumption of a single drone while increasing coverage completeness. The model implementation and experiments were performed in the ROS/Gazebo simulation software. Obtained results show that the algorithm performs very well, with the drone able to manoeuvre itself in a combination of hills, valleys, rugged terrain, and steep topography while balancing coverage and energy consumption.
How to Organize Web3 Participants? Decentralized Autonomous Organizations

This chapter introduces how to organize Web3 participants, which include Web3 educators, investors, developers, and other users who are interested in Web3. Decentralized autonomous organizations (DAOs) are believed to play a significant role in our future Web3-featured society governed in a decentralized way. In this chapter, we first explain the definitions and preliminaries of DAO. Then, we conduct a literature review of the existing studies of DAO published in the recent few years. Through the literature review, we find out that a comprehensive overview of the state-of-the-art studies of DAO is still missing. To fill this gap, we perform such an investigation by identifying and classifying the most valuable proposals and perspectives closely related to the combination of DAO and blockchain technologies. We anticipate that this chapter can help researchers, engineers, and educators acknowledge the cutting-edge development of DAO.
What Financial Crimes Are Hidden in Metaverse? Taxonomy and Countermeasures

The concept of metaverse has sparked widespread attention from the public to major industries. With the rapid development of blockchain and Web3 technologies, the decentralized metaverse ecosystem has attracted a large influx of users and capital. Due to the absence of industry standards and regulatory rules, the Web3-empowered metaverse ecosystem has witnessed a variety of financial crimes, such as scams, code exploits, wash trading, money laundering, and illegal services and shops. To this end, in this chapter, we first summarize and classify the financial security threats on the Web3-empowered metaverse. Then, from the perspective of academic research and government policy, we summarize the current anticrime measurements and technologies in the metaverse.
When Digital Economy Meets Web3: Applications and Challenges

Web3 has attracted a considerable amount of attention due to its unique decentralized characteristics. The digital economy is a driver of high-quality economic development and is currently in a rapid development stage. In digital economy scenarios, the centralized nature of the Internet and other characteristics usually bring about security issues such as infringement and privacy leakage. Therefore, it is necessary to investigate how to use Web3 technologies to solve the pain points encountered in the development of the digital economy. By fully exploring the critical technologies of digital economy and Web3, in this chapter we discuss the aspects of Web3 that should be integrated with the digital economy. Our aim is to find a better entry point to solve the problems in machine learning, finance, and data management, by examining the latest advances of Web3. We hope this chapter will inspire those who are involved in both academia and industry and finally help to build a favorable ecosystem for digital economy in the era of Web3.
How to Enrich Metaverse? Blockchains, AI, and Digital Twin

Metaverse as the latest buzzword has attracted great attention from both industry and academia. Metaverse seamlessly integrates the real world with the virtual world and allows avatars to carry out rich activities including creation, display, entertainment, social networking, and trading. Thus, it is promising to build an exciting digital world and to transform a better physical world through the exploration of the metaverse. In this chapter, we dive into the metaverse by discussing how to enrich metaverse using the technologies of blockchains, artificial intelligence (AI), and digital twin. By investigating the state-of-the-art studies across the metaverse components, digital currencies, AI applications in the virtual world, and digital twin technologies, we present our outlook on building a promising future metaverse. Further exploitation and interdisciplinary research on how to enrich the metaverse will definitely require collaboration from both academia and industries. We wish that this chapter can help researchers, engineers, and educators build an open, fair, and rational future metaverse.
How Economic Systems Enable Metaverse: Value Circulation in Metaverse via Web3

Economic systems play pivotal roles in the metaverse. However, we have not yet found an overview that systematically introduces economic systems for the metaverse. Therefore, we review the state-of-the-art solutions, architectures, and systems related to economic systems. When investigating those state-of-the-art studies, we keep two questions in our mind: (1) what is the framework of economic systems in the context of the metaverse? (2) What activities would economic systems engage in the metaverse? This chapter aims to disclose insights into the economic systems that work for both the current and the future metaverse. To have a clear overview of the economic system framework, we mainly discuss the connections among three fundamental elements in the metaverse, i.e., digital creation, digital assets, and the digital trading market. After that, we elaborate on each fundamental technology of the economic system for metaverse. Those technologies include incentive mechanisms, monetary systems, digital wallets, decentralized finance (DeFi), and cross-platform interoperability for the metaverse. For each technology, we mainly discuss three topics: (a) the rationale of this topic, (b) why the metaverse needs this technology, and (c) how this technology will evolve in metaverse. Through this chapter, we wish readers can better understand what economic systems the metaverse needs and the insights behind the economic activities in metaverse.
An Introduction to Web3 and Metaverse

