Reviewerly/exHarmony huggingface dataset:
Artificial Intelligence Technology Boosts the Construction of English Intelligent Teaching Mode in Colleges and Universities

Abstract With the goal of improving the teaching effect of English in colleges and universities, this paper combines artificial intelligence technology with teaching and constructs an English-intelligent tutoring teaching model. By analyzing the main forms of artificial intelligence in the field of teaching, the advantages of intelligent teaching mode in college English teaching are explored from personalized teaching as well as teaching effect. Combining the RBF neural network to analyze the students’ learning data, the function approximation principle and interpolation method are used to improve the accuracy of the analysis of the students’ data. Using the form of network topology, the information transfer process in English intelligent teaching is explored. To improve the classification of students’ abilities and the prediction of their grades, judgment trees are added to the network. The English Intelligent Assisted Teaching model was applied to teaching to explore its feasibility and effectiveness. The results show that students’ satisfaction with personalized teaching is 0.85, and their satisfaction with personalized evaluation is 0.8. The students’ translation ability under the English Intelligent Assisted Teaching Model has improved more, from 75 to 90.
Application of big data technology in traditional dance video movement recognition research

Abstract This paper proposes a multi-feature fusion approach for action recognition under big data technology with the goal of improving traditional dance video action recognition. By analyzing the basic method of dance action, the extraction process of dance action features is analyzed using both single-layer and hierarchical methods. Multi-feature fusion action recognition is chosen as the main method for action recognition. The image and audio features of the dance video are combined to improve the accuracy of recognizing dance actions. Use the optical flow algorithm to construct a histogram of the optical flow direction. The method’s feasibility is explored by applying the multi-feature fusion recognition method to traditional dance movement recognition. The results show that in traditional dance movement recognition performance, the performance of the method of multi-feature fusion recognition is improved by 7.6% compared to other traditional methods. The multi-feature fusion recognition method has more than 50% accuracy in recognizing different traditional dance movements and similar movements in terms of movement recognition accuracy. To a certain degree, this study enhances the efficiency of traditional dance movement recognition and conserves human and financial resources in dance movement recognition.
Incorporating Multidimensional Data Analysis Methods for Probability Theory and Mathematical Statistics Teaching Reform and Practical Exploration

Abstract Based on the new curriculum reform and big data technology, this paper uses the radial function and RBF neural network algorithm in the multidimensional data analysis method to obtain the center, variance and output layer power of the neurons of Probability Theory and Mathematical Statistics. Construct the teaching evaluation model of Probability Theory and Mathematical Statistics according to the RBF neural network algorithm and screen 25 secondary indicators from the three aspects of teachers, students, and course content, thus forming the teaching evaluation index system of Probability Theory and Mathematical Statistics. Determine the evaluators and evaluation methods, according to the specific implementation of the evaluation. The evaluation model of “Teaching Probability Theory and Mathematical Statistics” based on the RBF neural network is tested for reliability and validity. The results show that the evaluation values of 25 indicators in the indicator system of the RBF-based evaluation model for the teaching of Probability Theory and Mathematical Statistics are all out of the range of 8.010-9.0, and | u 1 | = 16.392≥2.241, | u 2 | = 10.052≥2.241 in the examination scores of Probability Theory and Mathematical Statistics from the second semester of the academic year 2018/2019 to the second semester of the academic year 2020/2021, i.e., the examination scores of the course for the five semesters as a whole do not obey a normal distribution. This study enables students to better master the theoretical knowledge of Probability Theory and Mathematical Statistics, which is of great significance to the educational reform and practical exploration of Probability Theory and Mathematical Statistics in colleges and universities.
Prediction of Criminal Suspect Characteristics with Application of Wavelet Neural Networks

Abstract Crime incidents grow at a certain rate every year and become more complicated day by day, but at this stage, the development of the field of crime analysis and prediction is still lagging, so this paper applies a wavelet neural network to predict the characteristics of criminal suspects. Analyze the applicability of the WNN model, explore the principle of criminal suspect feature prediction, and study the basis of wavelet neural network analysis. Establish the face feature recognition framework, use the Morlet function and Mexican Hat function pair to optimize and improve the wavelet neural network, and construct the model evaluation index for evaluating the model prediction effect. Preprocessing the suspect feature point data by using the grayscale reflection in Haar features. Simulation experiments are used to analyze the performance of different algorithms and the prediction effect of criminal suspect features on wavelet neural networks. The proposed algorithm in this study is superior and has a lesser number of parameters than ResNet. From the loss curves of different algorithms, the algorithm proposed in this study has a faster-descending loss curve and a smaller loss rate in the epoch value of [100, 200] interval. The overall range of the evaluation indexes of the algorithms proposed in this study is above 88%, and the highest accuracy rate can reach 94.234%, which is a good performance of the algorithms and accurate prediction.
The response aspects about the March 1st Movement of the Education Bureau of the Japanese Goverment-General of Choseon

