Reviewerly/exHarmony huggingface dataset:
A Secure Client Video Deduplication Scheme Based on 3D CNN

With the advent of the era of big data, users are generally used to uploading local data to the cloud server to reduce the pressure of storage. This will lead to many duplicate data in the cloud server and form a large number of redundant, especially the existence of videos. To solve the problem of wasting storage space for duplicate videos, on the one hand, we propose a two-stream 3D CNN combined with triple metric method to construct a duplicate video detection scheme, when the threshold is 3.6, the recall rate can reach 97.1905%, while the false positive rate is only 0.0743%, which is better than the I3D deduplication scheme. Through the Chamfer distance matching algorithm for two-stream video features, compared with the duplicate video detection model based on I3D network with EMD distance in terms of false positive rate, recall rate and time cost, it achieved a good video deduplication effect. In addition, some videos are used to test the deduplication performance of Hamming distance, EMD distance and Chamfer distance, the results show that the Chamfer similarity distance algorithm has a higher recall rate and a lower false positive rate than the other two methods. On the other hand, we propose a video ownership proof scheme based on random block fusion. The server and the client respectively perform random cropping operations and fusion on the video frames, then generate verification tags and ownership evidence. Finally the server calculates the distance to determine whether the client can pass the authentication. The experiment results show that the scheme has less time and storage overhead and can resist the spoofing attack of criminals.
Joint Modeling of Chinese Minority Language Translation Tasks

Neural Machine Translation (NMT) normally requires a large amount of parallel corpus to obtain good performance, which is often unavailable for minority languages. Current methods normally pre-train seq2seq models on monolingual data in a denoising manner and then fine-tune the parallel data to improve the performance of low-resource translation. But minority languages used in adjacent areas may co-relate with each other, and jointly modeling them may lead to better performance. In this paper, we propose to improve the performance of Chinese minority language translation with Multilingual NMT (MNMT). As the tokens of the minority languages are not covered by either Chinese BART or mBART and the vocabulary size of the multilingual data exceeds that of the pre-trained model, we map the vocabulary of minority languages to that of the pre-trained BART according to the frequency and enlarge the BART vocabulary by repeating low-frequency tokens respectively to address them. Our experiment results on the CCMT 2023 Chinese minority language translation tasks show that joint modeling can improve the Uyghur-to-Chinese and the Tibetan-to-Chinese tasks by +2.85 and +1.30 BLEU respectively with BART base, and lead to BLEU scores of 55.48, 53.52, and 48.26 on the Mongolian-to-Chinese, Tibetan-to-Chinese and Uyghur-to-Chinese translation tasks respectively with BART large.
A Multi-Model-Based Approach to Corpus Text Pre-Screening

The construction of a self-built corpus for a research topic necessitates the prescreening of acquired texts. During the construction process, certain acquired texts may not be directly relevant to the research subject due to various influencing factors. While both manual and algorithmic methods can be employed for pre-screening, manual screening becomes impractical when dealing with a large volume of text. As such, we propose a multi-model-based approach utilizing TextRank, TF-IDF, and KNN algorithms for pre-screening corpus texts. And the effectiveness of this method will be validated through rigorous evaluation.
Action Recognition Method Based on Graph Neural Network

Currently, action recognition methods based on human skeleton features encounter challenges in obtaining the human skeleton and are susceptible to occlusion and illumination. Conversely, image-based methods incur high computational costs. To solve these challenges, this research suggests a novel action recognition method utilizing graph neural network. The method aims to reduce computational expenses while leveraging human body structure information in images. Firstly, we create a spatial feature extraction model that constructs video frame images as graphs, proposing a construction method. Then, we utilize the GCN to extract spatial information about the human body structure. The Temporal Transformer is employed to extract spatial information between multiple video frames, ultimately producing action recognition classification results. On two frequently used action recognition datasets, we empirically validate the approach and assess its performance in comparison to other state-of-the-art action recognition approaches. The experimental results showcase the effectiveness of the proposed method, improving performance while minimizing computational consumption.
Prompt-Based Translation of Chinese into Taiwanese Mandarin Braille

