Reviewerly/exHarmony huggingface dataset:
Fault Monitoring and Location Strategy for Power Communication Network Based on Multidimensional Data Association Fusion

To make full use of the massive data contained in the power communication network for fault assessment, a fault monitoring and location strategy for power communication network based on multidimensional data association fusion is proposed, which includes processes such as multi-source data fusion, massive data filtering, and clustering fault research. A massive data filtering method including two steps of time-aligned filtering and feature value hit filtering is proposed to filter out a large amount of irrelevant data. A dichotomy-based k-means initial cluster center selection algorithm is designed to improve the cohesiveness of clustering clusters. This strategy can analyze the specific network devices and links where failures occur from a large number of complex and diverse network alarms, and accurately assist in fault recovery.
Named Entity Recognition of Multilevel Features

Name entity recognition (NER) is a well-researched task in natural language processing, which uses different levels of features of label prediction. The NER task is regarded as a sequence label task. The previous works were performed on one or two types of features in the character layer, word layer or sentence layer, without considering all the characteristic information. In this paper, we first use three different granularities of features of NER, which are extracted by CNN, Attention and BiLSTM, and use CRF for label prediction. We evaluated our model on the CONLL 2003 dataset and achieved advanced performance. The effect obtained in the NER task is a F1 value of 91.87%.
Application of an Adaptive Particle Swarm Optimization Algorithm in Structural Design of Pressure Vessels

The optimization design of pressure vessels is widely used in practical engineering. To solve this problem, this paper proposes an adaptive particle swarm optimization algorithm. First, the algorithm defines a criterion to evaluate particles according to their fitness value and the degree of violation of constraints. Then, the teaching mechanism is used to guide the population to evolve in a feasible direction, and the inertia weight and learning factor are dynamically changed adaptively to balance the exploration and mining capabilities of the algorithm. Finally, particle reset strategy is used to avoid the stagnation of algorithm update. The algorithm is verified by a specific optimization design problem of pressure vessels. The simulation results show that the algorithm can effectively solve the optimization design problem of pressure vessels. Compared with other existing optimization methods, the algorithm has greater advantages in accuracy. At the same time, the algorithm can better solve other engineering structure optimization problems.
Assessment of Blockchain based Energy Trading methods for Electric Vehicles

Nowadays, electric vehicles (EV’s) is one of the most amateur topics, as it not only supports usage of green energy, but also has various other advantages like it leads to pollution free environment, saves fossil fuels and many more. For purpose of energy trading among EV’s and energy providing organization, blockchain is one of the most trustworthy and efficient solution available with us, as it provides contract-less payment and provides transparency among EV owner and organization. A section of this paper also shows the comparison study of all the discussed methods on the basis of algorithm, problem addressed, main feature and remarks which is actually beneficial while understanding. The emergence of blockchain and EV technology can be a boost for energy trading, also use of cryptocurrencies leads to paper-less transactions which is more secure, robust and has less chances of fraud. In this paper various methods have been reviewed that can actually be implemented in today’s scenario and with the further modification, it can become more efficient.
Information completion Enhanced Sentiment Analysis between aspect

In recent years, sentiment analysis has appeared in our life to a great extent and has become a part of our life, but the previous coarse-grained sentiment analysis can no longer be satisfied with the current life[1] [2]. Aspect-Based Sentiment Analysis (ABSA) aims to identify aspect terms, and their corresponding sentiment polarities and perspectives. Therefore, ABSA has become a trend of life now[3]. However, the current pre-trained language models cannot perfectly solve the semantic information between sentences. In our paper, We propose a new way for IECSA to face the classification problem of ABSA, by using the CLS in Bert to learn better results, so that it can effectively learn more semantic information
A Target Identification Method Based on Uncertainty Estimation and Evidence Theory

Aiming at the problem that the accuracy of target identity recognition is easily affected by environment, target state and hardware limitations in surveillance scenes, a target identity recognition method based on uncertainty estimation and evidence theory is proposed, which fuses face and pedestrian information. Firstly, an uncertainty estimation module based on Dirichlet distribution is proposed to realize the reflection of the influence degree of various complex factors on single-mode characteristics in a monitoring scene by numerically simulating and expressing the subjective logic of single-mode decision. Secondly, a combination decision-making method based on DS (Dempster-Shafer) evidence theory is proposed to realize multi-modal dynamic fusion decision-making and eliminate the possible decision-making conflicts among single modes. Finally, a Bayesian risk-based classification loss method is proposed to realize the generation of evidence-level information under multi-modality through constraint network, and the test is carried out on the data set under the urban surveillance scene, and the accuracy rate reaches 87.87 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> , which is superior to the single-modality recognition and splicing fusion method.
Feature Recognition and Data Analysis Strategy for Touch Screen Objects Based on Camera Array

