Reviewerly/exHarmony huggingface dataset:
Analysis of the Impact of Major Emergencies on Scientific Research

Major Emergencies play an important role in guiding and promoting scientific papers. However, there is still a lack of a complete review and framework that can effectively address major emergencies. This research aims to fill this gap by comparing and studying the impact of four major emergencies on scientific literature. We propose a "top-down" and "bottom-up" (TDBU) framework which can generally process the global database, including the China National Knowledge Infrastructure (CNKI) database for the "top-down" part and the Web of Science (WoS) database for the "bottom-up" part. We employ bibliometrics and data visualization methods to analyze the published literature. Our extensive experiments using CiteSpace from the aspects of keyword analysis, co-occurrence analysis, and other measures confirm that this framework can provide an effective reference for the research direction mining of similar events.
FGP-Astar Algorithm Based on Path Planning for Mobile Robots

Aiming at the problems of high search nodes, long evaluation time, and too many large continuous turning angles in the traditional A* algorithm for global obstacle avoidance and path tracking, which lead to the instability of obstacle avoidance in actual robot work. A Facilitative Global Points A* Algorithm (FGP-Astar) is proposed, which is a global path planning algorithm based on an improved A* algorithm. Based on the bidirectional A* search strategy, the adaptive weight factor of the heuristic function is configured to prioritize approaching obstacles and assist in tracking, improve the node search efficiency, and avoid the intervention of invalid nodes. Bezier curve is used for path smoothing and key path points are extracted. At the local path planning level, this paper addresses the deficiencies of traditional dynamic window approach that easily fall into local optimal paths and cannot avoid complex obstacles. A new sub-function is incorporated into the evaluation function of the Dynamic Window Approach (DWA), which is based on the traversable area of obstacle distribution. Gazebo simulation is applied to ROS1 mobile robot platform, and a comparison experiment of path optimization for fixed point delivery is carried out. The simulation results show that FGP-Astar reduces the global path planning time by 55.63% compared with the traditional A* algorithm, and the number of nodes traversed in the path prediction is reduced by 65.79%. It demonstrates the rapid deployment of the algorithm in global path planning, which reduced computational load, and obtaining more reasonable paths. Subsequent research needs to increase sensitivity and responsiveness in dynamic obstacle avoidance.
Mask-VGG: A Shot Scale Classification Model Based on Mask Generation

The development of artificial intelligence has made it possible to automate video production. Shot scale is an important visual characteristic, and intelligent shot scale classification is the key to automate video production. The convolutional neural network has a good performance in the field of image classification. However, when it comes to shot scale classification, a limitation arises in terms of the model's inability to pay attention to the subject of the frame. In the existing schemes, a foreground segmentation model is used to separate the subject from the background, and then shot scale classification is done by a classification model. This “two-step” scheme is computationally expensive and inefficient. In this paper, a Mask-VGG is proposed to classify shot scales in one step, in which a mask generated from the feature map could effectively strengthen the foreground information. And the convolutional layer is to retain the position features of the subject in the shot scale classification task. The proposed model improves the performance of shot scale classification, and the accuracy reaches 95.43%. It also matches the requirement of the live video production in a speed of 6 fps.
Chinese Named Entity Recognition of Non-Performing Assets Based on BBC: BERT-BiLSTM-CRF

In the post-epidemic era, the number of non-performing assets and entities has been growing rapidly, leading to a surge in the number of related entities. However, the traditional method of constructing a dictionary of non-performing assets of enterprises using the identification method of BERT-CRF does not contribute significantly to the accuracy. In response to this problem, this paper is a proposal for a new type of model named BERT-BiLSTM-CRF(BBC) which provides Named Entity Recognition (NER) for non-performing assets by combining the pre-trained BERT language model and BiLSTM-CRF. Compared to the static word vector model, BERT is able to generate dynamic word vectors based on the context, which improves the accuracy of the semantic coding together with the accuracy of the NER task for non-performing assets. The word vectors obtained from the BERT model are fed into BiLSTM model for further training, and then CRF corrects the model's output to produce the labeled sequence with the highest score. The proposed model achieved an F1 score of 0.936, which represents a 6.727% improvement and a 6.120% accuracy increase in comparison with the baseline model.
NOAA National Water Level Observation Network Requirements