This article examined the measures taken by the Education Bureau of the Japanese Government-General of choseon to the March 1 Independence Movement and the responses of schools. Until now, schools have been understood as a space for independence movement. However, on the other hand, it was a space where the governance policy of the Japanese Government-General of choseon could be reflected in detail. With the 2.8 Declaration of Independence of Korean international students, the Education Bureau began to respond to the independence movement in earnest. The Education Bureau strengthened contact with the police officer and instructed schools at all levels to manage students. The director of the Education Bureau visited Tokyo to prevent the influence of the 2.8 Declaration of Independence from affecting colonial choseon. However, this preliminary response failed. After the start of the March 1st Independence Movement, the Education Bureau strengthened contact with the school authorities at provincial level and the government school. the Education Bureau crack down the school staff and students who participated in the Independence Movement and prevent school students strike and Independence Movement. In addition, foreign-run religious schools were cracked down. After the March 1st Movement, the Education bureau's status rose. As it became a department that manages religion and schools, it was in charge of post-processing of the March 1st Movement. On March 1, 1920, the private school regulations were revised to respond to the Independence Movement to commemorate the first anniversary of the March 1st Movement. Based on these changes, three foreign principals of private religious schools were canceled.
Behavioral biometric authentication on smartphone using keystroke dynamics

Over the past few years, smartphones have revolutionized our life. Authentication plays important role as all private and business data are stored in smartphone. Password authentication is not secure technique as password can be easily stolen by hackers. Hence, need to use advance technology to increase the security of authentication. Keystroke Dynamics is behavioral based authentication technique. It identifies the user identity through their typing behavior to check user is genuine or imposter. In this paper author is presenting a KDSP system for Smart devices to increase the security of authentication at login time. On the basis of timing feature set, typing speed, flight time and error rate of keystroke dynamics author is checking the identity of the user. Proposed system works on all the Android smartphones and tablets. All experimental work was performed on 268 subjects to capture keystroke data on server. In Statistical analysis, Equal-error-rate (EER) is measured 7.415% of the 60 subjects for False acceptance rate (FAR) and 208 subjects for False rejection rate (FRR).
The Principles of Escalation and Escalation Management

This chapter discusses the historical and theoretical antecedents to modern escalation management theory. The author argues that escalation can only be managed, not controlled, by understanding its nature, understanding actions that might trigger opponent escalations, and avoiding taking those actions, even though one's own risk and escalation calculi might not see them as important or compelling.
Escalation Management in Practice

This chapter summarizes the implications of the case studies for escalation and escalation management. It validates the going-in expectation that escalatory decisions would reflect complex webs of cultural, geographic, political, technological, and military considerations in different ways in different contexts. But, in general, this section finds that these considerations often can be assessed before and during peer-on-peer air wars, and these assessments can enable opponents to reduce the likelihood that their actions will cause unexpected and undesired escalations by their enemies. The key to managing escalation will be to avoid tripping the escalatory triggers of one's enemies, rather than to rely on increasing force to dissuade them from responding with their own escalatory actions.
Run’n’Gun

I learned to play ball on the rez, on outdoor courts where the sky was our ceiling. Only a tribal kid’s shot has an arc made of sky. We balled in the rez park against a tagged backboard with a chain for a net, where I watched a Hualapai boy from Peach Springs dunk the ball in a pair of flip-flops and slip on the slick concrete to land on his wrist. His radius fractured and ripped up through his skin like a tusk, which didn’t stop him from pumping his other still-beautiful arm into the air and yelling, ...
Compressing the Trees of Canonical Binary AIFV Coding

Canonical binary AIFV coding [1] contains two trees T <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> and T <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> . We show the method to compress T <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> , and the method to compress T <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> is with a similar way. We provide a new method to store the number of leaves, master nodes and complete internal nodes in each layer and compactly encode the string of numbers according to the specific property between the nodes.
Exploring the Application of Large Language Models in Detecting and Protecting Personally Identifiable Information in Archival Data: A Comprehensive Study*

