Reviewerly/exHarmony huggingface dataset:
Multi-dictionary learning with superpixel-based clustering for SAR Image despeckling

In this paper, a despeckling method is proposed to reduce the speckle noise from synthetic aperture radar images, which is based on the sparse model. First, we use logarithmic function to transform speckle noise to additive noise in order to simple the despeckling process, while the image is transformed to logarithmic domain. Then, simple linear iterative clustering, which has the merits of quick computation speed and high accuracy among superpixel algorithms, is employed to segment the log-intensity image. Considering the difference of texture and edge, the transformed image is distinguished into different regions by using fuzzy c-means to analyze superpixels. Final, the output image is obtained by multi-dictionary learning, which homogeneous regions of original image are represented by corresponding dictionary. From simulated and actual experiments, the proposed method can provide the better performance than other competing methods.
An Automatic Topic-oriented Structured Text Extraction Method based on CRF and Deep Learning

Automatic extraction of text information plays an important role in machine translation, knowledge mapping and other fields. In recent years, with the rapid development of computer technology and the popularization of Internet application, the resources acquired by people through the Internet show explosive growth. Facing the massive information resources, how to extract the required information quickly and effectively and convert it into structured data has become a hot topic of current research. Based on this, this paper proposes a automatic topic-oriented text extraction method combining BiLSTM and CRF models. This method firstly establishes the text extraction topic, then carries on the automatic entity recognition to the data related topic, finally forms the standard structured data, thereby realizes the unstructured text data to the specific structure of the information block, which lays the foundation for knowledge mining. Taking the data collected in ACL 2018 Chinese NER as the test data, the precision of our algorithm is 95.74%. Compared with the traditional neural network method, our text information extraction method can effectively identify more entity information in the text data, and improve its effect in practical application.
A Proposed Academic Chatbot System using NLP Techniques

A chatbot is a computer program that can converse through a textual or auditory user interface. A chatbot is capable of answering academic and non-academic queries of a user visiting a college's website. A chatbot eases the work of university staff. A chatbot is capable of answering the queries generated instantaneously anytime without any involvement of human activity. A user can gain access to required relevant information efficiently with the help of a chatbot. It's almost not possible for the user to differ between a chatbot and a user.
Intelligent Algorithms of English Semantic Analysis Based on Deep Learning Technology

The grammatical relationship between words often plays an important role in word understanding and word processing, and is widely used in fields such as information retrieval, word processing, dictionaries, and understanding construction. This article aims to study an intelligent algorithm for English translation analysis based on deep learning technology. It highlights the research background and the importance of semantic analysis, and explains the research status. To show the relevant science and technology used, the word checking algorithm studied in this article uses the two word embedding methods to process the data in the training corpus. Then it will be used as the input of the long and short-term memory neural network. Finally, the algorithm is realized by training the neural network to obtain the language model. The trained language model is used to predict the output of the word at a specific location and compare it with the original word and feed it back to the user. In this paper, the average F-value of the semantic analysis of English composition under 5 themes is 95.00%.
An Improved Monkey Swarm Algorithm

Monkey swarm algorithm is generated by simulating the monkey climbing process. The update process is easy to operate and suitable for solving complex practical problems. In order to further enhance its optimization ability, this paper presents an modified monkey swarm algorithm. Using the chaos variables generates the initial feasible solution of the monkey algorithm initially, the decreasing factor is then taken as the climbing step in the climbing process to improve the performance of monkey swarm algorithm. Finally, in the simulation experiment, the solution results of typical functions are given to illustrate the feasibility of the proposed improved method.
Problematic Commodity Traceability System based on Fabric's Embedding National Cryptographic Algorithm

With the rapid development of economy, more and more free trade areas appear in the market, but in the process of commodity trading, there is a situation that the problem goods flow back to the market, and consequently, the public is more concerned about the subsequent processing flow of the problem goods. In response to the public demand for problematic goods traceability, a problematic goods traceability system based on Fabric embedded with national cryptographic algorithm is designed. We use blockchain technology to provide evidence for the processing flow of goods and embeds the national cryptographic algorithm into the fabric platform, uses Kafka-Zookeeper algorithm to reduce the coalition consensus time, and guarantees the generation and validity of transactions based on smart contracts and endorsements, and uses blockchain and Interplanetary File System as the storage engine for business data requirements. The registration time for the processing flow of the core functions of the system is 0.61 seconds, which meets the expected requirements of the system.
Bangla Image Captioning With Bidirectional GRU &amp; Attention Mechanism