The National Ocean Service's (NOS) Center for Operational Oceanographic Products and Services (CO-OPS) provides the National infrastructure, science, and technical expertise to collect and distribute observations and predictions of water levels and currents. The foundational purpose of the NWLON is to provide the national vertical reference framework (tidal and Great Lakes datums) for monitoring and communicating water levels, and providing long-term sea-level trends. CO-OPS establishes standards for the collection and processing of water level and current data; collects and documents user requirements, which serve as the foundation for all resulting program activities; designs new and/or improved oceanographic observing systems; designs software to improve CO-OPS' data processing capabilities; maintains and operates oceanographic observing systems; performs operational data analysis/quality control; and produces/disseminates oceanographic products. This paper summarizes CO-OPS NWLON requirements and the detailed considerations that go into each NWLON station. CO-OPS is finalizing the full NWLON Requirements Technical Report and it will be published soon.
Research on the Application of Machine Learning in the Field of News Commentary Analysis

With the development of the Internet, research on news commentary analysis has been received increasing attention. The analysis and application of news commentary is of great significance to mainstream media opinion guidance, content production guidance, and media communication efficiency enhancement. However, traditional methods for news commentary analysis have some problems with low efficiency and low accuracy. Research on the application of machine learning in news commentary analysis is a hot topic. This paper deeply analyzes the importance of news comment analysis, the existing problems, and the expected objectives. It proposes the application of machine learning techniques to address the challenges in news comment analysis and sets forth the methods for achieving the goal of shortening the distance between media and the public through such analysis, thereby enhancing its capacity for influencing public opinion and competitiveness.
Retrieval-Based Question Answering Based on Emotion-Aware Graph Attention Network

Retrieval-based Question Answering (RQA) is a critical research subject in the field of Natural Language Processing (NLP). Its goal is to obtain the most relevant answer candidate from a knowledge base. Existing methods often overlook the emotional and structural information contained in questions, leading to a decrease in accuracy. This paper proposes an RQA method based on an Emotion-Aware Graph Attention Network (EAGAN). The method initially screens candidate answers using the BM25 algorithm, followed by extracting emotional features of the question using a pre-trained language model. Subsequently, it uses a Graph Attention Network to transform the question into a graph structure and compute the attention weights for each node in the graph. Lastly, it multiplies the graph attention weights with the emotional features to obtain semantic features of the question, and calculates the similarity between semantic features to screen out the optimal answer. Experimental results show that this method achieves high EM and F1 scores on multiple datasets.
Research on Hybrid Path Planning Algorithm Based on Anti-Collision

In order to solve the problem of fast switching of special equipment in the cultural complex, the improved Moth-Flame Optimization algorithm (MFO) is combined with the improved Dynamic Window Approach (DWA) to realize the hybrid path planning of handling robots in different scenarios of cultural complexes in this paper. Specifically, the traditional Moth-Flame Optimization algorithm is improved and applied to the global path planning problem. On the basis of the planned global path, the intersection of dynamic obstacles with the global path is detected, and the improved Dynamic Window Approach is used for local obstacle avoidance in the section with collision risk, finally, the hybrid path planning in the complex environment of the cultural complex is realized. The simulation experimental results prove the effectiveness of the proposed algorithm.
Accelerating Blake3 in RISC-V

This paper proposes a novel hardware design to accelerate the Blake3 hash algorithm, a cryptographic hash function, on RISC-V architecture, using both vector extension (V1.0) and a specially designed Blake3 instruction. The design leverages the parallelism and simplicity of BLAKE3 to achieve high throughput and low latency, implementing the core compression function using a custom instruction set architecture that supports vector operations and constant computing. The Blake3 compression function is speed up in architecture based on hardware/software co-design. The design achieves a speed-up of 10 times for the compression function compare with a single core of 2.7GHz Xeon 8280 and 10% faster than an AVX512 implementation.
An Active Semi-Supervised Learning for Object Detection

The rapid progress in deep learning technology has rendered extensive annotated datasets indispensable for augmenting algorithmic performance. However, annotating datasets requires a significant amount of resources and manpower. To address the high costs of annotating object detection tasks, we present an active semi-supervised learning algorithm framework tailored for object detection. The framework utilizes a semi-supervised learning model equipped with active learning strategies to manually label difficult-to-process minority samples. Additionally, we introduce the SimOTA(Simplified Optimal Transport Assignment) label assignment strategy, which obtains optimal samples from a global perspective. Moreover, the loss function for the unlabeled data has been adjusted to enable the utilization of semantic information from the noisy pseudo labels. The empirical findings obtained from assessments conducted on publicly accessible datasets provide evidence that the active semi-supervised learning algorithm framework proposed in this paper outperforms current advanced active learning strategies and semi-supervised learning algorithms in the area of object detection.
Micro-expression Recognition Based on MobileVit-SE Block

