Reviewerly/exHarmony huggingface dataset:
Research on Web Log Data Mining Technology Based on Optimized Clustering Analysis Algorithm

With the continuous development and progress of network and information technology, facing the rapid growth of Web service resources, how to obtain the knowledge people need through existing server resources? Web mining technology can realize the mining and discovery of new knowledge from fixed resources. Log mining can discover customer interests and hobbies through Internet users&#x0027; access behavior, so that the website can provide more humane services. A comprehensive understanding of log mining related technologies is of great help to achieve user clustering. The main analysis of the Web log user clustering in this paper is to establish a user session sequence matrix based on the data preprocessing of the log file, and then perform cluster analysis on it, and finally perform related applications.
Improvement of the system for assessing academic performance of Bachelor’s degree students as a method for increasing the quality of higher education

The subject of this research is the system for assessing academic performance of Bachelor&amp;rsquo;s degree students. The goal is to improve this system namely increase the accuracy of the results, motivation and engagement of the participants in the performance review. The article employs the following empirical methods: research on this topic, modern trends in the system for assessing academic performance, development of the system of academic ratings, complication the assessment system due to inclusion of the results of social, cultural and sports activity of students in the overall rating. The central methods of obtaining information include observation and recitation of Bachelor&amp;rsquo;s degree students of the Russian university. Recommendations are formulated in accordance with the acquired data. The novelty lies in consideration of the system for assessing academic performance as the factor that determines the quality of education, as well as in development of recommendations for improving the assessment system. The result of the conducted research consists in recommendations on the form and content of performance review aimed at increasing the accuracy of the assessment system. The advantages of revision of the assessment system for beneficiaries (universities, students, and employers) are substantiated. The author analyzes the effectiveness of the strategies &amp;ldquo;assessment of academic performance&amp;rdquo; and &amp;ldquo;assessment for academic performance&amp;rdquo;, as well as the role of intermediary and final performance review for various beneficiaries. The conclusion is made on the need to continue the research of the assessment system and methods for its improvement, taking into account the new educational trends.
Fast Low-light Image Enhancement Algorithm Based on Fusion

Low-light images generally exist in various situations, and the existing low-light image enhancement algorithms are poor in generality and have high computational complexity, and the comfort of the enhanced image is not satisfactory. This paper proposes a fast low-light image enhancement algorithm based on fusion. First, transform the passing space to HSV space to extract the V component. Then, according to the influence of Weber-Fechner's law on human visual senses, and effectively combining the characteristics of low-light images, a global adaptive brightness transformation function is constructed, and this function generates the first enhanced image. Then use CLAHE to increase the local contrast and generate a second enhanced image. Finally, an image fusion strategy is adopted to extract image details to fuse the image effectively. The proposed algorithm is fast and straightforward, can effectively improve the quality of low-light images, and better preserve its naturalness. Experimental results show that the proposed algorithm has achieved good results both subjectively and objectively.
Management of the Export of Educational Services in a Modern University

The export of educational services today is the most important indicator of the competitiveness of the university, the degree of its integration into the global educational space. The management of this area is largely focused on the search for new forms and types of interuniversity interaction, which makes it possible to increase the efficiency of educational activities. The main forms of the export of educational services include pre-university training of foreign citizens training in basic educational programs, postgraduate education programs, licensed advanced training and professional retraining programs, short-term educational programs. The last form of the export of educational services includes various types of internships, the potential of which is not sufficiently revealed and used in modern education. Our article is devoted to this problem. For a foreign student coming to Russia, the purpose of the internship is to improve the knowledge of the Russian language in a specific subject area. This determines the modular nature of the formation of internships, on the one hand, and their practical focus, on the other. The principle of modularity is optimal for the implementation of this form of exported educational services, since it allows to develop an internship program of any degree of complexity in accordance with the requests of the “customer” – through the use of ready-made teaching units from the bank of modular materials. To be successful, the marketing strategies of educational organizations must be adapted to the specific needs of the partner countries and be actively promoted in the international educational market.
Text Stance Detection Based on Deep Learning