Image captioning is a complex study subject in which natural language subtitles/captions are generated for pictures relying upon what is shown in them. The captioning models have demonstrated impressive results in recent times and models with attention mechanisms are becoming more popular as a result of improved performance. However, the use of attention strategies for Bengali captioning challenges has received little focus. Since no study on employing attention approaches using the Bangladeshi dataset has been done yet, this paper aims at building an attention-based captioning model for the Bengali language and Bangladesh’s geographic regions. With a caption-model composed of a GRU (gated recurrent network) and Bahdanau-attention, we used four CNN architectures: VGG19, InceptionV3, Xception, and ResNet50, for accurate features. The models were prepared by a public dataset named “BanglaLekhaImageCaptions” incorporating 9,154 pictures with Bangladeshi geological and cultural settings and Bengali annotations. Both qualitative and quantitative evaluations were used to assess the models. Experimental findings reveal that the models beat the existing works’ results and that when the encoder is Xception, the accuracy is adequate with a BLEU-I score of 87.18.
PBIM: A Participant Based Incentive Mechanism for Consortium Chain

In the existing public chain environment, most incentive mechanisms are for miners, who can obtain rewards after the packaged blocks are put on the blockchain. Compared with the public chain, the consortium chain network does not have the concept of coin rewards. The number of consensus nodes in the consortium chain network is limited, and the consortium chain forces nodes to provide services when they are admitted. Not only that, the transactions in the consortium chain are more complicated. Therefore, the original incentive mechanism based on miners and transaction gas is no longer applicable to the consortium chain environment. It has become a trend to consider the incentive model from the perspective of the participants. This paper proposes a participant-based incentive mechanism for the consortium chain, which is aimed at all participants. Based on the complexity of transactions, this paper proposes a multi- dimensional transaction ranking strategy to motivate participants. This strategy ensures that active participants have priority on- chain rights and inactive participants can also meet their needs, and solve the problems of malicious attack, transaction starvation and so on. Experiments show that our incentive mechanism can adapt to multiple scenarios and provide an active environment for consortium chain.
Research on Head and Shoulders Detection Algorithm in Complex Scene Based on YOLOv5

For subway stations and other places with dense traffic, the effect of pedestrian detection is easily affected by occlusion. At the same time, the head and shoulder detection has strong anti occlusion ability and low computational requirements. In view of the existing problems, this paper proposes a head and shoulder detection algorithm based on yolov5 in complex scenes, and designs and adds a deformable convolution network and attention mechanism module in the feature extraction network to improve the ability of feature extraction. The experiment is designed to train and test the pedestrian head and shoulder data set in the subway complex environment. The experimental results show that the accuracy of pedestrian head and shoulder detection for small target is improved in subway complex scene.
Integration Project Based on Computer Information Technology Management System

Deepening project management research will better promote the development and improvement of project risk management theory and the computer system integration of the entire project management theory, and accelerate the development of scientific and refined computer system project management integration in my country. The purpose of this article is to analyze the task of integrating management systems based on computer information technology. Based on the research project of M company's computer information management system integration, the risk factors in the project implementation process were carefully evaluated and analyzed. Based on the theory of project risk management, combined with project risk characteristics and characteristics and key points, the Borda classification method and risk table method are used for quantitative risk analysis. The results show that among the seven risk factors, the required risk factors receive the most attention It is the risk factor that changes the scope of the project, and it is also the most difficult risk factor to solve in project risk management.
Pose Estimation of Circular Workpieces With Occlusion Based on GAN-Support Ellipse Detection in Manufacturing

In automated assembly tasks, ellipse detection is usually applied in the vision-based pose estimation of circular workpieces. However, the existing ellipse detection methods cannot effectively solve severe visual occlusion in cluttered environments. To address this problem, this paper proposes a Generative Adversarial Networks (GAN)-supported ellipse detection method against the occlusion condition. The trained GAN network can restore the occluded image of workpieces, so that the elliptical features can be detected robustly. In the experiments with different degrees of occlusion, the ellipse detection rate is above 90%, which shows better performance than other existing methods.
RCANet: A Rows and Columns Aggregated Network for Table Structure Recognition

