Reviewerly/exHarmony huggingface dataset:
Deep reinforcement learning based multi-agent non-cooperative game strategy approach

To address the problem of deep learning of multi-agent evolution based on cooperative equilibrium strategies without considering non-cooperative interactive self-learning games and evolution, this paper introduces non-cooperative game evolution into the interactive self-learning framework based on the MADDPG algorithm and designs a multi-agent interactive self-learning game evolution method. The experimental results show that the training curve tends to stabilize and reach non-cooperative equilibrium after the training of this method. The visualized experimental results are obtained by reproducing the experimental environment on Ubuntu. It is finally demonstrated that the MADDPG method based on the noncooperative equilibrium strategy has significant learning ability in terms of multi-agent.
Fuzzy ISM Application for Designing the Logical Structure of the High-speed Railway Intelligent Dispatching System

The effective operation of intelligent dispatching system for high-speed railway (HSR) can reduce the heavy task pressure of dispatchers, thus improving the level of railway transportation organization. Since the railway intelligent dispatching system covers dispatching jobs such as driving, planning, locomotive, freight transport, passenger transport, motor train, construction and maintenance, it is necessary to fully consider their correlation when designing the logical structure of the railway intelligent dispatching system. In this paper, the main basic functions and database of railway intelligent dispatching system are extracted from Data Flow Diagram (DFD), and the logical structure of the system is clustered by using fuzzy Interpretive Structure Model (fuzzy ISM) method. After that, the basic functions are further divided, which can reduce the complexity of system design, the difficulty of system development and the running cost, and improve the system efficiency.
Research on Generative Text Summarization Fusing Multidimensional Semantic Information

Automatic text summarization is a method to streamline text content into essential information to promote quick comprehension of crucial information. Aiming at the problems of the seq2seq model in current generative summarization methods that cannot fully understand the context and the generated summary is inconsistent with the original topic, this paper proposes an automatic text summarization method incorporating multidimensional semantic information. The model is based on the pointer generation network, extracting the global semantic information with the BiLSTM encoder and extracting local semantic information with the local convolution extractor to enhance the fine-grained features of the original text. Meanwhile, to enhance the topic semantic features of the original text, the BERTopic model is utilized to collect topic words. Then the attention mechanism is used to integrate the topic information into the pointer generation network. In the training stage, we design a hybrid learning goal for optimization to avoid the exposure bias problem using the cross-entropy loss function and the reinforcement learning loss function. The experimental results show that the method significantly improves ROUGE evaluation metrics on the publicly available dataset LCSTS.
MCUNeXt: An Efficient U-Shaped Network for Pathology Image Segmentation

Accurate pathology image segmentation is a process that assists physicians in developing medical plans and evaluating the effectiveness of treatment. Due to the complex background of pathology images and many cell nuclei, manual segmentation is time-consuming and laborious, so it is important to design a model for automatic segmentation of pathology images. In this paper, we proposed a U-shaped network that can effectively improve the accuracy and reduce the model complexity. We use depth-separable convolution and convolution kernels of different sizes to admit the convolutional blocks of UNet with the aim of reducing the number of parameters, obtaining multi-scale capabilities, and being able to effectively combine global and local information. Also, the inverted bottleneck structure is proposed to be able to increase the accuracy while reducing the number of parameters. We replace pooling with convolution for downsampling and are able to retain more information. Meanwhile, our proposed method has been extensively experimented on CRAG dataset, and compared with the standard UNet, our parameter quantity is reduced by 36%, Jaccard is 3.61% higher, and our method has less hole phenomenon and sticking phenomenon, better boundary accuracy compared with other excellent methods, and the results show that our method is competitive.
Generating Talking Facial Videos Driven by Speech Using 3D Model and Motion Model

Facial expression is one of the most important features of a face. In previous works on generating talking facial videos driven by speech without additional driving information, existing models struggled to directly learn the mapping from speech to facial features, resulting in poor quality of generated facial expressions. In this paper, we propose a method for generating speech-driven facial videos using 3D models and motion capture. This method demonstrates good performance in terms of model robustness, adaptation to large head poses, and improvement of fine-grained facial expression details. We learn features from speech, reconstruct the face by fitting 3DMM coefficients using speech, and employ a motion-captured based generative adversarial network to ensure clear facial texture details in the generated faces. On the publicly available dataset VoxCeleb2, our method achieves scores of 31.22 in PSNR, 0.89 in SSIM, 19.4 in FID, and 1.96 in F_LMD, outperforming other methods. On the MEAD dataset, our method achieves scores of 30.65 in PSNR, 0.68 in SSIM, 20.5 in FID, 2.36 in SyncNet, and 2.45 in F_LMD, outperforming other methods. Experimental results demonstrate that our method effectively enhances the model robustness for speech-driven facial video generation without additional driving information.
A Fast-implemented Signal Identification Scheme for Active Blanket Jamming

