Reviewerly/exHarmony huggingface dataset:
Design of Intelligent Operation and Maintenance System for Information Security Based on Web

In order to solve the problems of poor communication, low efficiency and poor service quality in information operation and security management, a Web-based intelligent information security operation and maintenance system is proposed. Adopting SOA (service-oriented architecture) architecture and based on IitLL (information technology infrastructure library) technology, an integrated management system of information security operation and maintenance services is established to meet business and management requirements. The system is mainly composed of data acquisition layer, summary analysis layer and presentation layer, and realizes three management functions: business operation, operation and maintenance services and information security. Through configuration management tracking and intelligent log analysis, the automatic collection, analysis and early warning of information security incidents are realized, which provides a basis for dealing with all kinds of incidents. The application shows that the system realizes the unified scheduling of operation and maintenance human resources, and realizes the unified management and process standardization of information security and operation and maintenance services through real-time monitoring of business systems and infrastructure. (Abstract)
Points Transaction Mechanisms Based on Blockchain Technology

The Loyalty program is an effective incentive measure for companies to maintain individual users. However, the traditional loyalty program has problems such as inability to interact across platforms and lack of credibility. The points alliance is an effective measure to solve the above problems. By formulating the corresponding points exchange agreement within the alliance, the barriers of data interaction between different platforms can be broken. Blockchain technology has the characteristics of decentralization and distribution, which is consistent with the technical needs of the points alliance. Based on the research of decentralized exchange, this paper designs a blockchain-based points trading mechanism, and explains the details of points trading and the implementation of order matching. The mechanism proposed in this paper helps to increase the probability of successful exchange of points, thus increasing the use value of loyalty points.
A Creativity Survey of Deep Clustering Applied to Images

In recent years, deep clustering has shown good performance in image segmentation, image classification and other tasks. Domestic and foreign researchers have conducted in-depth research on the improved clustering model based on deep learning model and its application and proposed a large number of algorithms. However, finding the key research questions and research methods is also crucial to the research of deep clustering. This paper studies and analyses a series of image-based deep clustering algorithms. Firstly, the research status at home and abroad is summarized. Secondly, the research objects, research methods and measurement methods of the target literature are classified. Finally, the future research directions and innovation points are summarized.
Dual-station Passive Tracking Algorithm based on Cubature Kalman Filter Smoothing

In dual-station passive tracking, the Cubature Kalman Filter(CKF) algorithm is widely concerned. To improve the trscking precision of CKF algorithm, a dual-station passive tracking algorithm based on Cubature Kalman Filter smoothing(CKFS) is proposed. In this paper, Cubature Kalman Filter is used as forward-filtering to provide current location results, and smoothing the previous state vector and covariance matrix using the current location results. Therefore an initial value with higher precision is obtained to get more precisely locating results. comparative analysis and validation are made on the tracking performances of CKF algorithm and CKFS algorithm, and the experiment results show that, under the same conditions, the CKFS can more effectively improve the tracking precision than the CKF algorithm. The CKFS algorithm can reduce nearly 10% of the position error and nearly 3% of the velocity error.
Optimized Design Implementation and Research of SM3 Hash Algorithm Based on FPGA

With the further development of computer technology and the advent of big data in the 5G era, the demand for the design implementation of hash algorithms in high-performance hardware is increasing. The commonly used international hash algorithm standards are SHA-1, SHA-2, and SHA-3 series algorithms published by NIST, SM3 algorithm is the first national hash algorithm standard independently developed by China, and the high-performance implementation of the SM3 algorithm is part of China's move to the world. This paper proposes a two-in-one SM3 algorithm IP core implementation framework for the SM3 hash algorithm. For the compressed iterative part of the core operation of the SM3 algorithm, the framework uses a circular, iterative extension approach, adding registers to the combinatorial logic to implement a pipeline design. Moreover, the parallelism of hardware is used to improve the addition circuit and reduce the time delay of the longest critical path. The method uses a Virtex-5 FPGA device as the target device, and the SM3 algorithm achieves a maximum throughput rate of 1939.4 Mbps.
Boosting Interpolation Consistency with Difference and Adaptive Threshold