Most existing table structure recognition methods can be classified into two major categories: detecting table borders methods and detecting table rows and columns methods. The method of detecting the table borders can produce the imbalance between positive and negative samples, because the number of pixels in the table borders is very small. Although the method of detecting the rows and columns of the table avoids this imbalance, some studies simplify the prediction of rows and columns into column-by-column and row-by-row prediction, which creates a problem with large error tolerance. To solve this problem, two modules are proposed, called Rows Aggregated (RA) module and Columns Aggregated (CA) module. Firstly, the method of feature slicing and tiling is used to make approximate prediction for the rows and columns that solves the problem of large error tolerance. Secondly, the row and column information is further retrieved by calculating the attention maps of channels. Finally, we use RA and CA to build a semantic segmentation network, which is called Rows and Columns Aggregated Network (RCANet), to complete the rows segmentation and columns segmentation. We generate rows and columns masks on ICDAR2013 dataset and evaluate the model. Experiments show that the proposed model has better performance than the segmentation model based on detection table rows and columns method, and its average precision, recall and F1 value are 2.08%, 3.21% and 2.45% higher respectively.
A Knowledge Graph-Based Abstractive Model Integrating Semantic and Structural Information for Summarizing Chinese Meetings

With the rapid increase of users, online meeting platforms have accumulated massive meeting transcripts. However, it is still a challenge for users to quickly master the chief information and manage the meetings, despite there are already some useful text summarization models. In this paper, a Knowledge Graph-based Meeting Summarization Framework is proposed to tackle this challenge. First, a two-layers meeting domain Knowledge Graph is developed to integrate more information of meetings. Based on which, an encoder-decoder architecture is utilized to summarize meetings. For encoding meetings, a structural-level and semantic-level embedding strategy is considered, concretely, the Knowledge Graph is embedded to obtain the structural information, an interaction intention recognition model and a two-level transformer mechanism are devised to get the semantic information. Finally, the structural information and semantic information are combined and fed into the decoding network to generate meeting summaries. Extensive experiments on the Chinese meeting dataset show that our summarization framework outperforms other state-of-the-art models.
Text Detection Based on Bidirectional Feature Fusion and SA Attention Mechanism

Most of the scene text detection methods can not balance accuracy and speed, so these text detection methods are difficult to apply to the scenarios with real-time requirements. We propose a text detection network that attempts to improve text detection&#x2019;s accuracy while guaranteeing the speed of text detection. Firstly, we use ResNet with SA attention mechanism as a backbone network for extracting image features. Then, different feature layers are fused by a bidirectional feature fusion mechanism, and a bilinear interpolation method is adopted for upsampling to reduce the feature loss in the feature fusion process. Finally, the post-processing part uses a differentiable binarization module to ensure the generalization and real-time performance of the model. The model has good performance on public dataset, achieving 41 of FPS and 83.2% of F-measure on the ICDAR2015, which verify the model has both good real-time performance and high accuracy.
T-Tracer

In a data sharing network where parties lack mutual trust, credible traitor tracing is indispensable to reliable data delivery. Currently, the main obstacles for traitor tracing are the deficiency of content-independent data watermarking scheme and the untrustworthiness of third parties. In view of this, we propose the T-Tracer --- an encoding-based watermarking scheme. With T-Tracer, each party of data delivery embeds its digital fingerprint into the byte sequence of the shared data by generating a tailor-made symbol mapping table, whereby the watermarking can handle any type of data. A blockchain network overlaying all data sharing participants serves as a trusted third party to ensure the credibility of T-Tracer. By applying T-Tracer, anyone in the network can recognize the culprits by identifying their fingerprints from the data objects that appear as evidence of data delivery repudiation. The evaluation results show that T-Tracer is effective and practical in improving the credibility of traitor tracing.
Providing Document Delivery/Interlibrary Loan Services from Within a Pandemic: Filling in the Gaps

On March 11, 2020, the World Health Organization (WHO) characterized the alarming spread and severity of the coronavirus disease 2019 (COVID-19) as a pandemic. Responding to university directives, as well as professional association recommendations, many academic libraries not only suspended in-person services but also barred access to physical collections for interlibrary loan purposes. Such decisions had a significant impact on interlibrary loan (ILL) operations around the world. This paper describes how the University of Louisville's Kornhauser Health Sciences Library (KHSL) not only maintained a continuity of ILL services but also expanded those services worldwide.
DFIL: an Efficient Web Service Prediction Method based on Deep Feature Interactive Learning

