Reviewerly/exHarmony huggingface dataset:
A Hybrid Approach to Vulnerability Assessment Combining Attack Graph and Hidden Markov

The use of attack graphs in the study of network vulnerability assessment is a classical and effective way, which breaks the deficiency of traditional methods that can only do a static assessment based on the threat level of vulnerabilities. In order to assess the overall vulnerability of the network system, this paper studies different vulnerability assessment methods based on attack graphs and proposes the AGH model, which uses CVSS to statically assess the probability of vulnerability exploitation and combines the Hidden Markov Model and Viterbi decoding algorithm to calculate the maximum probability of attacker attack path. Finally, the feasibility of the method is verified by conducting experiments with a real laboratory network topology.
DOCUMENTATION MANAGEMENT AND THE AUDIT TRAIL IN PUBLIC ADMINISTRATION

Our world and the issue of its public administration are undergoing transformations in the form of complex digitization. The ensuing changes are also affecting processes within public administration organizations and include modernization of documentation management. The question remains as to how documentation management can be effectively set up for modern public administration. As such, the research objective of the present paper is the design of documentation management and the audit trail so as to ensure the effectiveness of public administration. From fulfilling this goal, we can hypothetically expect a significant simplification and streamlining of documentation management, which will provide, among other things, information sufficient for conducting auditing activities and ensuring the effectiveness of the public administration system – the audit trail. Analytical testing is used to meet this objective, where documentation processing intervals were measured on a selected sample of 284 documents in the field of public administration organizations. The sample included directives, work procedures, forms, and operating rules that constitute controlled documentation. The conclusions of this research have primarily yielded a schematic workflow of documentation management in public administration. The proposed documentation management workflow will specifically enable public administration to increase efficiency, consistently implement the audit trail, and increase computerization, which will contribute holistically to the development of public administration. Keywords: Administration, Audit, Documentation, Management, Trail
A study of Wortfeld and lexical cohesion observed in Spanish Lexical Solidarity

Vocabulary is structurally separated according to whether individual words form groups based on their meaning or not. Ibsen's famous so-called Feld or Wortfeld theory is an approach to reveal the structure of the whole vocabulary by studying the properties of detailed words belonging to a vocabulary unit in an attempt to reveal the structure of the vocabulary in a language. This study, based on Ibsen's word field theory, confirmed the internal consistency of the word fields found in Spanish verbal locution, collocation, and phraseology for determining to specify meaning, Lexical Solidarity. Its purpose is to reveal the structure, moreover, Coseriu’s Lexical Solidarity considers the word field’s lexical structure begun in Ibsen as an integrated structure, and sees that the lexical unit has a systematic structure due to the limitations of the word field composition. Therefore, when analyzing the lexical structure of the representative Lexical Solidarity. We will examine the coherence between the standardized lexical units by the isolation of morphology and meaning caused by the word sets and the sequential properties of each lexical unit.
Evaluation method for interoperability of weapon systems applying natural language processing techniques

The current weapon system is operated as a complex weapon system with various standards and protocols applied, so there is a risk of failure in smooth information exchange during combined and joint operations on the battlefield. The interoperability of weapon systems to carry out precise strikes on key targets through rapid situational judgment between weapon systems is a key element in the conduct of war. Since the Korean military went into service, there has been a need to change the configuration and improve performance of a large number of software and hardware, but there is no verification system for the impact on interoperability, and there are no related test tools and facilities. In addition, during combined and joint training, errors frequently occur during use after arbitrarily changing the detailed operation method and software of the weapon/power support system. Therefore, periodic verification of interoperability between weapon systems is necessary. To solve this problem, rather than having people schedule an evaluation period and conduct the evaluation once, AI should continuously evaluate the interoperability between weapons and power support systems 24 hours a day to advance warfighting capabilities. To solve these problems, To this end, preliminary research was conducted to improve defense interoperability capabilities by applying natural language processing techniques (①Word2Vec model, ②FastText model, ③Swivel model) (using published algorithms and source code). Based on the results of this experiment, we would like to present a methodology (automated evaluation of interoperability requirements evaluation / level measurement through natural language processing model) to implement an automated defense interoperability evaluation tool without relying on humans.
A Study on the Data Pseudonymization Methodology for Defense Training Data as Artificial Intelligence Technology is applied to the Defense Field