In automated Braille translation, accommodating linguistic nuances and the rules peculiar to Braille across various languages poses considerable challenges. Mandarin Chinese stands out in this aspect due to its necessity to ascertain the appropriate pronunciation of characters based on context. Although rule-based algorithms have historically dominated this space, recent empirical evidence highlights the efficacy of statistical approaches and the emergent exploration of Large Language Model (LLM)-based techniques. This paper explores the potential advantages of leveraging a prompt-based strategy for the automated translation from Mandarin Chinese to Taiwanese Mandarin Braille. As a methodology, we devised a script capable of ingesting a Chinese sentence and subsequently generating a prompt that comprises the Zhuyin of unequivocal characters and dictionary definitions for those with polysemous readings. Utilizing a set of 103 test sentences, we assessed the precision with which GPT-3.5, GPT-4, and Liblouis (a widely-recognized open-source rule-based Braille translator) ascribed readings to polyphonic characters. Our findings revealed that, notwithstanding certain inconsistencies in the GPT-3.5 outputs, the extended GPT- 4 model exhibited superior performance compared to Liblouis.
An Improved Method for UAV Perspective Object Detection Based on YOLOv5s

To resolve the issues caused by large-scale variations, and the small targets are easy to be missed detection and false detection from the perspective of unmanned aerial vehicle(UAV), we propose an improved algorithmic model specifically tailored for the identification of minuscule objects in UAV scenarios. The improved model is based on YOLOv5s and improved it in several elements. To develop the detection ability of tiny targets, the improved model incorporates a dedicated detection head tailored specifically for small objects. To extract as much feature information as possible from the image, the network incorporates a Bottleneck Transformer module that pays attention to multiple things at once. Additionally, improve the neck network's pyramidal structure to a BiFPN feature fusion structure by integrating the Coordinate Attention (CA) module. These enhancements significantly enhance the attention and focus on small targets. Finally, optimize the detection performance using the Eiou loss function, which leads to an improvement in detection effectiveness. Our model underwent training and testing using the VisDrone dataset. Experimental results demonstrate that it significantly improves the recall, precision, and average precision of UAV perspective object detection. Compared to the primeval YOLOv5s model, it achieves a 3.2% increase in mAP50 metric, which has superior detection performance, and effectively handles small object detection tasks in the UAV perspective.
Comparative Analysis of Language Models for Linguistic Examination of Ancient Chinese Classics: A Case Study of Zuozhuan Corpus

Exploring the comparative analysis of translation styles across languages holds great significance for capturing the essence of ancient Chinese classics. This article presents a comprehensive analysis of language models for linguistic examination of ancient Chinese classics, using the cross-language Zuozhuan corpus as a focal point. We utilize the capabilities of five pre-trained language models to compare their effectiveness with the deep learning model Bi-LSTM-CRF in the areas of word segmentation and parts-of-speech tagging. Optimal training results obtained from the models facilitated the completion of word segmentation and parts-of-speech tagging across the entire corpus of ancient Chinese classics. Building on these advancements, this research delves into a meticulous lexical-level scrutiny of the linguistic style evident in ancient Chinese classics and their corresponding English translations. In contrast to the original Chinese text, the contemporary Chinese translation exhibits greater semantic clarity, manifesting a relatively singular phrase function and a heightened diversity in vocabulary combinations. Conversely, the English translation demonstrates a tendency toward simplification. The analysis encompasses facets such as parts of speech distribution, word length variation, lexical richness, and textual density, providing unprecedented insights into the cross-linguistic nuances of these venerable literary works.
End-to-End Binocular Active Visual Tracking Based on Reinforcement Learning

Active Visual Tracking (AVT) aims at controlling the motion system of the tracker to follow the target given visual observations. Existing works have achieved significant breakthroughs by leveraging deep reinforcement learning. However, these methods rely solely on a single visual observation image, and the tracker can only infer relationship between the position of itself and the target according to the size of target in observed images, lacking ability to perceive the distance of the target. Furthermore, environmental background interference poses challenges for the tracker. To addresses these issues, we propose a binocular stereo matching method for AVT. The disparity feature of the target is obtained by stereo matching of the binocular images to perceive the distance of target. To address the problem of background interference, we introduce template matching into active visual tracking to embed target template information into the state representation, thereby enhancing the ability of the tracker to identify objects. To ensure stable policy training, we adopt an asymmetric training mechanism that utilizes true state information during training, further improving tracking performance. Experimental results show that our method achieves better results in different test scenarios compared to baselines.
Analysis of CART Algorithms in Data Mining

In the information age, with the continuous growth of massive data, the demand for processing data to obtain information in various fields is increasing. Among various data mining methods, CART, the classification and regression tree, stands out as an intuitive and efficient decision tree algorithm. It is composed of feature selection, tree generation, and pruning. Gini is selected as the index in classification and MSE in regression. After recursively constructing a binary tree, use the criteria to pruning, and finally, a good tree is obtained. Moreover, in section 3, we exemplify CART is used for selecting the best features to classify data, whether it is discrete data or continuous data. In addition, we use UCI data to conduct experiments to demonstrate that the CART algorithm after feature selection has a higher dataset classification accuracy rate. From this paper, what can be found is that the CART algorithm is useful, and it is worth combining other methods to continue to study more applications.
Chinese Stylistic Competence: Evaluation Method and Datasets of Large Language Model's Performance