Stance analysis refers to the usage of natural language processing and data mining technology to determine the stance tendency of a specific target topic in text. The current stance detection research faces the problem of the relationship between the topic target information and the stance text information being not fully tapped, which affects the text stance analysis task performance in social media. In view of this, based on the existing deep learning framework, combined with the topic target information in stance analysis, this study proposes a stance analysis model based on a convolutional neural network under the independent encoding of the topic target information and the condition encoding of the topic target information. The SemEval2016 English Dataset and the NLPCC2016 Chinese dataset are used herein separately to conduct the experiments. The experimental results show that the model is effective in the stance detection task of a specific topic target.
image construction of the City Yiwu in overseas media

Every year, hundreds of thousands of businessmen from all over the world flood into Yiwu, a small commodity town known as “The world's largest small commodity trading market.” As Yiwu’s slogan says, “ Buy from the world, and sell to the world.” This small town has been influencing the world using its own style. This paper extracted 1413 sentences from the Now Corpus using the keyword "Yiwu." We divided data into two time periods: 2010-2015 and 2016-2021, and made an in-depth analysis of Yiwu's overseas image with the change of timeline.The research results showed that with the gradual maturity of projects such as "China-Europe Freight Train" and "Yiwu Fair,"Yiwu's oversea image has become more positive during these 12 years. Moreover, Yiwu has gradually become a modern city integrating with the world instead of a world-famous “Christmas factory." Importantly, residents of Yiwu are influencing the world with their unique wisdom.
Blockchain Technology for Information Sharing and Coordination to Mitigate Bullwhip Effect in Service Supply Chains

Supply chain management experiences inefficiencies due to several reasons, such as the lack of information sharing and coordination among supply chain participants. Moreover, these participants may not share their information with other parties in the supply chain due to trust issues and considering such information as sensitive asset. This behavior may hinder the supply chain efficiency and motivate the occurrence of bullwhip effect (BWE). This phenomenon has been intensively investigated in manufacturing industries. In addition, information sharing has been considered the main remedy to eliminate BWE in literature. However, few attempts examined the effect of information sharing in reducing this phenomenon in service supply chain (SSC). Digitalization and computerization have the potential to convert and reshape the supply chains in all kind of businesses and improve the coordination among supply chain partners by exchanging real-time information. Blockchain as a disruptive technology has many distinct features like disintermediation and decentralization that provide integrity, visibility and security for the supply chain. To bridge this gap, this paper aims at proposing a blockchain architecture to mitigate BWE in SSC by improving end-to-end visibility among supply chain partners through sharing backlog information. The proposed supply chain-based blockchain enables SSC partners to share securely and transparent backlog information mitigating thus BWE.
Uncertainty-Aware Pseudo-Labeling for Spoken Language Assessment

Automatic spoken language assessment has gained popularity in computer-assisted language learning (CALL). Normally building these systems relies heavily on labor-intensive human-labeled data. In this paper, we adopt the pseudo-labeling (PL) method to make better use of the massive amount of unlabeled data. Traditional pseudo-labeling mechanism selects predictions of high confidence from the unlabeled pool to augment the labeled data, which may be over-confident and less informative. To select more informative, less noisy data, we optimize the pseudo-labeling process by modeling both data and knowledge uncertainty. Based on a self-adaptive learning mechanism, we sequentially and iteratively select unlabeled samples to augment the training sets during the multi-step self-training procedure until the stop criterion is satisfied. Experimental results based on data from the spoken English tests demonstrate superior performance compared to the baselines and other semi-supervised learning (SSL) methods in Pearson correlation coefficient (PCC) and accuracy.
Towards improving the robustness of sequential labeling models against typographical adversarial examples using triplet loss

Abstract Many fundamental tasks in natural language processing (NLP) such as part-of-speech tagging, text chunking, and named-entity recognition can be formulated as sequence labeling problems. Although neural sequence labeling models have shown excellent results on standard test sets, they are very brittle when presented with misspelled texts. In this paper, we introduce an adversarial training framework that enhances the robustness against typographical adversarial examples. We evaluate the robustness of sequence labeling models with an adversarial evaluation scheme that includes typographical adversarial examples. We generate two types of adversarial examples without access (black-box) or with full access (white-box) to the target model’s parameters. We conducted a series of extensive experiments on three languages (English, Thai, and German) across three sequence labeling tasks. Experiments show that the proposed adversarial training framework provides better resistance against adversarial examples on all tasks. We found that we can further improve the model’s robustness on the chunking task by including a triplet loss constraint.
The research of Wireless Edge Computing Gateway with Anomaly Detection

