Reviewerly/exHarmony huggingface dataset:
International Chinese Learning Resource Management Model Based on Blockchain Technology

A management model of international Chinese learning resource is proposed to ensure security, efficiency, and accuracy using blockchain technology. The dimensions of the model are defined to construct the model in four key aspects: identity authentication, result authentication, resource sharing, and community governance. Distributed Ledger Technology, National Algorithms, and Consortium Blockchain Mechanisms are adopted to develop the model. The developed model consists of five distinct layers and five modules. The model allows Chinese language learners and teachers to access a wide range of resources with an innovative approach.
Design of Visualization Platform for Aviation Cultural and Creative IP Based on Computer Virtual Technology

With the development of the aviation industry, the visualization of aviation culture IP is becoming increasingly popular. Visualization of aviation culture IP enables people to tend to enjoy visual beauty and is an important bridge for information transmission and communication between the aviation industry and people. However, due to the complexity and variability of the aviation environment, as well as the uncertainty of aviation information, the design of the platform for aviation cultural creativity IP visualization faces enormous challenges. In disseminating information about the product, the platform needs to show the cultural added value of the product. In order to enhance consumers' visual impression and improve the application effect of visualization platforms, we conduct in-depth research on the design of the platform through online surveys on their application effects using combined computer virtual technology. The survey results showed that 34.2% of the people were very satisfied with the visualization effect of the platform, and 48.6% were very satisfied with the body feeling in using the platform. The results imply that the platform for aviation culture creative IP visualization based on computer virtual technology provides users with an immersive and ultimate sensory experience, attracting users' interest in aviation culture.
An Automatic Discovery Framework of Frequent Topics, Association Rules, Clusters, and Sequential Patterns in Social Media with Chinese Word Segmentation

More data are created in social media representing situations in the real world. Four hundred thousand posts on Facebook were collected and added to the mysql database to select the content to store them in comma-separated value (CSV) files. This work parted the data in order to use Chinese word segmentation system. Then, the data were tagged for the contents of posts in the Chinese word segmentation system. C# was used to parse the tagged result and select common nous (Na) and proper nouns (Nb) to count and the frequent pattern growth (FP-tree and FP-growth) algorithm was used to discover the frequent patterns. The result shows the top-k objects of the maximal counts and the association rules of the objects. The K-medoids algorithm was used to cluster the objects, and the efficient PrefixSpan (Prefix-Projected Growth) algorithm was used to discover their sequential patterns. This work is the first step for knowledge discovery and data mining of frequent topics in social media and these algorithms are original types. Various novel algorithms of topics can be proposed for mining new variations of association rules, clusters, and sequential patterns within this framework.
Recommendation Model of Graph Neural Network Based on Neighbor Aware Interaction

The personalized recommendation system plays a major role in various online services. In recent years, the graph learning based emerging research about recommendation systems has developed rapidly. Some scholars use graph neural network to formulate high-level connection between users and items to improve embedding. The relationship information between neighbor nodes implies the expression differences of diverse neighbors, and it can reflect the signal strength of item characteristics. However, most studies have not considered the difference in implicit relationships in diverse neighbors. In order to distinguish the importance of different neighbors, this paper proposes a neighbor aware interaction graph attention network (NAGAT) for recommending tasks. It uses a novel neighbor aware attention layer to calculate the similarity between each pair of neighbors, the contribution of each pair of neighbors in neighbor interaction is distinguished by allocating distinct neighbor aware attention weight for each neighbor. Then, interaction information of neighbor pairs is combined with the node representation aggregated through the graph attention network to generate a novel embedding representation. Several experimental researches were conducted on two public datasets in Yelp2018 and Amazon-books, and the results show that the proposed is 1.8%-2.0% higher compared to currently advanced methods.
ATM weapon, fraud detection and cybercrime prediction

The automatic teller machine is an important source for making transactions in everyday life. The automatic teller machine is used by the public and it should be secure and safe to use by people. There may be scenarios when some strange things happen in automatic teller machines such as physical attacks by using guns or knives in public and some may try to anguish the automatic teller machine physically using a hammer, axe or by wearing a helmet inside the automatic teller machine. Another cause may be criminals enter into automatic teller machines and do unusual activities or then can put people at gunpoint and tell them to give their automatic teller machine card credentials, to solve these issues we need a real-time monitoring system that can analyze the scene in real-time and intimate to respective authorities to take preventative actions to avoid these uncommon actions. The authorities may be the security of the automatic teller machine who takes care of the automatic teller machine or police station and the control room of the automatic teller machine system. The real-time monitoring and detection of anomalous tasks are quite difficult because of the changing background. Here we have developed a real-time automatic teller machine monitoring system, which can monitor continuously and detect if any unusual activities occur and raise an alert by using SMS or sound continuously to respective authorities to take preventative action. Keywords— weapons, helmets, multiple people, criminals, intimation
Comparative Analysis and Case Study of Integrating DEFSOP and ISO 27050 International Standards