Active blanket jamming has become universal in modern electronic countermeasure (ECM). The jammers transmit high-power jamming signals to attack victim radars, making it difficult to obtain useful information. Unfortunately, with the continuous development of jamming theory, increasing blanket jamming types are emerging, which further increase the difficulty for radar systems to counter jamming accordingly. In view of the current complex electromagnetic environment, this paper analyzes the signal models of five typical active blanket jammings. According to the different characteristics of these jamming forms, various features are extracted for jamming identification. Simulations have proved the feasibility of jamming discrimination with the selected features, and a blanket jamming identification scheme is thus proposed based on the decision tree strategy. It is worth noting that the identification procedure is accomplished via time-domain or frequency-domain features, which saves considerable computing resources and is therefore suitable for fast implementation in practical engineering.
A Fusion Processing Method for Multi-source Chinese Place Names based on Syllable Syncopation

To address the problem that the traditional address fusion model cannot accurately complete the address fusion of Chinese place names, this paper proposes a fusion processing method for multi-source Chinese place names based on syllable syncopation, which can accurately reflect the similarity between place names and improve the accuracy of place name fusion by changing the proportion of dissimilar parts in place names through the syllable syncopation. The accuracy rate, coverage rate and F1 value of the toponym fusion work achieved in this paper are above 99.696%, 94.860% and 97.230% respectively.
A Multi-Key Weighted Decision Outsourcing Scheme

The entropy weight method is a multi-criteria decision analysis approach that calculates the entropy value of each criterion and then conducts a full review of numerous criteria. However, the data indications used in the weight computation frequently contain sensitive information about the individuals. Data owners are reluctant to share data with others due to privacy concerns, but they are prepared to collaborate on data analysis. One of the most prominent techniques for addressing privacy concerns is to utilize public-key encryption for user data. Using the same public key for encryption, on the other hand, involves concerns. Each user should have their own public-private key pair, which is a more trustworthy technique. This work describes a unique multi-key-based weighted decision-making strategy that uses the DT-PKC multi-key cryptosystem to safeguard each user's privacy. The suggested technique involves only one round of user-cloud interaction, significantly lowering user-side computational cost. The entropy weight method is used to calculate weight in an efficient and succinct manner. The suggested approach is both efficient and secure, according to theoretical research.
Multi-Branch Decoding Medical Image Segmentation Based on Transformer

Highly accurate medical image segmentation model is crucial to assist doctors in medical diagnosis and treatment. Despite the good performance of U-Net series networks in most medical segmentation tasks, they face several limitations, such as module fails to extract sufficient features and lacks ability to capture contextual information. Therefore, this paper proposes a multi-branch decoding model. This model uses Res2Net as backbone and captures features with different receptive fields through a multi-scale context-awareness module. Furthermore, during the decoding process, a Transformer decoding branch is incorporated to perceive global information. And a high-level feature aggregation decoding branch is introduced to reduce the influence of background noise. The proposed model is experimented on CVC-ClinicDB, Kvasir-SEG and ISIC 2016. Results show that the improved model outperforms the original U-Net series networks in terms of Dice and IoU.
Keyword Spotting Based on CTC and Similarity Matching for Chinese Speech

Inspired by the end-to-end speech recognition systems and the deep learning technique, This paper presents a mandarin KWS system using an end-to-end approach, which can directly predict a posteriori of speech units. This system consists of two parts, the first part is a ASR model which is based on connectionist time classifier(CTC) and convolutional neural network and Long Short-Term Memory network. The second part is a keyword scorer which is based on lattice search space. The main difference between this KWS system and other KWS systems lies in the output letters which are syllables and corresponding keyword search mechanism. Through experiments, the results indicate that, 1) Character similarity matching method can improve the effect of keyword spotting;2) Long keywords have higher recognition accuracy than short keywords.
Text Analysis and Recognition of Emotional Content Using Deep Learning Methods and BERT

Recognizing the emotional content of Natural Language sentences can improve the way humans communicate with a computer system by enabling them to recognize and imitate emotional expressions. In this paper, deep learning, deep neural networks as well as Transformers were examined. Specifically, we designed and developed deep learning methods and BERT-based implementations for recognizing emotional content in user-generated data. Extensive experiments were conducted using these models on a variety of textual data and all the designed methods were evaluated. The results of the study show that BERT achieved the best overall performance, getting an F1-score of 0.86 on the Twitter Data dataset. Also, the Bi-LSTM with Attention Mechanism and Bi-LSTM using Word2vec performed quite well, achieving F1-scores of 0.83 and 0.82, respectively.
Chinese Medical Short Text Matching Model Based on Fine-Tuning BERT-Attention-BiLSTM