To the superiority of low cost and convenience, large-scale Internet of things has been deployed for various monitoring pervasively. Typically cloud platform is utilized as a remote data and control center. However, it is a challenge to the bandwidth and real-time feedback to the server because of mass data with uploading and processing. In this paper, to cope with the above problems, we design a gateway based on edge computing. The gateway not only retains the function of connecting multiple sensors and the interaction between devices and cloud, but also can provide real-time local services for terminal devices. It also can drop abnormal data through sensors’ correlation analysis and reduce the amount of data upload by diverting the computing tasks of a server. In this paper, an anomaly detection method based on the bag of words model is introduced. At first, the gateway makes a bag of words representation by the data collected from each sensor, including normal and abnormal data. Then it aggregates these class predictions using the bag of words to calculate the final prediction. Through the simulation analysis based on the data such as humidity, temperature, and lighting monitored by sensors in agricultural greenhouses, the results show that the bag of words model can detect abnormal sensor data.
MAKE: A Combined Autoencoder to Detect Adversarial Examples

With the continuous development and maturity of deep learning technologies, security issues in deep learning are also getting more and more attention. The generation of adversarial examples makes scholars more aware of this point. The addition of small disturbance in the original image can cause the misclassification of images by deep learning models, which seriously hinders the development and popularization of deep learning technology in the future. Therefore, a method based on MSE and KL AutoEncoder (MKAE) to detect adversarial examples is proposed. By using MSE and KL divergence together, it is proved that MKAE can resist various types of adversarial attacks. At the same time, compared with the existing feature squeezing and MagNet detection algorithms, the detection accuracy is improved. This method is not dependent on the specific attack mode, which is a movable detection and defense model.
Significant Association Rule Mining Without Support and Confidence Thresholds

User-defined specification of support and confidence thresholds often degrades the significance of the association rules. The task is highly domain-dependent and henceforth leads to discovery of huge number of association rules. In addition, the existing approaches exclude rare itemsets for significant association rules. Significance depends on the associability between the itemsets forming association rules. This paper introduces an efficient technique for discovering significant association rules with high associability using multiple minimum supports (MMS), jaccard similarity index, and maximum permissible flexible dissociation. Subsequently, an efficient algorithm called SAMSAR (Semi-Automated Mining of Significant Association Rule) is developed that does not require support and confidence thresholds. Performance of the proposed SAMSAR is quite satisfactory in comparison with the relevant approaches.
Painting Element Segmentation Algorithm Based on Deep Network

We know that art and technology complement each other, because the art puts forward new requirements and development directions for technology, and technology supports the art performance more colorful. In recent years, the in-depth study of deep network has greatly promoted the progress of image processing technology, especially the combination of deep network and painting art has become a hot topic. Segmentation is of great importance in image processing. Painting elements segmentation and extraction is the basis of further research, such as identification, comparison and restoration in the later stage. There are many defects and shortcomings in traditional image segmentation such as low accuracy, noise sensitivity. While the method based on deep network shows many advantages, which can solve the problem of noise and non-uniformity in the image.In this paper, U-Net and IndexNet are selected to study the Figure segmentation of ancient painting. There are a few ancient painting samples for training network. If the network is trained directly, it cannot overcome the over-fit problem. A data enhancement method is proposed to improve the data volume and the reliability, then the data is used for U-Net training, and the loss function is minimized. There are four subsampling operations in the whole U-Net network. The operation of the up-sampling part is the opposite of the down-sampling, with 4 up-sampling times. The self-made dataset is put into the corresponding folder, and the appropriate learning rate is chosen through experiment. IndexNet is also trained and parameterized to produce predictions. Then the differences between the two models are analyzed and compared, and the better prediction results are selected for guided filtering. The guided filter is used for image dehazing and extinction, and when the guided image is the original image, the filter becomes an edge-preserving filter to optimize the prediction results. In the segmentation task of ancient paintings, we combine the segmentation network with the guided filtering network. There is high segmentation accuracy with our method. The Figure achieves a complete separation from the background.
Building a C++/CLI Wrapper

In this chapter, we build our first wrapper component. We use C++/CLI which allows .NET clients to call C++ code. We take StatsLib, our small library of statistical functions that we built in Chapter 2 , and expose the functionality via C++/CLI.
Optimal Symmetric Ratcheting for Secure Communication