This comprehensive study investigates the application of Large Language Models (LLMs) for detecting and protecting Personally Identifiable Information (PII) in archival data, a pressing concern for archives under the mandate to increase public access while safeguarding personal privacy. The paper juxtaposes traditional supervised learning methods against LLMs’ unsupervised capabilities in PII detection, unveiling LLMs as viable alternatives capable of achieving satisfactory performance levels without the need for extensive training datasets. Through empirical analysis, the study validates the feasibility of LLMs in identifying sensitive information within large volumes of archival material. The findings highlight LLMs’ significant interpretability, providing understandable rationale behind PII identification—a feature that not only enhances trust in AI applications but also aids archival staff in the review process. This research contributes novel insights into the intersection of AI and archival science, presenting LLMs as powerful tools for addressing the twin challenges of data accessibility and privacy.
Bookbuddy: A Mood Based Book Recommendation System

Nowadays, with the abundance of books available, readers often struggle to find books that align with their personal interests and moods. This paper proposes a personalized reading system that leverages machine learning techniques to provide book recommendations based on users' individual preferences and emotional states. The system integrates popularity-based, collaborative, and mood-based filtering approaches to generate highly personalized recommendations that cater to users' unique reading interests and mood. To facilitate user engagement and interaction, the system incorporates a database of books, reviews, and ratings, and allows users to access E-Books for free and purchase hard copies if desired. The paper outlines the system's design and implementation, highlighting its potential to improve users' reading experience and encourage exploration of new books and authors. The proposed system represents a significant contribution to the field of personalized recommendation systems and demonstrates the effectiveness of machine learning techniques in generating tailored recommendations.
CERBERE: Cybersecurity Exercise for Red and Blue team Entertainment, REproducibility

Experimenting in cybersecurity requires manipulating reliable and realistic data. In particular, labelled data derived from the observation of a complete campaign is rarely available, due to its high sensitivity and the difficulty of accurately labelling datasets. This situation harms the reproducibility of research results and therefore to their impact. In this article, we present the CERBERE project that addresses this issue through a reproducible attack-defense exercise and a labelled dataset usable for research purposes. The attack-defense exercise is first composed of an exercise for red teamers automatically deployed with variable attack scenarios. Second, an exercise for blue teamers can be operated using the system and network logs generated during the attack phase. We provide with this article, the software to rebuild the infrastructure for red teamers. We share a labelled dataset where we spot the ground truth, i.e. the log lines that have been involved in the attacker’s actions.
CLIP-PubOp: A CLIP-based Multimodal Representation Fusion Method for Public Opinion

Vision Language Pre-training (VLP) has made significant progress in the field of universal multimodality in recent years. Universal multimodal datasets (such as MSCOCO, Flickr30k, etc.) have become one of the standards for evaluating VLP models which rely on images and corresponding captions for representation modeling. However, in a public opinion event, besides image captions, it also includes other texts such as content texts and comments, which may have a positive impact on image-text representations of public opinion. In this paper, we propose the method to explore the positive effect of content texts on initial multimodal representations. We name our model CLIP-PubOp, which is based on CLIP, a famous VLP model using contrastive learning. We add a linear fusion layer before the fusion of image and text representations, which fuses event content text representations and caption representations in a certain proportion to obtain enhanced text representations. On this basis, multimodal representations can be obtained by fusing enhanced text representations with image representations. We crawl through four mainstream categories of public opinion events online as our datasets and conduct experiments on both Chinese and English version of datasets. The experimental results show that content texts of public opinion events have a significant positive effect on multimodal representations, with an average accuracy improvement of about 2%-10% in image-text retrieval tasks.
Research on Computer Remote Network Multi-Person Video Connection Intelligent Network Training System

In this paper, a remote video conferencing system based on cloud video is proposed and implemented. The system uses a mesh transfer card for MV-440 single channel high definition MPEG4 images. This paper introduces the design and implementation of online video system based on DELPHI+ VDK. The main functions are high-definition video, camera control, data processing, image adjustment, sound Settings and different software styles. This method fixes images that have been damaged on the network by resending and FEC encoding. The image quality has been greatly improved in the unsatisfactory network environment. The experiment proves that the multi-person online intelligent network training system has the characteristics of small load, good controllability and remarkable management effect.
Research on Flipped Classroom System of Industrial English Based on Computer Intelligent Network Technology