With the increasing scale of the Internet medical market, a large number of platforms have started to provide free medical diagnosis services to users. The number of questions on medical Q&A platforms is growing rapidly, and there are a lot of repetitive and similar questions, which can no longer meet the demand of users to get fast and accurate answers only by the manual answers of medical experts. To solve the above problems, this paper proposes a Chinese medical short text matching model based on fine-tuning BERT-Attention-BiLSTM (FBAB), use fine-tuning BERT model for representation of text, the attention mechanism is used to obtain the interaction information between sentences, use the Bi-directional Long Short-Term Memory (BiLSTM) to fuse the contextual information of the 2 sentences., and the feature information is further extracted by maximum pooling and mean pooling to obtain the sentence-level semantic representation, which is input to the prediction layer after stitching, and the softmax classification function is used to calculate the probability that the two sentences are similar. Experiments show that compared with the classical deep short text matching model, the FBAB model proposed in this paper shows better results on the medical short text corpus, which proves the effectiveness and feasibility of the model.
Land Productivity Evaluation Based on Data Mining

In order to objectively evaluate the indicators affecting cultivated land productivity, under the background of big data, taking Jiaozuo City as an example, data mining is used to analyze and analyze the cultivated land productivity indicators, and finally the cultivated land in Jiaozuo City is divided into three types, and four indicators that have an impact on the cultivated land types in Jiaozuo City are screened: average annual rainfall, effective soil thickness, total nitrogen and latitude.
Real-time Lightweight Hand Detection Model Combined with Network Pruning

Hand detection, often as a pre-processing step for hand keypoint localization and hand segmentation, is a critical task in gesture interaction. A fast hand detector is demanding because it saves time for downstream tasks, leading to a real-time performance in hand gesture interaction. Achieving fast hand detection is challenging, especially on resource-limited devices. In this paper, we propose a lightweight real-time hand detection model called BlazeYOLO. We reduce the model's computational cost by utilizing light modules and pruning redundancy channels. A BlazeBlock, which consists of depthwise convolution, pointwise convolution, and residual structure, serves as the building block of the model backbone. The Only Train Once method trains and prunes the entire model. The model parameters are grouped into zero-invariant groups. During training, the parameters within the same group are updated or set to zero simultaneously. Due to the channel dependency induced by depthwise convolution and residual structure, we design a pruning scheme for the BlazeYOLO model. Following the pruning scheme, the groups whose parameters are all zero can be directly pruned without affecting the model output. The pruned model is the final model and does not require additional fine-tuning. Our proposed model achieves 95.5% average precision (AP) with only 0.11 GFLOPS of computational cost, reaching a high inference speed of 78.1 FPS on mobile devices.
User Profile-Based Viewport Prediction Using Federated Learning in Real-Time 360-Degree Video Streaming

Streaming 360-degree videos has become increasingly popular due to the growth in demand for immersive media in recent years. Companies such as Youtube, Facebook, and Netflix already use 360-degree video streaming. In order to reduce the amount of data transmitted only the part of the video at which the user looks is streamed at high resolution and enabling this requires accurate viewport prediction. However, recent approaches to streaming 360-degree video do not characterize user profiles or have low viewport prediction accuracy when either historical data is unavailable for the user or when the user starts watching a new video. This paper proposes a novel approach to User Profile-Based Viewport Prediction Using Federated Learning (UVPFL) in 360-degree Real-Time Video Streaming. UVPFL profiles users based on their head movements for different categories of videos. For high viewport prediction accuracy of a new user or a user with no historical data, UVPFL bases its viewport prediction on the viewport of similar users. Testing UVPFL in 360-degree real-time video streaming has resulted in an accuracy of up to 86% for the first seven seconds of video play. UVPFL also achieved an average accuracy of up to 96% for the complete length of video play. UVPFL has outperformed three state-of-the-art available viewport prediction solutions by 1.12% to 64.9% for a 1 second prediction horizon.
Machine Learning Classification for Video Cleaning

Video data can often contain plenty of information that would be useful to analyze. However, most methods of collecting video data involve setting a camera to record for long periods of time, gathering mostly uninteresting data interspersed by sections of interesting events. Unlike past work in video cleaning which mostly deals with fixing incorrect labels, this paper explore ways to pick out the sections of interest from a mostly unlabeled video and only using small amounts of labeled data for training. Using only standard video features for generality, a variation of k-nearest neighbors is proposed and compared to other simple machine learning classifiers. These techniques are shown to be able to achieve up to 94% accuracy on the test data with as few as 100 training samples.
Design and Implementation of Blockchain-based Anonymous Electronic Voting System

