Reviewerly/exHarmony huggingface dataset:
Feature learning model based on Feature space constraints and Self-Attention learning

Supervised time series anomaly detection is of significant importance in both machine learning research and industrial applications. Autoencoder-based methods have achieved outstanding performance in this field. However, these methods often suffer from the challenge of limited separation between low-dimensional representations of normal and anomalous data. To address this issue, this paper introduces a model called FFSA, which utilizes feature space constraints and self-attention. The FFSA model is built on the foundation of a temporal network-based autoencoder, featuring an information entropy feature learning module and a self-attention feature learning module. The information entropy feature learning module comprises an MLP mapping layer, layer normalization, and an entropy analysis layer, while the self-attention feature learning module includes layer-wise parameter extraction and concatenation, layer normalization, and a self-attention mechanism. These modules work together to increase the differentiation between normal and anomalous data and to extract more informative low-dimensional representations. Experimental results on multiple public datasets demonstrate that the FFSA model outperforms mainstream anomaly detection models, achieving an impressive F1 score of up to 97% on the KDDCUP dataset.
Self-Paced Self-Training Based on K-Nearest Neighbors

Self-training is a classical semi-supervised classification method, which iteratively trains labeled samples that are enhanced using unlabeled samples. However, traditional self-training selects pseudo-labeled examples without distinguishing between different classes. Furthermore, wrong pseudo-labeled samples accumulate, leading to an increase in learning bias. In this paper, we propose a self-paced self-training based on K-nearest neighbors (SPST-KNN). SPST-KNN is composed of three main components: sample difficulty calculation, sample type determination, and self-paced selection based on KNN. The types of examples, including safe, boundary, and outlying examples, are determined by KNN. According to the principle of self-paced learning, SPST-KNN preferentially selects easy pseudo-labeled examples in each sample type to be added into the labeled sample set. Experiments and ablation study on 20 real-world datasets have demonstrated the superiority and effectiveness of the proposed SPST-KNN.
Modelling Coarse- and Fine-grained User Representation for News Recommendation

News recommendation systems help users quickly access content of interest from a vast amount of news. The key challenge lies in obtaining accurate user and news representations. Existing news recommendation methods typically model the entire sequence of user interactions with news, which makes it difficult to capture fine-grained user representation as user interests typically change over time. In this paper, we propose two different granularities of user representation: fine-grained user representation obtained from subsequences and coarse-grained user representation obtained from the entire user browsing sequence. We combine the fine-grained and coarse-grained user representation for news recommendation (CoFi-Rec). Our approach in news recommendation has been extensively validated through experiments conducted on two realistic datasets, MIND-small and MIND-large. These experiments demonstrate the effectiveness of our approach when compared to 7 state-of-the-art news recommendation methods.
Instance Discrimination for Improving Chinese Text Classification

Due to the information technology industry's rapid development, an abundance of Chinese text data has emerged on the Internet. Extracting valuable information from this data through Chinese text classification technology has become a highly significant topic. Despite the fact that pre-trained models for text representation have achieved the best results possible for a number of Chinese text categorization problems, they often encounter classification errors when faced with highly similar texts that require different labels, resulting in performance degradation. This issue is particularly pronounced in Chinese due to the substantial differences between Chinese and English. Even a slight difference in a Chinese character can lead to completely opposite semantic meanings in a sentence. To solve this issue, we propose a straightforward multi-task learning model that incorporates instance discrimination. Additionally, we perform supplementary training on BERT using instance discrimination as an intermediate task. In essence, our objective is to enhance the model's ability to acquire stronger and more diverse text representation vectors. We conducted experimental verification on three Chinese datasets, and the results demonstrate that our proposed method surpasses relevant single-task learning classifiers and other advanced Chinese text classification techniques.
Transmission Optimization Based on Industrial Internet Information Model

With the standardized development of industrial Internet, the communication among industrial Internet platforms, devices and gateways is more frequent. Due to the problems of heterogeneous data and low data interoperability existing in different devices, the communication between industrial Internet is hindered. With the development of industrial Internet information model, on the basis of reducing data heterogeneity and improving interoperability, it is more difficult to transmit information due to the increase of information content. Therefore, this paper will design an information model transmission optimization method, through the information model XML file content optimization compression, while in the information model may exist in the Chinese information processing, so that it can better compress the Chinese-English information. The results show that the XML file used in information transmission can be compressed by using this optimization method, and it is proved that the proposed optimization method can improve the transmission efficiency.
Research on Deep Neural Network Testing Techniques