As technology advances and cyber crime rates rise, companies are facing threats such as hacker attacks, cyber fraud, and theft of property, making the practical work, its process, and the verification of digital forensics more important. Because digital evidence preservation needs corresponding capacity and a large space, digital forensics personnel in compliance with the provisions of the SAQSIQ and its related sub-laws need to follow ISO27050 international standards. For the evidence preservation and complete implementation process, it is important to strengthen the control measures and norms of electronic evidence discovery. Therefore, we study the digital evidence identification standard and its operating procedures and international standards ISO27050 of electronic evidence discovery standard. We also analyze the security mechanism of four parts of international standards and digital evidence identification standard operating procedures, ISO 27050-1:2019, ISO 27050-2:2018, ISO 27050-3:2020, and ISO 27050-4:2021. The results provide before-, in- and after-process to construct effective evidence preservation systems, integrate the identification principle into the user's property from disaster, and verify the importance of digital evidence identification.
DeepWise Cyber Teen Guardian: Protecting Internet Environment via a Novel Automatic Adversarial Samples Detection System Based on Revised Neural Network

In order to assist teenagers to surf the internet healthily, there are image filters based on image recognition neural networks removing inappropriate content, but image filters are vulnerable to attack by adversarial samples, which are generated by adding well-crafted noise to an image, making the filter consider an inappropriate image as appropriate. In order to defend against adversarial attacks, various adversarial detection algorithms are designed. The state-of-the-art detector enjoys a promising detection performance, but it suffers from high computational overhead. In this work, DeepWise is proposed, which records the mean and covariance for each class of images at each recording layer of the neural network during training. The input image’s mean and covariance at each layer and the network’s classification of the image are also recorded. Then, the Mahalanobis distance is calculated between the distributions of input image and the training images at each layer based on the network’s prediction. A linear regressor takes these distances as input, and determines whether the input image is an adversarial sample or not. DeepWise achieves a computational saving ranging from 39.77% to 87.98% for datasets SVHN, CIFAR- 10, and CIFAR-100 on the image recognition model ResNet-34 and DenseNet-3 while preserving a comparable AUROC to the existing state-of-the-art method.
Research on Industrial Internet Data Security Technology based on Formal Verification of SM4

Industrial Internet promotes the transformation of industrial platform from business system driven to data driven through the flow of industrial data between massive multi-source equipment and heterogeneous systems. The data flow of industrial Internet involves the interactive transmission of a large number of key sensitive information between enterprises. Coupled with its inherent openness and heterogeneity, it is facing serious security challenges such as sensitive data leakage and integrity destruction. Cryptography technology is one of the important methods to ensure data security. Among them, the national cryptographic algorithm can meet the requirements of security, autonomy and controllability of Chinese industrial Internet data, and avoid the risk of "backdoor" when the current mainstream international cryptography algorithm protects the key sensitive data. However, there are still security threats such as memory leakage and time-side channel attack which cause the invalidity of encryption mechanism when the national cryptographic algorithm is deployed in industrial Internet system. Formal verification method is an important method to verify the security and reliability of cryptographic algorithms. Existing researches have designed a variety of formal verification mechanisms for the underlying cryptographic algorithms of some structures and protocols, but the research on formal verification for national cryptographic algorithm is still in a blank stage. This paper proposes a formal verification method of SM4 cryptographic algorithm based on programming framework F*, which can ensure the memory security and resist the time-side channel attacks during the implementation of the algorithm. And the method can reduce the security risks, when the national cryptographic algorithm is deployed in the industrial internet.
Development of Paragraph Automated Essay Scoring System of Chinese Writing