Interpolation consistency is a consistency regularization technique in semi-supervised learning (SSL), which encourages the model’s prediction of the interpolated samples to be consistent with the interpolation of the sample prediction by perturbing the samples with linear interpolation. However, this method only focus on having similar outputs for the same sample, while ignoring the differences between different samples. At the same time, in order to ensure that this consistent performance works effectively, it is usually necessary to set a fixed confidence threshold, which will cause some difficult samples to be unable to effectively participate in training. To this end, we propose a hybrid contrastive loss that strengthens the model’s attention to the differences between different samples, during consistent training. What’s more, we also design an adaptive threshold module, which sets a dynamic threshold by evaluating the current learning effect of each class, thus considering both easy samples and hard samples. Compared with existing semi-supervised methods, our method achieves state-of-the-art results on several standard datasets, especially on CIFAR-10 with 1000 labels and 4000 labels, which outperforms the current best results by 6.23% and 15.96% respectively.
Interference Identification Method for UAV Based on Improved Support Vector Machine

To improve the interference suppression ability of the UAV data link system, it is necessary to accurately identify various interference signals in complex electromagnetic environments. Support vector machine (SVM) is widely used in the field of interference identification, but its classification performance is greatly affected by parameter selection. Therefore, in order to solve the problem of low interference identification accuracy in the traditional SVM algorithm, an improved SVM algorithm based on Sparrow Search Algorithm (SSA) is proposed. The method optimizes the penalty factor and kernel function parameters of the SVM algorithm through SSA, which improves the classification accuracy and efficiency of the algorithm. Simulation results show that the identification performance of the proposed SSA-SVM algorithm is better than the traditional SVM and random forest algorithms. When the interference-to-noise ratio is 0dB, six kinds of UAV interference signals can be accurately identified. In addition, the identification accuracy is also significantly improved at low signal-to-noise ratios. For compound interference, the SSA-SVM algorithm also shows good identification performance. When the interference-to-noise ratio is greater than 2dB, 15 types of compound interference signals can be accurately identified.
Application of an Efficient Blockchain PBFT Optimization Algorithm for Environmental Monitoring

Blockchain technology has changed the centralized management model of the existing environmental monitoring system by virtue of its decentralized and non-tampering characteristics. However, with the increase in the amount of monitoring data, the environmental monitoring blockchain system is faced with a large number of computing and communication overhead caused by data consensus, which affects the normal collection, calculation, and upload of environmental monitoring data. Aiming at the above problems, this paper proposes a credit grouping Byzantine fault-tolerant(CGBFT) mechanism which is suitable for environmental monitoring blockchain scenarios. First, the reliability analysis is performed based on the authenticity and integrity of the data transmitted by the monitoring nodes to obtain the credit value of each node as the basis for selecting the master node. Second, the consistency protocol of PBFT is optimized for the characteristics of the environmental monitoring blockchain system to meet the data consensus strategy of high throughput and reliable fault tolerance. The scheme analysis shows that the improved PBFT consensus mechanism can effectively solve the problems of high communication overhead and low efficiency in the system performance.
Moving Target Detection Based On DLBSP For Complex Background Surveillance Video

The robustness and efficiency of the most of existing target detection methods in complex background surveillance video could hardly meet the application requirements. To this, a novel background subtraction method based on Differential Local Binary Similarity Pattern (DLBSP) is proposed in this paper. Subsequently, a bi-feature model is constructed to enhance sample richness based on codebook construction combining texture and color. Furthermore, a hybrid modeling strategy, which could quickly construct a relatively ideal model with low maintenance cost is put forward to improve further the background modeling algorithm under complex background video with camera shake and background mutations. The comparative experiments on select seven categories (including 43 video sequences) of dataset CDnet2014 could demonstrate the superiority of the proposed method. It obtains better average FM detection results under complex background video containing dynamic backgrounds, sudden changes in lighting, and camera shakes.
Research on sentiment classification algorithm for Corona Virus Disease 2019 Weibo text

Since the outbreak of Corona Virus Disease 2019, it has had a significant impact on people’s lives. In order to help the government grasp the social opinion and do more scientific and practical propaganda and public opinion guidance for prevention and control, and to fully reflect people’s attitude toward the epidemic and provide data support for government departments to release epidemic prevention measures. This paper uses Corona Virus Disease 2019-related Weibo comments as the research object and analyzes their sentiment using deep learning algorithms. The number of characters in Weibo comments is usually less than 140, which belongs to the category of short texts. Due to the use of few words, random user language, and irregular grammar, these texts have poor performance in text separation and word vector expression, adversely affecting sentiment classification. In order to solve this problem, this paper constructs the BERT-DPCNN model for sentiment analysis of epidemic short texts, which can not only extract the sentence-level text dependencies but also effectively avoid the problem of gradient disappearance of deep neural networks. The experiments show that the BERT-DPCNN model has the best effect and is of great value for the sentiment classification of short epidemic text.
On Automatic Scoring Algorithm for Subjective Questions based on LSA and Dependency Parsing