Stylistic competence is an important pragmatic competence, and adequate stylistic competence is required for large language model (LLM) to land in language life. In this paper, stylistic competence is defined as the ability to use appropriate style for communication in a specific register, and based on this, three tasks of stylistic classification, stylistic generation, and stylistic transformation are designed to evaluate the Chinese stylistic competence of LLMs represented by ChatGPT. It is found that LLMs have their own advantages and limitations in different tasks and styles. GPT-4 demonstrates the most comprehensive and excellent Chinese stylistic competence, ChatGPT3.5 and ERNIE Bot have better performance, ChatGLM-6B and SparkDesk have unstable performance and notable shortcomings, with their overall abilities being somewhat lackluster. In addition, the degree of informality of the texts generated by each model is relatively limited, the literary grace is ordinary, and problems such as consistency errors, normative errors, factual errors, illogicality, insufficient sentence fluency, and obvious traces of machine translation still exist. For LLM, it should be viewed from an instrumental perspective, expanding its stylistic competence with rich and diversified stylistic data resources and technological advances, and at the same time reasonably utilizing and giving full play to its stylistic resources attribute, to make it better serve the language life.
A Double Diagonal Ratio Metric for Tiny Object Detection in Aerial Images

In recent years, with the development of deep learning and the maturity of object detection technology, unprecedented progress has been made in aerial image object detection tasks. But for detecting tiny objects with little pixel information and easily confused with the background, the general-purpose detectors still do not achieve good performance. Through analysis, we found that, in addition to the defects of the tiny objects themselves, the sensitivity of the most commonly used IoU-based metric to tiny objects is one of the main reasons for the poor detection performance. Therefore, we design a new metric, Double Diagonal Ratio (DDR), which alleviates the poor performance by using a first power line segment ratio instead of a second power area ratio. Note that, DDR is simple in design and can be easily embedded into almost all detectors assignment, non-maximal suppression, and loss function modules to replace IoU. In addition, DDR does not introduce additional hyper-parameters and has stronger robustness. We evaluate our metric on the AI-TOD dataset, and extensive experimental results show that DDR significantly affects the detector performance improvement, especially on tiny objects.
Design and Implementation of Multi-interactive Intelligent Teaching in Exhibition Design Courses

We discuss the design and implementation of multi-faceted interactive intelligence instruction for exhibition design courses. Our intent is threefold: 1) adapting the new humanities and social sciences to the undergraduate training requirements of applied higher education institutions. 2) combining course characteristics, teaching processes and teaching objectives, and 3) developing a smart teaching model for the curriculum that use “Chaoxing Xuexitong” intelligent terminals. Because successful implementation requires both leadership from teachers and a collaborative effort from students, we review the development process of online teaching to identify the advantages of online teaching platform construction. We also consider how teaching organization and information, and the design perspective can be optimized using the Chaoxing classroom teaching platform. This mode of delivery yields benefits via the optimization of teaching effectiveness while providing a model that can be used to explore intelligent teaching of information technology in exhibition design courses.
Rethinking of 2D/3D Medical Image Segmentation Based On Classical Unet

Image segmentation is critical for the building of medical assistance systems. However, it is challenging for the complex characteristics of medical images, such as high noise, low resolution, and insufficient data. In this paper, we conducted comprehensive research on both 2D and 3D Medical Image Segmentation (MIS) methods. For 2D MIS tasks, we proposed a light-weighted network based on a classical network for medical images, U-net, incorporating a multi-scale feature fusion module. This network has been trained on the Synapse multi-organ dataset and compared with two novel Transformer-based 2D segmentation networks, demonstrating the Transformer's effectiveness in this task. We have applied well-done data preprocessing for 3D MIS tasks and trained a 3D-Unet on the BraTS2021 dataset, achieving good performance compared to the SOT A methods in this benchmark. Furthermore, we thoroughly discussed the 2D and 3D medical image segmentation tasks based on the experiment results.
Research on Recognition Textual Entailment with Integrated Attention Mechanism and Lexical Information