This article aims to design and implement an anonymous electronic voting system based on blockchain. The system solves the problem of privacy protection by applying one-time ring signatures and hidden address, which were presented by Nicolas van Saberhagen in his CryptoNote. Ring signature can make the verifier only verify the legitimacy of the vote and cannot infer who sent the vote, which protects the privacy of the voter. The key image generated by one-time ring signature is used to eliminate the problem of repeated voting and ensure the principle of one person, one vote. The hidden address technology generates an anonymous address through the candidate's public key and thus encrypts the content and direction of the ballot, and only after the election is over can the candidate disclose his private key to verify the specific candidate. In addition, blockchain technology solves the problem of fairness and impartiality, and the nature of blockchain itself makes each ballot transparent and open as well as tamper-proof during the voting process. Finally, the Hyperledger Fabric platform was used and smart contracts were written in Go language, and the RuoYi framework was used for the front and back-end design of the system to implement the described voting system.
Adaptive Search Range of Template Matching for Plenoptic Video Coding under Microlens Constraints

This paper proposes a fast template matching (TM) based intra prediction for plenoptic video coding. While the conventional TM method has demonstrated itself as a potentially effective coding tool for plenoptic video, it suffers from a huge time complexity of TM search at both encoder and decoder. In this paper, we note correlation among matching pixels arising from the microlens array structure of plenoptic camera, and propose an adaptive search range (ASR) exploiting relationship between the two sizes of prediction unit and microlens. The experimental results show that our method can reduce encoding time by about 88% and 73% compared to the conventional TM method in the AI-Main and RA-Main conditions, respectively, with acceptable coding loss.
Latitude-Guided Temporally Dependent Rate-Distortion Optimization For Panoramic Video Coding

Panoramic video, features a high frame rate and high resolution, can provide viewers an immersive visual experience. However, it necessitates more overhead bits during video transmission, leading to a heavy burden on storage and bandwidth. Most of the existing panoramic video coding optimizations did not take into account the effect of pixel position on the Lagrange multiplier in the rate-distortion optimization (RDO) process. Meanwhile, those methods did not take into account the effect of the temporal impact factor on the RDO process. In this paper, we proposed a latitude-guided temporally dependent RDO algorithm for panoramic video. Firstly, the quantization parameter (QP) of the coding unit (CU) is determined by the latitude corresponding to the pixel. Secondly, the Lagrange multiplier of the corresponding coding tree unit (CTU) is calculated by the QP of the CTU. Finally, the Lagrange multiplier is fine-tuned to improve the RDO process by using the temporal dependency among CTUs. Experimental results show that, on the platform of HEVC test model (HM-16.14), the proposed algorithm obtains bitrate savings of 4.15%, 4.14% and 4.05% on average under the quality assessment criteria of Spherical PSNR (S-PSNR), Weighted to Spherically Uniform PSNR (WS-PSNR) and Craster Parabolic Projection PSNR (CPP-PSNR) for the equirectangular projection (ERP).
Blockchain Enabled Trustworthy Management Scheme for Network Co-Construction and Sharing

Network co-construction and sharing can improve network efficiency, but the co-construction and sharing management in resource allocation, user behavior, network quality, etc. is complicated to solve manually. It is necessary to use new technologies to realize mutual trust, visible data, symmetrical information and equal perception. This article focuses on the development of ICDT, co-construction and sharing, puts forward the network coconstruction and sharing management based on blockchain, which realizes the data cochain of all parties, data encryption security, rights confirmation of all parties, visible data, mutual trust of all parties, which improves the efficiency of the coconstruction and sharing management. Finally, this article puts forward the prospect of federal learning and the development of a blockchain co-empowerment operator network.
A Genetic Algorithm-based Image Enhancement Approach for Autonomous Driving at Night

Image enhancement increases the perceived quality and improves the experience of viewers by processing images that are difficult to see, such as due to low light or overexposure. This is specifically important for night monitoring cameras or for the performance of visual-based night autonomous driving algorithms. This paper proposes a multi-adaptive fusion image enhancement algorithm (MAAF) to adaptively select and fuse Histogram Equalization (HE), Multi-scale Retinex (MSR), and Gamma Correction (GC) in the image frequency domain through Discrete Cosine Transform (DCT). Based on a genetic algorithm, the proposed MAAF combines the advantages of the different methods in terms of lighting enhancement (HE), edge enhancement (MSR), and overexposed image enhancement (GC) to achieve an overall performance optimization. A comprehensive evaluation score (CES) is also proposed in this paper as an overall assessment metric. MAAF was evaluated in terms of multiple metrics, including entropy, average gradient, contrast, and PSNR. Experimental results showed that MAAF obtains the highest CES compared with other algorithms.