With the continuous development and progress of computer network technology, this article utilizes J2EE technology to develop a new network platform on this basis. A new method based on computer-aided design was proposed and experimentally validated. After practical application, this system not only provides a convenient interface for online communication between teachers and students, but also has functions such as user registration, querying course information, online exams, uploading, and downloading homework. The application of this method will effectively promote the implementation of English flipped classrooms and play a positive role in cultivating and cultivating college students' autonomy.
Image Piracy Detection on Social Media Platforms Such as Instagram and Facebook

Image piracy detection is an important issue on social media platforms like Instagram and Facebook. It refers to the unauthorized use of someone else's images without their permission, which can lead to copyright infringement and violation of intellectual property rights. Having an app that detects image piracy can be very useful for photographers, artists, and other creators who rely on their images for their livelihoods. Such an app could help to protect their work and prevent others from profiting from their creations without their consent. Overall, an app that detects image piracy on social media platforms like Instagram and Facebook could help to protect the intellectual property rights of creators and prevent unauthorized use of their images.
Marketing Inspection and Monitoring System for Power Grid Enterprises Based on Electricity Regulation

Research and development of electric power marketing online audit system. Using Java language, SSH framework, data warehouse, AJAX and ORACLE database design. The four functional modules of running state management, power supply quality and emergency disposal monitoring, operating results monitoring and work quality monitoring are completed by using the above technology. Secondly, the power marketing data mining method based on clustering is studied. Firstly, the data is input, and then the basic method of data mining is designed. A method of SLIO for discrete domain is presented to obtain useful data. Through the simulation analysis, the system realizes the abnormal early warning and closed-loop rectification. The experiment shows that the method has high efficiency.
A Combined Cryptography and Error Correction System based on Enhanced AES and LDPC

Any military organization nowadays depend on networks and communications. Sensitive data that is essential to national security relies on these networks and applications. As a result, the need to safeguard and protect this data arises. An authenticated crypto-coding (encryption combined with channel coding) system is developed which incorporates im-proved Advanced Encryption Standard for security and Low Density Parity Check codes for error correction. Image en-cryption and decryption is done with AES-256 with authentication. LDPC is incorporated as one of the rounds of Encryption. The entire communication system is implemented and simulated using MATLAB. Encryption-encoding-scrambling-modulation is performed on transmit side. The channel model of Additive white Gaussian noise with varying values of SNR-Signal to Noise ratio is used. The results of Bit error rates (BER), no. of errors, recovered images and histograms for dif-ferent SNR values are added. The parameters like correlation, entropy and noise variance are calculated and verified. The Enhanced encryption offers a higher security compared to conventional AES. Hence, the modified method can be used to safeguard sensitive information.
Research on Digital Mining Model Algorithm of Computer 3D Science Knowledge Graph Visualization Technology

Through the visibility of knowledge map and the use of map visibility to express the correlation between different types of information. Firstly, the paper mines the multimodal data related to the field of computer science and establishes a more systematic multimodal knowledge base. Combined with association extraction rules, association extraction was carried out. A multimodal computer domain knowledge map which can be automatically extracted from relational ternary associations is constructed. The simulation results show that this method can be used to integrate academic resources in a deep level. It provides advanced knowledge services to the online community.
Fine Tuning GANs for Image Colorization: Effects of Training Data and Epochs

Ever since the advent of Deep Learning, there has been a lot of experimentation and research being carried out in image processing. This gave rise to the conversion of black and white i.e. grayscale images into colorized images. Image colorization is a popular image-to-image translation problem. It finds its application in various domains like image restoration, movie colorization, colorization of old aerial surveillance shots, medical image colorization, etc. Earlier this was done manually by artists and photographers but recent advancements in deep learning have enabled automatic image colorization using techniques like Convolutional Neural Networks and Generative Adversarial Networks. CNN is a neural network that has layers that are essential for extracting features from images and can learn the complex mapping between grayscale images and their corresponding ground truth images. On the other hand, GANs is a relatively new technique that consists of two components: generator and discriminator. This research paper aims to present the results obtained by using both the above-mentioned methodologies for image colorization. Many examples of generated images and their respective accuracies and structure according to the technique used are discussed in the later part.