Abstract To mitigate state exposure threats to long-lived instant messaging sessions, ratcheting was introduced, which is used in practice in protocols like Signal. However, existing ratcheting protocols generally come with a high cost. Recently, Caforio et al. proposed pragmatic constructions, which compose a weakly secure ‘light’ protocol and a strongly secure ‘heavy’ protocol, in order to achieve so-called ratcheting on-demand. The light protocol they proposed has still a high complexity. In this paper, we propose the lightest possible protocol we could imagine, which essentially encrypts and then hashes the secret key. We prove it secure in the standard model by introducing a new security notion, which relates symmetric encryption with key updates by hashing. Our protocol composes well with the generic transformation techniques by Caforio et al. to offer high security and performance at the same time. In a second step, we propose another protocol based on a newly defined integrated primitive, extending standard one-time authenticated encryption with an additional output block used as a secret key for the next message. We instantiate this primitive firstly from any authenticated encryption with associated data, and then we propose an efficient instantiation using advanced encryption standard (AES) encryption to update the key and AES-Galois/Counter mode of operation to encrypt and decrypt messages.
A Taxonomy of Optimal Feature Learning Model in Combined Data Mining System

Data mining is one the emerging field in recent days, which gained a significant attention due to its efficiency, proper decision making ability, and cost-adeptness. This paper investigating the performance of various data mining techniques in the financial domain. The main of this study to investigate the prospects and practices of the conventional data mining techniques for processing the large financial dataset. Also, the importance of data preprocessing, clustering, feature selection, and classification processes have been studied in this work. The different types of factors that helps to improve the reliability of data mining system are examined. Typically, the data retrieval process can be difficult in the financial banking systems due to its large size of information. Also, a machine learning classification technique can be developed for identifying the type of data with better performance rate and reduced time complexity factors. During experimentation, the existing techniques have been compared by using various performance indicators such as sensitivity, specificity, precision, recall, jaccard, and F-Measure.
Text-to-image Generation based on Spatial-Channel Attention and Semantic Redescription

Generating images from a given natural language description is a challenging task, and its primary purpose is to generate images which are visually real and semantically consistent. In this article, we propose a text-to-image-to-text model (SymmetricGAN) based on the spatial-channel attention mechanism, which has two modules: the spatial-channel attention module (SCAM) and the image semantic redescription module (ISRM). The spatial-channel attention module separates different visual attributes, so that the model pays more attention to the sub-regions corresponding to the most relevant words, and generate target images from coarse to fine under the cascade architecture. The image semantic redescription module regenerates the description text of the image to ensure that the composite image is consistent with the original semantic description. We show the comparison and results on two general datasets.
Fault feature fusion based on entropy-weighted nuisance attribute projection and orthogonal locality preserving projection

Traditional feature fusion methods are difficult to extract sensitive features related to fault under various operating conditions. A new feature fusion method is proposed in this paper, which uses the combination of entropy-weighted nuisance attribute projection (EWNAP) and orthogonal locality preserving projections (OLPP) to extract new features. The clustering performance of the proposed method is macroscopically described by the scatter plot graph, and the new feature vectors processed by EWNAP-OLPP as the samples put into the back propagation (BP) neural network for fault pattern recognition.
Application Research on Key Points of Software Security Development Technology

Abstract With the rapid development of network technology, software security has attracted more and more attention. Based on the software security lifecycle presents an improved suitable for small and medium-sized enterprise software security development process, make the software safety to cover the whole software life cycle, improve the level of safety of encoding software. The model used in the software development phase, test phase and release phase three software security key technologies are studied and discussed in detail. In addition, it also designed a software security development management platform, the software development process so that the model can better apply to the actual enterprise to the platform. After a number of units for a period of time of trial, get a better evaluation of the households.
Adversarial Attacks Impact on the Neural Network Performance and Visual Perception of Data under Attack

Machine learning algorithms based on neural networks are vulnerable to adversarial attacks. The use of attacks against authentication systems greatly reduces the accuracy of such a system, despite the complexity of generating a competitive example. As part of this study, a white-box adversarial attack on an authentication system was carried out. The basis of the authentication system is a neural network perceptron, trained on a dataset of frequency signatures of sign. For an attack on an atypical dataset, the following results were obtained: with an attack intensity of 25%, the authentication system availability decreases to 50% for a particular user, and with a further increase in the attack intensity, the accuracy decreases to 5%.