At present, most of the automated essay scoring systems used in teaching score the whole essay. Therefore, we develop an automated essay scoring system that scores and comments instantly on each paragraph. The feedback supports elementary students to practice writing essays and improve their writing skills. The study also researches the system’s usability. This developed writing essay scoring system uses training models created with Python. Firstly, the collected 166 essays are classified into each paragraph, then data is classified into 1−6 grades according to the composition scoring rules set by the experienced teacher. Secondly, training data performs feature extraction by ALBERT, a lite BERT. Then, training data is used to train the deep learning of the scoring model, the Bi-GRU model. Thirdly, the scoring model is linked to the comment database. The comment is selected according to the scoring model’s result. Finally, the models are combined to complete the automated essay-scoring system. When the user input content of the paragraph, the system links it to the scoring model and comment database of the paragraph. After the scoring model’s judgment is completed, the score and the comment feedback are output. The user can modify the content according to the comment until the modification is completed. The user continues to write until the completion of the essay. Elementary school teachers tested the system developed, received interviews, and filled out questionnaires to understand the usability of the system and its impact on learners' writing learning. Elementary school teachers scored an average of 87.9 points on the system developed in this study. The system was commented on for good impressions and ease to use. They also agree with the practicality of teaching. In scoring, the teachers had different results.
An improved Tibetan speaker recognition method based on ResNet

In the past decade, there have been relatively few studies on speaker recognition based on minority languages, especially Tibetan, and most of them use traditional recognition methods. Starting from the Tibetan level, this paper adopts the mainstream neural network learning, and proposes a new Fast ResNet-101 structure by improving the residual network (ResNet) module structure, thus constructing a complete Tibetan speaker recognition system. The improved model is compared and evaluated by two metric learning loss functions, prototype and angle prototype. By introducing Fast ResNet-34 and Fast ResNet-50 models as baselines, the experimental comparison shows that the Fast ResNet-101 model with deeper network structure has the best performance, and the model recognition effect after Angular Prototype metric loss function processing is better. The recognition error rate can reach 3.72 %.
Uncovering the Cloak: A Systematic Review of Techniques Used to Conceal Phishing Websites

Phishing represents a cybersecurity attack strategy commonly employed by cybercriminals to unlawfully acquire sensitive user information, including passwords, account details, credit card data, and other personally identifiable information. Phishing websites bear a striking resemblance to their legitimate counterparts, thus rendering them inconspicuous and challenging for an unsuspecting user to identify. Criminals and phishing experts frequently leverage cloaking mechanisms to evade detection software and web crawlers. This paper provides a comprehensive systematic review of primary studies conducted between 2012 and 2022 on using cloaking techniques to evade detection by anti-phishing entities based on data extracted from Scopus, Web of Science, and Google Scholar. Different server-side and client-side detection strategies, phishing techniques and cloaking mechanisms, toolkits, blacklists, phishing or anti-phishing ecosystems, and other such concepts have been taken as thematic outputs of the study and have been discussed in detail. This systematic literature review (SLR) is one of the first reviews to be conducted for analyzing the current cloaking or evasion techniques used by phishers, and the limitations of the study have been outlined as well.
Communication Encryption Scheme of 5G Power Trading Private Network Based on Dynamic Key

Security has been the primary concern for the 5G power trading private network, which involves a large amount of power trading information. The communication must be encrypted to prevent the leakage of important information. The communication encryption scheme for 5G power trading private network should meet the characteristics of small communication overhead, low storage space requirement and strong resistance to attack. Based on these characteristics, the dynamic key-based encryption scheme is designed. To ensure the security and reduce the computation overhead, the optimized symmetric encryption algorithm is used to update the shared key. And the dynamic key generation method is improved by adding random numbers to solve the problem that the correct transmission of the data link does not have true randomness. The encryption mode used can generate dynamic keys directly and synchronously at both ends of the communication, reducing the overhead of key exchange and eliminating the need for trusted third parties. The algorithm is also analyzed for security and efficiency and compared with other algorithms. The experimental results show that the improved algorithm enhances security with guaranteed efficiency.
Research on Tibetan Speech Recognition Based on CNN-DFSMN-CTC

In this paper, we present an improved acoustic model CNN-DFSMN , and it uses CNN to study local frequency domain and time domain features ,and introduces skip connections between memory blocks in adjacent layers, thus alleviating the problem of gradient disappearance when building very deep structures. In recent years, the acoustic model based on Connected Temporal Classification (CTC) has achieved good performance in speech recognition. Generally, lstm-type networks are used as acoustic models in CTC. However, LSTM calculation cost is high and sometimes it is hard to train CTC criteria. This paper, Be inspired by DFSMN's work, we replace LSTM with DFSMN in the acoustic modeling based on CCT, then combine convolution neural network (CNN) with this architecture to train an acoustic model based on CNN-DFSMN-CTC, match the acoustic model with the 3-gram language model, and combine dictionary and acoustic feature vector to identify and decode the recognition text. This further improves the performance of Tibetan speech recognition. The last experiment results show that the WER of DFSMN-CTC based methods is 2.34% and 0.94% higher than that of CNN-CTC based and LSTM-CTC based methods under the same test set. The recognition rate based on CNN-DFSMN-CTC is 3.52% and 2.23% higher than that based on DFSMN and DFSMN-CTC.
Detection of Data Scarce Malware Using One-Shot Learning With Relation Network