Recently, in the defense field, efforts are being made to collect data by building data centers to incorporate artificial intelligence technology. Weapon system training data can be used as input data for artificial intelligence models and can be used as high-quality data to maximize training performance and develop military strategies. However, training data contains personal information such as the names and military numbers of the personnel who operated the equipment, and training records that reveal the characteristics of the weapon system. If such data is passed on to the enemy, not only the specifications and performance of the weapon system but also the proficiency of each operator may be exposed. In this paper, we propose a pseudonym processing methodology for education and training data security and also suggest a direction for revising related laws.
CDRF: A Detection Method of Smart Contract Vulnerability Based on Random Forest

With the widespread applications of smart contract, a large number of smart contracts with virtual coins have been deployed on Ethereum. However, smart contracts may have vulnerabilities that cause huge losses. Therefore, it is a critical issue to effectively and time-savingly detect potential vulnerabilities in contracts. Oyente and Mythril are both contract analysis tools based on symbolic execution, which rely on control flow graph to detect vulnerabilities and are time-consuming. In this work, we propose CDRF to detect vulnerabilities in smart contracts with machine learning. First, we summarize four kinds of opcode fragments containing key vulnerability instructions. The opcode fragments are processed by word2vec and PCA to obtain one-dimensional binary features. Second, we use five machine learning algorithms to build the model. This method uses the 53651 real-world smart contracts on Ethereum for evaluation. When we use CDRF as the training model, the highest predictive value of F1-socre is 98.03 $$\%$$ , and the rest are above 93 $$\%$$ ; the highest predictive value of AUC is 99.56 $$\%$$ , and the rest are above 94 $$\%$$ . Meanwhile, the average detection time for each smart contract is 3 s. The experimental results show that the method is effective and time-saving.
Data Privacy and Security Model in Cloud Environments

The privacy and security of data stored in cloud environments remain a significant challenge for organizations and individual users. Since data is stored in different physical locations, data security and privacy are particularly critical in cloud environments. Today, data privacy and security are the main concerns of governments, organizations, academics, and IT industry experts. The future development of cloud computing depends on providing reliable data privacy and security solutions. The current study performs an in-depth assessment of data privacy and security issues affecting individual users and organizations. The analysis is followed up by a suggested solution of an adequate data security and privacy model based on a combination of file encryption and service fragmentation. Specifically, the development of the analysis and security model focuses on the Linux operating system. Finally, extensive simulation results prove that the proposed model gives more privacy accuracy and reliability in cloud environments.
Fish-eye image effective region segmentation algorithm based on circle fitting

When synthesizing panoramic images from fish-eye images, dynamically segmenting the fish-eye image's valid region is crucial. This paper addresses the challenge of segmenting the valid region in fish-eye images caused by lens glare by proposing a segmentation algorithm that combines adaptive threshold edge detection with improved circle fitting. The process begins by performing gradient and direction calculations on the original fish-eye image to extract edge contour information. Subsequently, the edge contours undergo refinement using the non-maximum suppression method. An adaptive dual threshold is generated using the maximum between-class variance method to enable accurate edge detection. The algorithm further initializes and fits the edge points using the least squares method while incorporating a loss function to enhance the circle-fitting algorithm. Finally, an iterative gradient descent method is employed to iteratively solve for the optimal solution and segment the fish-eye image's valid region. Experimental results demonstrate the algorithm's ability to accurately detect contours and effectively segment the valid region, successfully addressing the challenge of extracting the valid region caused by lens glare.
The Application of Improved Algorithms in Blended Teaching of Financial Analysis Courses

Higher education is developing towards the trend of informatization, and the way college students learn financial analysis courses has also undergone essential changes. The traditional classroom teaching mode is too single, the content is boring, it cannot attract students, and the teaching effect is not satisfactory. At the same time, there are many contents covered in the financial analysis course, among which financial texts can help enterprises avoid investment risks and prevent financial fraud. Therefore, colleges and universities adopt a blended teaching model, which combines traditional classroom teaching mode on the basis of constructivist education theory, and uses online teaching platforms to improve teaching quality. In the blended teaching of financial analysis classroom, an improved algorithm is introduced to retain the characteristic word frequency factor of financial texts and reassign the weight of characteristic words. The use of this method can improve the performance of financial text classification, so that teachers can complete the blended teaching of financial analysis courses in a targeted manner.
Grid based Mobile Communication Network using Multi Stage Authentication Method in Mobile WSN