Aiming at the problems of lack of sample data and unknown words often seen in the development of automatic scoring system for subjective questions, we propose an automatic scoring algorithm based on latent semantic analysis and dependency parsing for subjective questions. First, the questions, standard answers and candidates' answers are used as corpus for latent semantic analysis training to generate document vectors and word vectors. Then, the standard answers and candidates' answers are divided into sentences, and the sentences are analysed by dependency parsing. Then, the document vectors, word vectors and dependency parsing results are used as the input of the automatic scoring algorithm, three automatic scoring schemes are proposed according to the number of sentences and their differences between the standard answer and the candidates' answer. The experimental results show that this algorithm has obvious advantages and potential value compared with the control method.
Preliminary Discussion of Blockchain Application in Satellite Remote Sensing Data Share

Satellite remote sensing data is high-valuable in scientific research and commercial application. China is a world-leader in satellite remote sensing, however the data resources are highly concentrated in few institutions and the data distribution is centralized and separated. As blockchain applies more widely, it offers a new way for satellite remote sensing data sharing. This paper analyzes the feasibility of combining blockchain and satellite remote sensing data sharing, tying to finds out the binding point by analyzing the satellite remote sensing data structure. Comparing satellite remote sensing data entity and derived data, this article proposes a preliminary design and architecture of blockchain to share satellite remote sensing metadata. At last an analysis of advantages and challenges is given.
LSMM: A Life-cycle Security Management Mechanism for Secure Image Sharing

During the storing, transferring, processing, and exchanging phases of the full life-cycle of secure image sharing, there may exist security issues such as information leakage, infringement and abuse. To address those problems, a life-cycle security management mechanism is proposed. By utilizing the Commutative Encryption and Watermarking (CEW), the mechanism ensures that the ownership and confidentiality of one image can hardly be tampered with and compromised. Besides, with the help of the blockchain, the mechanism is enabled to record any changes during the whole process of the image sharing, which makes copyright traceability possible and thus reduces the occurrence of infringement and abuse. The experiment result shows that, in our mechanism, the quality of plaintext image with watermark obtained by decryption is high, and the invisibility of watermark is preserved. Also, the efficient analysis and experimenting of the mechanism exhibits that it is feasible in practice.
Research on aircraft platform environment data preprocessing process

The preprocessing of aircraft random vibration samples is critical to the formulation of environmental test conditions. This paper briefly reviews the phenomenon of singularity and trend term in random vibration sample data firstly. This paper describes the software system we developed and introduces the data storage and acquisition process based on Hadoop in detail secondly. Then the paper We summarize the relevant algorithms for identifying data outliers, including MATLAB-based visual inspection of data, probability density curves, narrowband spectral density curves, and algorithms for removing outliers, including least squares, intermittent noise outlier removal, IIR. Finally, the paper points out that data analysis process to generate spectrum, data conclude process, test conditions are for future research work.
Research on Event Extraction Model Based on Semantic Features of Chinese Words

Event Extraction (EE) is an important task in Natural Language Understanding (NLU). As the complexity of Chinese structure, Chinese EE is more difficult than English EE. According to the characteristics of Chinese, this paper designed a Semantic-GRU (Sem-GRU) model, which integrates Chinese word context semantics, Chinese word glyph semantics and Chinese word structure semantics. And this paper uses the model for Chinese Event Trigger Extraction (ETE) task. The experiment is compared in two tasks: ETE and Named Entity Recognition (NER). In ETE, the paper uses ACE 2005 Chinese event dataset to compare the existing research, the effect reaches 75.8 %. In NER, the paper uses MSRA dataset, which reaches 90.3 %, better than other models.
Do Community Members Harmonize in Face-to-Face Classrooms: Integrated Literacy Activities Implementation to Young Students