Malware has evolved to pose a major threat to information security.Efficient anti-malware software are essential in safeguarding confidential information from these threats.However, identifying malware continues to be a challenging task.Signature-based detection methods are quick but fail to detect unknown malware.Additionally, traditional machine learning archetype requires a large amount of data to be effective, which hinders the ability of an anti-malware system to quickly learn about new threats with limited training samples.In a real-world setting, the majority of malware are found in the form of Portable Executable (PE) files.While there are various formats of PE files, samples of all formats such as ocx, acm, com, scr, etc., are not readily available in large numbers.Therefore, building a conventional Machine Learning (ML) model with greater generalization for data-scarce PE formats becomes a hefty task.Consequently, in such a scenario, Few-Shot learning (FSL) is helpful to detect the presence of malware, even with a very slight number of training samples.FSL techniques help to make predictions based on an insufficient number of samples.In this paper, we propose a novel architecture based on the Relation Network for FSL implementation.We propose a Discriminative Feature Embedder for feature extraction.These extracted features are passed to our proposed Relation Module (RM) for similarity measure.RM produces the relation scores that lead to improved classification.We use Portable Executable (PE) file formats, i.e., ocx, acm, com and scr, after transforming them into images.We employ five-shot learning and then one-shot learning, which produce 93% accuracy with only one training instance.We observe that the proposed architecture outpace the baseline method and provides enhanced accuracy by up to 94% with only one sample.INDEX TERMS data-scarce malware, feature embedding, meta-learning, one-shot learning, relation network I. INTRODUCTIONM ALWARE is a fast-spreading epidemic in the current age of the Internet.Malware includes all programs that can cause a computer system crash, data leakage, unlawful incursion, resource damage, etc [1].According to the latest studies, it is increasing at a distressing rate and thus can be considered lethal for the health of the confidential data.Kaspersky's detection systems detected an average of 400,000 malicious file distributions per day in the year 2022.In 2021, daily 380,000 of these files were detected, signifying a 5% growth in comparison to 2021.To combat the increase in malware attacks, there is a vital need to develop an intelligent and efficient malware detection system [2], [3].Most malware detection systems employ machine learning methods such as support vector machines, decision trees, and logistic regression.Nonetheless, these models necessitate a
Digital Risk Assessment Framework for Individuals: Analysis and Recommendations

As individuals increasingly engage with the digital landscape, they face a multitude of risks associated with their online activities and the security of their personal information. Individuals seek guidance in balancing the benefits and risks of the digital transformation. To effectively mitigate these risks, it is essential to establish a comprehensive Digital Risk Assessment Framework tailored to individual users. In this research, an a interpretive study have been carried out to propose a novel Digital Security Management Framework. The main contribution of this study is providing a novel approach by examining the recent recorded threats against individuals, quantifying these threats, and proposing a novel digital risk framework detailing the list of threats and the corresponding risk treatment options tailored for individuals. The scenario of the case study is a family that use personal computers to access banking and investment accounts online, engage in online shopping and also frequently use social media to share artwork and opinions. 17 types of digital risks were identified and the probability of loss and impact of each risk have been quantified using Bernoulli distribution f(L;p). The quantified values were used to prioritise mitigation measures. According to the results, and the proposed framework, suitable treatment option(s) was recommended for each risk. The results show that online scams present the biggest financial risk to individuals, that security incidents present a moderate risk, and that communication-based harms (e.g. bullying and radicalization) are difficult to quantify.
Adaptive Motion Control for an Autonomous Mobile Robot Based on Space Risk Map

To make mobile robot move considerately, an adaptive motion control method integrated potential field method is proposed. Risk map is firstly defined to show the magnitude of risks in the empty spaces around objects, then the considerate path is generated based on based on risk map. The robot is controlled by using integrated potential field method. The robot can automatically move with a considerate path to avoid disturbing others without make multiple moving rules, and areas with medium level risk are still passable, when there is no better choice. Besides, the robot will still make the users feel conformable by using effective interactions, like moving slowly to make sure safety or warning the users by sound. The effectiveness of the proposed system is proven by comparing the experimental results with those controlled by conventional potential field method.