Profiting by the rapid development of computer science and technology, deep neural networks have been widely used in security-related fields such as face recognition, automatic driving, medical diagnosis and decision-making reasoning, and there is an urgent need for testers to conduct comprehensive and in-depth testing of these software to ensure their quality and security. However, intelligent software based on neural networks is fundamentally different from traditional software. In recent years, more and more researchers have shifted their attention from traditional software testing to intelligent software testing, and a series of evaluation criteria, test frameworks, and test case generation methods, etc. have been proposed for deep neural network models. This paper summarises and concludes the existing research from the perspectives of testing techniques based on test adequacy theory, testing techniques based on traditional testing theory and testing techniques based on adversarial samples. Finally, it summarises and looks forward to deep neural network testing and points out the problems in deep neural network testing, in order to provide some thoughts for researchers in related fields.
Grouped Federated Learning Algorithm Based on Non-IID Data

Federated learning is a new machine learning paradigm in which multiple clients collaborate to train a machine learning model while protecting local data privacy. Client-side non-identically and non-independently distributed (Non-IID) data is one of the most important challenges for federated learning algorithms. The classification accuracy of the federated learning model drops drastically due to the weight dispersion caused by data heterogeneity. In this work, we adopt a way of dividing client data that is closer to real applications. We propose a federated learning algorithm based on rearranging all clients(FedRC). FedRC sets up a novel within-group training approach and a server-side pre-aggregation policy to address the impact of Non-IID on the model. In addition, we test FedRC on a simulated dataset and the experimental results demonstrate that FedRC outperforms the baseline algorithm and significantly improves the performance on Non-IID data.
An Experimental Research of image enhancement for old Grayscale photos of China

Improving the quality of old pictures are very meaningful both for national or international science and technology development and for the life of ordinary people. We analyzed an old photo of the first author's relative. We first apply median filters to remove many salt and pepper noises without losing too much details of the original photo. For other kinds of damaged areas in photo, we proposed an approach to reduce the damaged areas. The experimental results show that the quality of the processed photo is improved in some degree as opposed to the original one. We will continue to apply or propose other image processing method to our old photo to get better result further in future.
Sentiment Analysis of Chinese Comment Text Based on CNN-BiGRU-ATTENTION

Sentiment analysis for Chinese comment data is a hot research topic today, and has significant implications for various fields of social life such as business, government, and education. This article selects comment data from 10 categories of products, integrates attention models, and proposes the CNN-BiGRU-ATTENTION model for sentiment analysis of Chinese comment data. Extract text features through CNN and BiGRU layers, and then introduce the extracted features into the attention mechanism to complete feature extraction of text information. Finally, obtain classification results through the Softmax layer. The experimental results show that the proposed model has improved accuracy compared to CNN, Text-CNN, GRU, BiLSTM, and CNN-ATTENTION models.
UAV Air Combat Situation Assessment Method based on Improved Clustering and Self-Learning Network

Air combat situation assessment is an effective prerequisite for autonomous decision-making and control of unmanned aerial vehicles in air combat. However, there are significant challenges in its continuity and timing design. Aiming at this difficult problem, this paper proposes a new solution from the perspective of data mining, combining specific deep learning techniques: transforming the situation assessment problem of close-range air combat into a pattern classification problem of high-dimensional data. Firstly, an improved clustering algorithm is proposed, and the actual air combat data is clustered. Through expert interpretation, a complete situation label air combat data sample set is established. Furthermore, a SAE-LVQ situation assessment model and algorithm are established using a stack type self-encoder to reduce the dimension of data, ensure the significance of data features, and use a learning vector quantization network to achieve high-precision classification effects. The simulation results verify that the proposed method achieves accurate situation assessment.
Environmental Complexity Based UAV Path Planning with Variable Step RRT

In response to the problem of three-dimensional path planning for unmanned aerial vehicles (UAVs) in complex environments, this paper proposes an environmental complexity based variable step Rapidly-exploring Random Tree (RRT) path planning algorithm. Firstly, a model of environmental complexity function is established in a three-dimensional environment to measure the complexity of UAV flight environments. Then, an evaluation function for path planning is established. Based on the environmental complexity function and the evaluation function, the optimal step size for the RRT algorithm is found with the genetic algorithm. With a large number of simulated random environmental variables and the corresponding optimal step size, the environmental complexity based RRT step size function is identified and established that represents the relationship between the optimal step size of the RRT algorithm and the complexity of the environment. Based on the sliding window and the environmental complexity based RRT step size function, the variable step RRT path planning for UAVs is constructed. This algorithm can dynamically select the optimal step size according to the local environment, improving the overall performance of UAV path planning algorithms.Finally, comparative experiments are conducted to verify that the proposed algorithm is superior to the traditional on
A High-Capacity Adaptive Image Steganography Algorithm Based on Three-Shell Matrices