Micro-expressions are important information that can understand a person's deepest emotions within their heart and can clearly reflect human real emotions and mental states. They have excellent application prospects in various fields such as medicine, criminal interrogation, and public security. The amplitude of facial movements in micro-expressions is small and their duration is short, making recognition more difficult compared to macros expressions. To identify micro-expressions and execute microexpression recognition tasks on portable devices, this paper optimizes the MobileVit model for visual recognition on mobile devices and adds a MobileVit Block based on the channel attention mechanism (SENet), which is hereinafter referred to as the MobileVit-SE Block. The optimized model replaces the MobileVit Block after the second MV2 down-sampling and the fourth MV2 down-sampling in the original model. Experimental results show that the optimized model achieves an accuracy of 0.817 on the fusion dataset of CASMEII, SMIC, and SAMM, which is slightly lower than the accuracy of 0.82 achieved by directly using the MobileVit model for recognition. However, the processing speed is doubled, meeting the requirements of application scenarios requiring fast detection and recognition of micro-expressions.
Enhancing Event Argument Extraction: A Span-level Representation Approach

In the domain of event extraction, the process of identifying and categorizing event arguments and their corresponding roles is a critical subtask. Current methodologies for event argument extraction are predicated on the classification of individual argument roles based on a single word, ignoring the form of argument phrases. However, words in a phrase are prone to be mistaken when the classification is based on word level. To address this issue, we propose a novel event argument extraction model that employs span-level representation to avoid false classification occurring on some words in the same phrase. We achieve this by using span-level information instead of word-level information. Specifically, we first use a bidirectional Gated Recurrent Unit to effectively encode input with context and predict the positions of all arguments in the sentence. Subsequently, an attention mechanism is applied to integrate multiple words under the same argument phrase, considering their different importance, and generate a high-quality span-level representation. Experiments conducted on the ACE2005 dataset demonstrates that the our model can effectively leverage span-level information from argument phrases.
Pseudo-noise Code Length Detection in DSSS Signal Based on Deep Learning

Direct Sequence Spread Spectrum (DSSS) has been commonly applied in mobile communication, electronic countermeasures and other fields in recent decades due to its strong anti-interference ability, high spectrum utilization rate and low interception rate. In the case of non-cooperative communication, it is difficult to obtain more prior information at the receiver, traditional detection method based on auto-correlation is greatly affected by signal-to-noise ratio (SNR) and the detection performance is very poor at low SNR. In addition, because of deficient research on the application of deep learning in DSSS signal detection problems, we propose a multi-classification problem based on deep learning to detect the pseudo-noise(PN) code length in DSSS signals. First, we propose to use two neural network methods based on convolutional neural network (CNN) and based on long short-term memory (LSTM), which inputs the received signals into two neural networks. In addition, in order to make full use of the data features that CNN and LSTM are good at processing respectively, we also propose to use the CNN-LSTM joint network to detect the PN code length, where the CNN part inputs the time-frequency image features, and the LSTM part inputs the sequence features of the received signal. In this paper, we use the traditional autocorrelation detection as a comparative experimental method. The simulation results show that the detection performance of the deep learning-based methods is obviously better than the traditional autocorrelation detection method.
Research on Voice Interaction Robot System Based on the Hybrid Chat Model of Retrieval and Generation

With the rapid development of artificial intelligence technology, the impact of intelligent interactive robots on human life is growing. This paper constructs an intelligent voice interaction robot system based on the cloud platform and Android platform. This system uses a microphone array for audio data collection, environmental noise reduction, and voice activation. The audio processing technology based on Voice Activity Detection (VAD) and time window is adopted, which transmits data to the voice recognition engine in real time and dynamically. By invoking iFlytek’s open cloud platform’s automatic speech recognition (ASR) and synthesis services, it completes the conversion of user speech to text and the conversion of robot reply text to speech (TTS); By combining the retrieval chat model based on text similarity method and the generative chat model based on Chinese pre-training model, a retrieval-generation hybrid chat model is proposed, realizing the text communication of the robot in specific fields and daily chat needs. This voice interaction robot system can respond to users in real time, cover a wide audience, and has broad application prospects.
A Visual Analysis System on the Development of Global Cinema