Recognition Textual Entailment is a fundamental research direction in natural language processing. However, due to the complexity of sentence structure and the multiple parts of speech of words, it affects the model's deep understanding of sentences. From a linguistic perspective, different parts of speech have different understandings of sentences. We believe that a complete sentence consists of two parts: sentence structure and syntactic structure. In response to this issue, this article proposes a recognition textual entailment model that integrates attention mechanism and part of speech tagging. NLTK (Natural Language Toolkit) is used to annotate the part of speech of sentences, and the part of speech information is interactively fused with the sentence features extracted by the Conformer model. After part of speech tagging, the model can better grasp the syntactic structure of sentences. Experiments have shown that the above method combines sentence structure and syntactic structure, which can effectively enhance the model's understanding of deep semantics and improve its accuracy compared to classical models.
Rating Prediction of Multi-Aspect Reviews Using Simultaneous Learning

This paper proposes a method for rating prediction tasks in review documents. Existing models for predicting the rating scores assigned to each aspect were often developed separately. However, there is a relationship among aspects. Therefore, we apply a multi-task learning framework to our prediction models. We evaluate our model with a review dataset about a game software domain written in Japanese. Each review document contains six-level rating scores for seven aspects of the game software. We utilize BERT as the backbone model for the prediction. Our model simultaneously learns the parameters of seven BERTs for seven aspects. Hence, we name this method “Simultaneous Learning.” It leads to the improvement of the performance of the prediction. In addition, we compare two types of input data for the rating prediction task: all sentences and selected sentences. Experimental results show the effectiveness of our simultaneous learning model in the multi-aspect rating prediction. Our simultaneous learning method obtained 0.713 on RMSE. It demonstrated a 0.046 improvement in RMSE, as compared with non-simultaneous learning. Further, we confirmed that our method based on simultaneous learning was effective in the case that the number of sentences related to an aspect is small.
RADM-DRE:Retrieval Augmentation for Document-Level Relation Extraction with Diffusion Model

Existing data augmentation methods attempt to utilize more raw samples or incorporate external knowledge to enhance the model, with the assumption that the explicit data pool for retrieval must be accessible in both training and testing stages. We argue that the data generated from the distribution of raw data beyond the raw data itself can provide more informative augmentation and can relax the strong assumption that the original raw data must be accessible in testing stage. To address this issue, we propose a novel framework that introduces a diffusion model for the first time. The Diffusion Model aims to generate data with diversity by directly inheriting the attribute of diffusion model from the data distribution, serving as a data generator. However, the raw text is discrete which is hard to generate via diffusion model directly. Thus, we model the original data in a transformed continuous embedding space, and conduct retrieval from that data distribution. Then, we concatenate the retrieval results with the original features for augmentation. Experimental results on the public datasets DocRED, CDR, and GDA demonstrate promising performance.
Front-End Fusion and Large-Scale Weakly Supervised Decoding Module Based Myanmar Speech Recognition

End-to-end (E2E) speech recognition based on deep neural networks has become the mainstream approach for building high-performance speech recognition systems. Training E2E models relies on large-scale datasets of “audio- text” pairs, which presents challenges for researching speech recognition in non-general-purpose languages under extremely low resource conditions. Unsupervised pre-trained models have achieved good performance in many low-resource automatic speech recognition and have been widely applied. In this paper, under extremely low-resource conditions, we build a baseline system for Myanmar language speech recognition based on speech self-supervised learning front-end models. Moreover, we explore methods to further improve the speech recognition performance for Myanmar language based on this baseline system, and propose and implement two optimization methods: (1) linear fusion of acoustic spectral features with self-supervised speech representation features to extract richer and more accurate speech features, and (2) introducing a multilingual multitask weakly supervised pre-trained Whisper model as the decoder module for Myanmar language speech recognition system. Experimental results show that the Myanmar language speech recognition baseline system constructed in this study has a character error rate of 21.5%. After introducing the proposed improvement methods in this study, the character error rate decreases to 16.0%.
Data Mining Based on Student Campus Card Consumption Information

This study focuses on the consumption data of the campus card of students in Qingyuan Polytechnic. By cleaning and analyzing the original data exported from the campus card center, the consumption patterns of students were obtained and provided reference for optimizing the school's management. We exported a total of 2,932,536 original consumption data from the campus card center for one academic year, imported the data into a database, cleaned and analyzed it using SQL statements, and conducted student consumption clustering analysis using the k-means++ algorithm. We used numpy, matplotlib, and other tools to create visualizations of the data. The analysis mainly focused on the overall consumption situation of students, daily consumption patterns, meal consumption patterns, consumption locations, and consumption clustering. We proposed some suggestions for optimizing student services based on the analysis.