This chapter briefly explains metaverse, Web3, and their relationship. To better understand Web3 and metaverse, we introduce some crucial concepts, such as blockchains, smart contracts, digital assets and tokens, decentralized applications, and DID. Finally, we also discuss the typical applications related to Web3 and metaverse such as games, virtual world metaverse, social network projects, DAOs, and DeFi.
Enhancing E-commerce with Collaborative Filtering: Challenges and an Overview

Digital marketing is experiencing a rapid growth in the present era, as we all are heading towards a digital world. People have started to become completely involved in digital things, which have slowly made them interact with digital marketing. So here arises a question: is it possible to do marketing in this digital world by knowing the user interest? Yes, if we get to know more about the user behavior towards digital marketing we can easily understand user interest and this is achieved with the help of “Recommendation Systems in machine learning”. All of us are familiar with the term recommendation system. It is a system that filters information in order to predict the rating or interest we have in an item. In this paper, we are going to analyze how the machine learning algorithm helps in the implementation of recommendation systems and here we are choosing collaborative filtering (CF) as a type of recommendation system to study the working of ML algorithms. Also, here we will have an overview about the role of CF in E-commerce and the advantages provided by CF. Lastly in this paper we will be discussing the challenges faced by Collaborative Filtering (CF) and how we can solve these challenges.
<b>MIRACL</b>: A Multilingual Retrieval Dataset Covering 18 Diverse Languages

Abstract MIRACL is a multilingual dataset for ad hoc retrieval across 18 languages that collectively encompass over three billion native speakers around the world. This resource is designed to support monolingual retrieval tasks, where the queries and the corpora are in the same language. In total, we have gathered over 726k high-quality relevance judgments for 78k queries over Wikipedia in these languages, where all annotations have been performed by native speakers hired by our team. MIRACL covers languages that are both typologically close as well as distant from 10 language families and 13 sub-families, associated with varying amounts of publicly available resources. Extensive automatic heuristic verification and manual assessments were performed during the annotation process to control data quality. In total, MIRACL represents an investment of around five person-years of human annotator effort. Our goal is to spur research on improving retrieval across a continuum of languages, thus enhancing information access capabilities for diverse populations around the world, particularly those that have traditionally been underserved. MIRACL is available at http://miracl.ai/.
ASCII Strip-Based Novel Approach of Information Hiding in Frequency Domain

Image steganography is used for hiding secret information as well as for copyright protection. A new approach for information hiding or copyright protection is proposed in this paper based on frequency domain and ASCII strip. A new concept of ASCII strip has been used in this paper. ASCII strip can contain secret information or copyright information. This strip is used as a watermark image in the proposed method. In embedding process, firstly the ASCII strip is generated, then host image is ruptured into sub-bands using DWT. After that, DCT is applied to the chosen sub-band, and ASCII strip is embedded. In extraction process, firstly DWT is used to conquer the watermarked image in four sub-bands, then DCT is performed on chosen sub-band to retrieve the strip and then ASCII strip is converted to hidden text information. Experimental results show that combining DWT-DCT with ASCII strip improves the imperceptibility and robustness. It also gives promising results when it was compared with other similar techniques.
Approaches for Cryptocurrency Price Prediction

This paper describes approaches to forecast Ethereum price based on regression analysis which are based on defined in this research list of factors which may affect price. These parameters can be a part of fundamental and technical analysis. In scope of forecasting the nonlinear regression models are used and compared, in couple with prediction of each factor which is used for regression by NeuralProphet. The models’ outputs were retrieved during experiment. Also, experiment includes models tuning to have more accurate result. The data time window for experiment is one year. This paper does not consider influence of political situation and nature cataclysms on cryptocurrency. Also, this research does not include index of openness of countries finance institute. The type of analyzed crypto is decentralized finance. The Java microbenchmark harness is used to calculate time which is spent for models training. Models’ performance is calculated by evaluation of regression metrics: Root mean square error, Mean absolute error, Mean square error, Explained variance.