It has been more than one hundred years since the birth of film, during which different countries and regions have developed their own film industry based on their unique culture, social structure and economic situation. In this paper, with the help of third-party Python libraries such as BeautifulSoup, Requests, CSV, etc., we wrote a web crawler to retrieve the film information from Douban.com over the years, including the film name, director, film genre, etc., and saved it as a CSV file. The information is analysed using the groupby function in Pandas, and the results are visualised using the word cloud and line graph templates in Pyecharts. The final results show the changes in film production and film ratings in Mainland China, Hong Kong, the United States and Japan from the 1960s to the present, and compare the differences in ratings among these four regions. This paper can help scholars in the fields of cinematography, art, economics and sociology to visualise the current status of the industry in the four maj or film production regions for more in-depth research in various disciplines.
A Dual-U Structure for Image Denoising Based on Attention Mechanism

The SWA-U <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> former with a nested dual U-shaped structure for removing complex noise from real images is proposed, which is an effective image denoising network framework. Each U-shaped structure includes an encoding and decoding structure based on the shifted-window core. The following are some benefits of the design: Firstly, it can better capture the dependencies between pixels over long distances and can resolve image information at different scales. In Shifted Window Attention Residual U-blocks (SWA-RSU), due to the use of a Shifted-window Transformer and the stacking of multiple encoders and decoders, it not only solves the problem of high computational complexity caused by the attention mechanism but also fuses feature maps at different scales, making the restoration effect more robust and ensuring the stability and accuracy of image denoising. Secondly, the Attention mechanism is combined with image convolution in the SWA-RSU module. the attention mechanism is mainly used to analyze the image. Since the entire network has a nested dual U-shaped structure, collecting the outputs of each layer of the SWA-RSU decoder, fusing their features, and finally using convolution to obtain the final result. The attention mechanism can help convolutional neural networks better deal with image noise and distortion. By applying different attention mechanisms to different regions, details and information in the image can be better preserved, thus improving the effectiveness of image denoising and restoration. Under these two designs, SWA-U <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> former can capture the deviation between degraded and original images at multiple scales, effectively completing the task of image denoising. Finally, conducted experiments on benchmark datasets, and SWA-U <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> former achieved outstanding performance compared to the best algorithms.
Motion Planning for Autonomous Underwater Vehicles in Near-Seafloor Operations Using State Lattices

This paper presents a motion planning algorithm to enable planning of safe and efficient motions for autonomous underwater vehicles (AUVs) at low altitudes using state lattices. The algorithm was developed for and tested on a HUGIN 1000 AUV conducting data gathering for optical identification of objects on the seafloor. As the risk to the AUV is greatest at low altitudes, a resolution-complete state lattice A* motion planner was employed to find collision-free resolution-optimal motions. The state lattice A* algorithm was also proven to have a fixed worst-case execution time for a specific motion planning problem. The planner was experimentally verified in a sea trial where a HUGIN 1000 AUV conducted a bathymetric mapping survey of a target area and then planned and traversed a collision-free motion between an operator specified start and goal point. As the HUGIN AUV currently operates using waypoint guidance, the motion was reduced to a sequence of waypoints, resulting in some deviation from the original motion. The route was however traversed collision free by the AUV at an altitude of 10 m. The main contribution of this paper is the adaptation of the state lattice A* planner to a dynamic model of an AUV and validation of the algorithm experimentally.
3D Reconstruction Using Multiple Acoustic Images Under Roll Motion Based on Backprojection Techniques

In underwater with high levels of turbidity, a multi-beam acoustic camera proves to be advantageous for obtaining visibility. Unfortunately, the loss of elevation information during the making process of 2D acoustic images causes it challenging to accurately determine the three-dimensional information of objects. This paper proposes a method that applies the Radon transform and back-projection method to restore elevation and reconstruct objects in 3D from 2D acoustic images. For this, 2D acoustic images need to be taken at different perspective views as the camera undergoes a rolling motion from 0 to 90 degrees. Experiment results are included as well.
Study on Pricing Strategy for Cloud Services based on Contract Theory

Cloud services providing has been developed with the increasing amount for cloud computing market. Multiple pricing strategies have been proposed to evaluate the commercial value for the heterogeneous cloud services. However, from the perspective of economics study, the profit from the whole business is generated not only depending on the benefit maximization for cloud service provider but also needs to encourage users to participate into the market. To this end, a contract-based pricing strategy for heterogeneous cloud services is proposed where users with different request level pay the price to the cloud service provider for the corresponding servings. The optimal contract is derived to maximize the profit for the cloud service provider under the constraints for maintaining the user attractiveness into the business. More discussion beneath such economic model is presented through exploring the effect for user preference on the optimal contract generation.