With the rapid development of the Internet, privacy protection has become a very important need. In order to improve the embedding capacity of carrier images, this paper proposes an image steganography method based on a three-layer shell matrix, where each secret message is adaptively fitted to each overlay pixel pair and the secret message can be extracted completely by the image receiver. Various experimental results show that the method obtains a higher capacity of 4.0 bpp and an average PSNR of 33.25 dB, which has advantages over other existing adaptive image steganography schemes. In addition, experimental results such as NCC structural similarity analysis, JPEG compression analysis, and normalised absolute error (NAE) clearly demonstrate the robustness and security of the proposed method.
A graph comparison learning recommendation algorithm based on knowledge graph enhancement

The recommender system's overall suggestion results may be erroneous and weakly robust due to the sparse interaction behavior between users and items in the system and the noise inherent in interaction samples. This research suggests a graph comparison learning recommendation method based on knowledge graph augmentation to address the aforementioned issues. First, a knowledge graph-based data improvement method is created to make use of the rich entity attribute data and help the recommendation system solve its data sparsity issue. In order to help the model better capture the crucial information contained in the nodes and to lessen the effect of noisy links between interaction samples on the quality of node representation, a graph self-attentive augmented network is also proposed. In order to test the algorithm's efficacy, experiments are carried out on three datasets: Yelp2018, Amazon-Book, and MIND. The results show that the recommendation algorithm is effective in enhancing recommendation accuracy in the case of small sample sizes and the presence of noisy data scenarios. The Recall metrics are improved by 8.59%, 7.08%, and 15.47%, respectively, compared with the state-of-the-art model.
PELATIHAN PEMBUATAN KONTEN BERBALAS PANTUN MEMANFAATKAN YOUTUBE DAN TIKTOK DI SMA SRIJAYA NEGARA UPAYA REVITALISASI SASTRA LAMA BAGI PELAJAR MILENIAL

&#x0D; The times have begun to erode the culture of reciprocating pantun. Many young people nowadays think that reciprocating Pantun is out of date. The purpose of this service community was to conduct training on creating online video content reciprocating pantun by utilizing YouTube and Tiktok social media at SMA Srijaya Negara to revitalize old literature for millennial students. The researchers carried out the implementation of Community Service (PkM) virtually. A total of 26 students of class X IPA 2 participated in this training. They uploaded the video collection of reciprocating pantun on the official YouTube of SMA Srijaya Negara and students' TikTok accounts as the product of this community service. The results of the service community showed that the students of Srijaya Negara High School could create 13 videos of content reciprocating pantun with various concepts after participating in the training.
TAPER-WE: Transformer-Based Model Attention with Relative Position Encoding and Word Embedding for Video Captioning and Summarization in Dense Environment

In the era of burgeoning digital content, the need for automated video captioning and summarization in dense environments has become increasingly critical. This paper introduces TAPER-WE, a novel methodology for enhancing the performance of these tasks through the integration of state-of-the-art techniques. TAPER-WE leverages the power of Transformer-based models, incorporating advanced features such as Relative Position Encoding and Word Embedding. Our approach demonstrates substantial advancements in the domain of video captioning. By harnessing the contextual understanding abilities of Transformers, TAPER-WE excels in generating descriptive and contextually coherent captions for video frames. Furthermore, it provides a highly effective summarization mechanism, condensing lengthy videos into concise, informative summaries. One of the key innovations of TAPER-WE lies in its utilization of Relative Position Encoding, enabling the model to grasp temporal relationships within video sequences. This fosters accurate alignment between video frames and generated captions, resulting in superior captioning quality. Additionally, Word Embedding techniques enhance the model's grasp of semantics, enabling it to produce captions and summaries that are not only coherent but also linguistically rich. To validate the effectiveness of our proposed approach, we conducted extensive experiments on benchmark datasets, demonstrating significant improvements in captioning accuracy and summarization quality compared to existing methods. TAPER-WE not only achieves state-of-the-art performance but also showcases its adaptability and generalizability across a wide range of video content. In conclusion, TAPER-WE represents a substantial leap forward in the field of video captioning and summarization. Its amalgamation of Transformer-based architecture, Relative Position Encoding, and Word Embedding empowers it to produce captions and summaries that are not only informative but also contextually aware, addressing the growing need for efficient content understanding in the digital age.
AN ANALYSIS OF DIGITAL CONTENT FOR TEACHING NARRATIVE TEXT AS A CURRENT IMPLEMENTATIONS OF ICTS IN EDUCATION AT THE THIRD GRADE OF SMAN 1 SELAYAR