As a representative of nonfunctional features, the Quality-of-Service (QoS) plays an important role in recommending the best service to users. To obtain the QoS value of web services, researchers have proposed many web service QoS prediction methods. However, most existing methods, e.g., the collaborative filtering method, leverage the historical user service invocation information to predict the QoS value, and only consider the QoS information of similar users or services, and ignore the attributes and characteristics of users and services. In this paper, we propose a Deep Feature Interaction Learning method, called DFIL for short, for web service QoS prediction. Specifically, (1) DFIL constructs a feature extraction method to effectively obtain the features of users and services. (2) DFIL trains a novel neural network for QoS prediction, which can accurately find the potential relationship between users and services based on the feature information, and provide good web service recommendation.
Dynamic synopsis and storage algorithm based on infrared surveillance video

Infrared surveillance video is difficult to watch quickly and store efficiently, a surveillance video synopsis and storage algorithm is proposed based on dynamic. On the basis of extracting moving targets, the constraints of time and space is broken to build an energy functional based on filling density to quickly display the video content on the premise of ensuring the monitoring video information. The Tube structure is formed by the moving target information, and the mapping relationship between the original video and the stored video is established. Image similarity from time and space dimensions is fully utilized to realize the storage of surveillance video. The space ratio between the stored information and the original video is less than 0.2.
Applications of the Decentralized Finance (DeFi) on the Ethereum

With the popularity of blockchain technology, more and more financial fields are ready to apply or have applied the blockchain technology to achieve technological upgrade. The Ether is the most popular platform which we can establish the smart contract to apply the blockchain technology. Decentralized financial DeFi is an important part of realizing the blockchain technology upgrade in the financial application. Based on blockchain technology, this paper introduces the basic working principle of blockchain and Ethernet. Then explains how it provides the technical foundation for decentralized finance as a technical innovation. It then proposes the advantages that DeFi apply in the financial sector and how it can bring benefits to individuals or enterprises based on the features of blockchain such as decentralization, no personal tampering and automatic information uploading.
TTP-Aided Secure Computation using Secret Sharing With Only One Computing Server

Secure computation methods include methods that use homomorphic encryption (HE) and those that use secret sharing (SS). Secure computation based on HE can be realized using one server, and the computation process can be made public if the encryption key is kept secure. However, HE generally requires a substantial computation cost. In contrast, SS has the advantage of low computation cost, allowing for high-speed processing; however, all servers must be managed independently. And the process of k or more servers cannot be disclosed. In particular, secret input will be leaked when the same organization manages multiple servers to implement SS. Therefore, a complex model where each server is independently managed is required. Iwamura et al. proposed a secure computation that is information-theoretic secure in n≥k instead of n≥2k−1 by assuming a trusted third party (TTP). In this paper, by making more effective use of a TTP, we demonstrate that secure computation based on SS is possible with only one computing server. Moreover, we show that the entire computation process can be disclosed if the key is managed safely. We realize a method that solves the disadvantages of conventional approaches with a faster computation than those methods.
PG-Pass:Targeted Online Password Guessing Model based on Pointer Generator Network

Existing targeted online password guessing models were based on Probabilistic Context-Free Grammars (PCFG), having inherent disadvantages that guessing structures were always the same for different users. This problem would lead to poor guessing efficiency. In order to solve this problem, we propose a targeted online password guessing model (PG-Pass) composed of the pointer generator network. It could automatically learn the impact of personal information on passwords and guess the target user’s password more accurately. Through extensive experiments, we obtain the optimal parameters. The results show that with only Personally Identifiable Information (PII), the guessing success rate of the PG-Pass model could reach 19.49% in guessing once, which is ten times higher than TarGuess-I. When guessing 100 times, the guessing success rate could be 41.07%, proving the effectiveness of the proposed model in this paper.
Positioning of Basketball Robot Target Recognition System Based on Data Mining Algorithm

Robot basketball is an emerging cross-discipline, involving robotics, intelligent control and image processing, computer vision and other fields, but as the amount of data collected by the system increases sharply, this information comes from different times and uses different expressions As the amount of information continues to increase, its effectiveness is limited. The purpose of this article is to study the positioning of the basketball robot&#x2019;s target recognition system based on data mining algorithms. Based on the research of data mining, support vector machines and related technologies, this paper establishes an SVM method to realize basketball robot target recognition. Experimental research shows that, according to the measurement accuracy characteristics of the binocular vision system, when the object distance L=500mm~600mm is used, the relative error of the measurement is small and the system accuracy is high.