Mobile Wireless Sensor Networks (WSNs) are used in a variety of applications, such as military, industrial and consumer products. As WSNs are increasingly deployed in wireless environments, security has become a major issue. One of the key security techniques for protecting WSNs against unauthorized access is the use of multi-stage authentication methods. These techniques use a range of techniques to verify the identity of the user attempting to access the WSN. In multi-stage authentication, the security of WSNs is enhanced through the addition of multiple levels of verification. Each verification stage requires an increased degree of validation. This means that the further a user moves through the stages, the more secure they become. Validation techniques used in multi-stage authentication can include the use of passwords, biometrics, cryptographic keys, certificates, tokens and other cryptographic techniques such as digital signatures or public key encryption. Due to the critical importance of security in WSNs, the use of multi-stage authentication is becoming increasingly common. It provides a robust level of security which is required for protecting the data and resources of WSNs from unauthorized access. Furthermore, as technology evolves, multi-stage authentication is capable of evolving with it, offering WSNs increasing levels of protection from the constantly changing threats.
Video stabilization algorithm based on multiple cameras

The proposed video stabilization algorithm for head-mounted mobile platforms addresses the issue of video shake. In the feature detection and extraction stage, the Super point feature extraction algorithm is used to extract feature points. This algorithm provides a more comprehensive set of feature points compared to traditional feature detection methods, enabling accurate motion estimation.In the motion smoothing stage, the camera path of a specific camera is preprocessed using Kalman filtering to remove significant shaking factors. Then, the processed path is used as a baseline to simultaneously smooth all camera paths through spatio-temporal optimization, aiming to make the camera paths as similar as possible and reduce distortion. Finally, the optimal path is selected from the refined paths to address the deviation among multiple cameras.Experimental results have demonstrated that this method achieves comparable stability to other methods while significantly reducing the required processing time.
A Novel Approach of Animal Skin Classification Using CNN Model with CLAHE and SUCK Method Support

This study describes the process of classifying animal skin images which are rather difficult to obtain optimal image characteristics. For this reason, in the pre-processing stage, we propose two methods to support feature extraction: sharpening using a convolutional kernel (SUCK-Sharpening) and adaptive histogram equalization with limited contrast (CLAHE-Equalized). SUCK works by operating on these pixel values using direct math to build a new image; this final value is the new value of the current pixel. CLAHE overcomes the limitations of the global approach by performing local contrast enhancement. Because of the advantages of the two methods, it becomes a solution to get features processed at the feature extraction and classification stage. The process of animal skin imagery has characteristics in terms of shape and texture, including the characteristics of animal skin color. In this study, some experiments have been carried out on several CNN models, with an average classification accuracy of more than 70% using the sharpened and equalized methods on six animal skins. More detail, the average classification accuracy using 3 CNN models supported by two methods, namely Sharpening and Equalize on the CNN Resnet 50V2 model is 67.73% and 73.78%, InceptionV3 model at 82.13%, and 74.76% and Densenet121 models were 87.64% and 87.46 %. This research can be continued to improve the accuracy of other animal skin images, including determining fake or genuine skin images.
S3DCN-OLSR: A shallow 3D CNN method for online learning state recognition

The repeated recurrence of COVID-19 has significantly disrupted learning for students in face-to-face instructional settings. While moving from offline to online instruction has proven to be one of the best solutions, classroom management and capturing students' learning states have emerged as important challenges with the increasing popularity of online instruction. To address these challenges, in this paper we propose an online learning status recognition method based on shallow 3D convolution (S3DC-OLSR) for online students, to identify students' online learning states by analysing their micro-expressions. Specifically, we first use the data augmentation method proposed in this paper to decompose the students' online video file into three features: horizontal component of optical flow, vertical component of optical flow and optical amplitude. Next, the students' online learning status is recognised by feeding the processed data into a shallow 3D convolution neural network. To test the performance of our method, we conduct extensive experiments on the CASME II and SMIC datasets, and the results indicate that our method outperforms the other state-of-the-art methods considered in terms of recognition accuracy, UF1 and UAR, which demonstrates the superiority of our method in identifying students’ online learning states.
Analysis of Text Data Reliability Based on the Audience Reactions to the Message Source