In 21 st -century learning, integrating literacies in classroom activities carries beneficial impacts on students' needs.The present study aims to investigate the implementation of integrated literacies in the teaching and learning process through face-to-face meetings involving four pre-service teachers, an experienced teacher, and around thirty young students in one of the literacy communities in West Java, Indonesia.The descriptive case study was well-employed to collect the data by observing pedagogical practices among teachers and students.The result then indicates that integrated activities were implemented in one and half hour duration in every meeting, they engaged actively during the classroom process, and alternative assessments were used to measure students' understanding and contextualize their knowledge.To sum up, integrated literacy activities are recommended to be applied in supporting emancipated learning to achieve Pancasila learners' profile which is sparklingly endorsed by the Indonesian government.Besides, some capacity improvements and consistency are definitely needed to be acquired through professional development, focus group discussion, and pedagogical preparation to realize the great process of its implementation.
Compare Linear regression, Decision Tree Regressor, and Random Forest Regressor based on python, a restaurant company on Kaggle as a case

The fastest-developed computer language around the world is python because it is easy to use. Most of the linear regression methods currently used for traffic prediction cannot explain the accuracy differences between methods. In this study, the idea of constructing a regression model, decision tree regressor, and random forest regressor is described by analyzing the sales volume, region, and time of a take-out company, and the accuracy difference between the three different models with the same data is compared. It is concluded that the random forest regression volume has the highest accuracy among the three, and the results are obtained by bringing them into the original data for experiments. Although the linear regression model improves the scientific and rational nature of logistics forecasting. It provides a decision basis for enterprises to forecast sales volume. However, if only the linear regression model is used for forecasting, it is still not accurate enough. It needs to be combined with other methods for experiments to further improve accuracy, scientificity, and rationality. In the paper, the basic knowledge of statistics is used to create three different linear regressions through python and find which one is the most accurate. R2 score, MSE, and RMSE are explained. Based on the study, when firms do the prediction of their business or any other projects, if the data set have the linear regression relationship, they could choose the Random Forest through python directly as it is the most precise among them.
A comparative evaluation of 3D geometries of scenes estimated using factor graph based disparity estimation algorithms

Passive stereo vision systems are useful for estimating 3D geometries from digital images similar to the human biological system. In general, two cameras are situated at a known distance from the object and simultaneously capture images of the same scene from different views. This paper presents a comparative evaluation of 3D geometries of scenes estimated by three disparity estimation algorithms, namely the hybrid stereo matching algorithm (HCS), factor graph-based stereo matching algorithm (FGS), and a multi-resolution FGS algorithm (MR-FGS). Comparative studies were conducted using our stereo imaging system as well as hand-held, consumer-market digital cameras and camera phones of a variety of makes/models. Based on our experimental results, the factor graph algorithm (FGS) and multi-resolution factor graph algorithm (MR-FGS) result in a higher level of 3D reconstruction accuracy than the HCS algorithm. When compared with the FGS algorithm, MR-FGS provides a significant improvement in the disparity contrast along the depth boundaries and minimal depth discontinuities.
Digital Intelligence Operation Center of Tongwei: Research on the Value Creation Path of Digital Technology Application

In the digital era, new technologies such as big data and artificial intelligence are rapidly emerging. The speed of digital transformation in enterprises is also accelerating. Tongwei listed company has made an innovative exploration in the field of intelligent finance; and built a Digital Intelligence Operation Center based on artificial intelligence and Robotic Process Automation. This paper introduces the framework, digital technology and business application scenarios of Tongwei’s new financial sharing center, discussing its innovation points and its future development path. This research expands the path of building intelligent finance workflows for enterprises and provides new ideas for the digital transformation of companies. The results of this paper have some reference value for corporate business process optimization and structural reorganization.
Innovation and Development in The Metaverse under The Digital Economy — Takes Tencent as An Example

n the era of digital empowerment, technology has changed dramatically, new digital technologies have exploded. Big data, blockchain, artificial intelligence and other technologies have begun to occupy a large part of technological development, focusing on customers and meeting people's needs while constantly presenting new forms of expression, metaversity, blockchain, AR, VR, 5G technology gradually into people's vision. More and more countries and enterprises have begun to enter the meta-universe field, especially game companies such as Tencent, and have made many breakthroughs in the meta-universe field in recent years. The metaverse will have more possibilities in the combination of innovation in various fields, and the future metaverse may change people's lifestyles, bring technological innovation, or bring disaster to mankind. Therefore, breaking through technical limitations and improving the management system have become urgent problems that the metaverse needs to solve.