The purpose of this thesis was to find out the types of digital content for teaching narrative text at the third grade of SMAN 1 Selayar and to find out how an English teacher used digital content in teaching narrative text. This research used a descriptive qualitative method, the instruments used in this research are observation checklist and structured interview. The observation checklist used to observe an English teacher about the implemented of digital content in teaching process and the structured interview used to know more specific about types of digital content that an English teacher used and how it used in teaching narrative text at the third grade of SMAN 1 Selayar. The results of analysis are: An English teacher in the third grade of SMAN 1 Selayar used power point and video. The teacher used the types so good with some preparations before showing digital content, the process of English teaching used three process; first, introduction of using digital content. Secondly, show the material using digital content and third, giving questions for the students about learning material about narrative text that have shown using digital content.
Analyzing the Readability of English for Nusantara Textbook for Grade Seven Using The WebFX

English for Nusantara (EN) books are textbooks provided by the Indonesian government and serve as educational materials that teachers can use to teach and learn English in Indonesia junior high schools. Before using them, teachers must evaluate their readability since inappropriate materials can demotivate students and enable them to achieve teaching and learning goals. This study aimed to fill the gap by evaluating the readability of EN. Using a sample EN for grade seven, this study brought a research question, what is the level of readability of English for Nusantara. To achieve this goal, the researcher used a qualitative study. The researcher used a free online readability tool to measure the text readability level, the WebFX. Among the texts in EN for grade seven, the researcher took 18 texts and analyzed them using the measurement formula in the tools above. The study found that the readability level was high, and the texts were easy to read. The limitations of the study and suggestions for future research were also discussed.
CPAFPN: An Efficient Feature Fusion Model for Maritime Ship Detection in Remote Sensing Imagery

In recent years, remote sensing imagery has been widely employed in the area of maritime surveillance, environmental monitoring, and safety. Object detection has become the fundamental task in the remote sensing imagery interpreting. Feature Pyramid Network (FPN)-based models is the traditional and widely used model for object detection in remote sensing. However, these models have limitations due to their reliance on a single-layer convolutional feature extraction network with lower resolution, which restricts their capability to extract detailed image features. This limitation degrade the detection accuracy of slender and densely packed ships. To address these issues, this article proposes Cross Path Aggregation Feature Pyramid Network (CPAFPN), an enhanced architecture, which achieves cross-path feature extraction between the linear network structure and the top-down pathway layers through lateral path aggregation and enhancement modules. Additionally, it efficiently fuses and maps features using specific adaptive pooling modules. Experimental results demonstrate that the Cross Path Aggregation Feature Pyramid Network (CPAFPN) method outperforms baseline methods, achieving a 4.2% improvement on the HRSC2016 (multi-class) dataset and a 0.9% improvement on the FGSD2021 (multi-class) dataset.
Sentiment analysis of hot event comments based on deep learning

Comments on Sina Weibo are one of the main ways for users to interact and give feedback when viewing Weibo content. Some comments may use emotional language to guide public emotions and attitudes, and gather together to form an atmosphere of public opinion. In recent years, deep learning has made great progress and has been widely used in natural language processing to solve problems such as sentiment analysis, question and answer systems, and speech recognition. This article takes the recent ChatGPT popular out-of-circle event as an example. It uses Python to crawl the comment data in related Weibo, and analyzes netizens' emotional attitude towards the event and its positive and negative classification based on the BiLSTM model. Realize the emotional analysis of netizens on the event so as to grasp the public opinion situation of the network in a timely manner.
Study on learning motivation theory based on mathematical model

Traditional ideological and political education sets educational goals intuitively based on subjective experience, and lacks logical reasoning and experimental data support. In this paper, a large amount of data is collected, the data is processed by smoothing techniques, and the entropy weight method is used to rank the research individuals. The results showed that the highest score in the urban group was 96.418, the lowest score was 9.783, and the highest score in the rural group was 97.569, the lowest score was 8.711.Then cluster analysis was used to divide the two groups into four categories, the results show that the urban group is better than the rural group in the region with strong learning motivation, and the rural group is better than the urban group in the region with weak learning motivation.