Classification between relevant and irrelevant data is one of the most important tasks in modern machine learning. This paper developes a semi-supervised classification methods that is based on partially defined information for a text source. The method is based on the analysis of the author’s engagement into the subject area and the combination of his assessment by readers with quality of published texts. To achieve this we used a combination of joint author and text analysis that allowed to significantly reduce workload for further data markup. The method uses a learning loop that balances class attribution probabilities for text data at the level of the most effective class separation for a given noise level in the data. It was found that different paths for estimating relevance probability for relevant and irrelevant records had to be used. Both likelihood and plausibility approaches were used to achieve acceptable level of classification. The method was developed in process of analysys of large number of texts from social networks. It showed high effectiveness on a large amount (>70 mln records, >8 mln unique authors) of confusingly similar text data.
Pengaruh Media Google Form terhadap Motivasi Belajar Siswa SMKN 71 Jakarta

The use of practical and efficient learning media is still rarely used in learning, giving rise to a less active and passive learning atmosphere at SMKN 71 Jakarta, therefore these problems can be overcome with Google form media. The purpose of this study was to analyze and find out whether there is an influence of Google Form media on student learning motivation and to calculate how much influence Google Form media has on student learning motivation. This research method is a correlational method that is used to measure whether there is a relationship and influence between the x and y variables and then a significance test is carried out. The results of this study are that there is an influence of Google Form media on student learning motivation with the results of the correlation that has been analyzed which is equal to 0.545 while the results of the significance test are positive and significant. The conclusion of this study is that there is an influence of Google Form media on students' learning motivation in the moderate category as well as positive and significant.
Tourist behavior after service robots

This is a conceptual review paper explains and discusses the role and potential of service robots in tourism and hospitality from the perspective of consumer behavior. The adoption and use of robots have increased rapidly in tourism and hospitality over the past few years due to a number of reasons. With the developments in digital technology and artificial intelligence, the use of robots has become cheaper and easier for businesses in various industries including tourism. Moreover, the developments such as the Covid-19 pandemic accelerated the adoption of robots in tourism and hospitality where interaction with the customer is frequent and intense. In line with the growth of the adoption and the use of service robots in the industry, there is a growing body of research in the field. The future potential of service robots and their adoption by businesses depend very much on a better understanding of service robots. This review study explores attitudes and expectations of consumers regarding the use of service robots at hotels.
Research on commodity intelligent recommendation system based on data mining

With the development of computer technology and the urgent need of the commercial society, the recommended products urgently require the realization of intelligence and precision. The commodity intelligent recommendation system based on data mining uses Spring Boot technology at the front end and SQLite database at the back end. Data mining algorithm is used for data analysis and screening. Design and implementation the function modules of the commodity intelligent recommendation system based on data mining, such as home page, mall, exploration and me. Through the test, the performance and throughput rate of the commodity intelligent recommendation system based on data mining are good. It has good stability performance and achieves the goal of intelligent product recommendation.
Research on Anomaly Detection Method of Non-Standard Machinery Production Process Based on Semi-Supervised Learning

The non-standard machinery refers to customized machinery produced to meet specific customer demands. The mainstream research direction in data stream anomaly detection focuses on deep learning, which involves learning data distribution through a large amount of training data. However, non-standard machinery equipment has the characteristics of a small production scale and sparse samples, making it difficult to obtain sufficient annotated training sets. This inadequacy in training data results in the model not learning enough, thereby rendering it unable to effectively detect abnormal events. In this paper, we propose a semi-supervised learning (SSL) based anomaly detection method. We employ a hybrid C-LSTM network based on the self-attention mechanism as an abnormality prediction model, where the convolutional neural network (CNN) and long short-term memory network (LSTM) extract spatiotemporal features of industrial data streams. The self-attention mechanism calculates the relationship weights between different positions in the input data, capturing long-term dependencies in time series data to fully learn data distribution. To improve the training effectiveness of the prediction model, we use an updating algorithm based on weighted fuzzy rough set (WFDA) to update the prediction model in a reverse manner. This algorithm can classify data streams in real-time, compare the classification results of the prediction model, and retrain unreliable data. The experimental results show that our proposed method achieves an F1 score of 0.955 and a recall value of 0.957 on a real-world data set, which is a 4.1% improvement in F1 score and a 6.4%improvement in recall compared to similar anomaly detection algorithms that do not use our proposed method.