Feature recognition and data analysis strategy for touch screen objects based on camera array is proposed to solve the problems of high cost of large-screen visualization platform and low accuracy of screening the feature attributes of touch-screen objects. The strategy constructs a touch screen object feature attribute model, which contains five sub-models: static feature attribute model, single screen bottom feature attribute model, overall bottom surface feature attribute model, stereoscopic feature attributes model and dynamic feature attribute model. Multi-screen stitching algorithm, recognition efficiency algorithm and recognition accuracy algorithm for touch screen objects are designed to make the recognition of touch screen objects more efficient and the recognition accuracy rate increasing. The strategy can well assist users to improve system performance and provide comprehensive support for building business systems with large screens and visualized interactive systems.
Simultaneous Interpretation Based on Deep Learning and Speech Synthesis

Simultaneous interpretation is a scarce skill which has only been performed by well-trained interpreters so far. The fast development and wide application of artificial intelligence stimulate us to think about its computer implementation. In this paper, we design and implement a system, in which original speech can be converted into translated speech through three steps: speech recognition, text translation and speech synthesis. We apply the newly-developed HiFi-GAN vocoder for speech synthesis, which provides a faster speed and better quality than previous vocoders. And we add the speaker's voice elements as a factor in the synthesis to make the synthesized speech much similar to the original speaker. As is demonstrated by an English to Chinese speech interpretation experiment, our purposed system outperforms a currently widely-used translation system, showing an exciting prospect for computer implementation of simultaneous interpretation.
Calculation method of classroom head-up rate based on head pose estimation

The head-up rate is an important indicator to evaluate the quality of classroom teaching, which can reflect the students' class status in time. With the continuous development of computer vision, many techniques are gradually applied to classroom scenarios. By estimating the student's head posture, it can truly reflect the quality of classroom teaching and effectively reduce the delay of classroom feedback. However, the existing classroom environment is relatively complex, the faces captured by the surveillance cameras are slightly deformed, the sizes are different, and there are occlusion problems, which pose a great challenge to the estimation of the head pose. In response to these problems, this paper improves the RetinaFace model of face detection and introduces the Convolutional Block Attention Mechanism(CBAM) in the backbone network ResNet50 to improve the performance of the network. The Refinement Feature Pyramid module adopted in it can further solve the problem of inconsistent face size. Through the PNP-based method to estimate the head posture, calculate the class head-up rate, and objectively reflect the teaching quality. Experiments show that the method in this paper can achieve higher-precision head pose estimation on the public datasets WIDER FACE and FDDB.
ES-Unet: U-shaped image segmentation network based on Transformer and CNN

As a computer-aided diagnosis technique, medical image segmentation can help doctors improve the accuracy and efficiency of disease diagnosis. In recent years, convolutional neural networks (CNNs) have made great progress in the field of medical image segmentation, and medical image segmentation networks using encoder-decoder and jump connection structures have a wide range of applications in the medical field.
Research and implementation of Archives knowledge Base for Multi-source heterogeneous data Fusion

Based on the document file text data, this paper studies the training corpus construction method of multi-source heterogeneous file data, designs the implementation process of archives knowledge base, including knowledge extraction, knowledge storage and visual display, and applies Neo4j diagram database to store and realize document file knowledge. With the combination of natural language processing, knowledge graph technology and archival knowledge service, this paper promotes the integration and sharing of archival data resources, promotes the information construction in archival field and improves the efficiency of users' access to archival knowledge. it can provide a new way of knowledge service for the integration of knowledge services in the field of archives.
A Meta-Learning Framework for Predicting Power Digital Equipment Defect Texts via Hypergraph Modeling

Power digital equipment defect text classification, which is often used as a guide for defect elimination or fault handling, has attracted the attention of researchers. Although various methods have been proposed, the obtained results are undesirable due to the requirement of a large number of labeled training samples of existing methods. However, the annotating process is typically costly and expert knowledge required in electrical field. In order to solve this problem, this paper uses meta-learning to overcome the problem of small sample size and difficulty of scene migration, which is a way to acquire “learning to learn” and learn new tasks quickly based on the knowledge already acquired. Therefore, we model the task of power digital equipment defect texts classification under a Meta-Learning framework in this paper. Specifically, we utilize a hypergraph-based document modeling to enrich the representation learning for entities, and improve the derived prototypes accordingly by prototype network. In addition, the module of relation network is introduced to train a more meaningful distance function for detect type prediction. Comprehensive experiments are conducted on real-world datasets, and the results demonstrate that the proposed framework outperforms existing methods according to F1-score.
Adjustable Key AES Encryption and Decryption Circuit

In this paper, an adjustable key AES encryption and decryption circuit is designed by applying iterative architecture techniques to address the problem that the Advanced Encryption Standard (AES) hardware design needs to occupy a large number of hardware resources. The circuit is compatible with three different encryption and decryption modes, AES-128, AES-192, and AES-256, and the user can select the encryption and decryption mode of the data according to his needs. Based on iterative architecture, the design uses a state machine to control the number of AES encryption and decryption round conversions according to different key lengths, while controlling the adjustable key extension module to expand the key internally and provide subkeys for the round conversions. During the wheel transformation, resources are shared between some of the common circuits of the decryption and encryption logic to reduce the overall resource consumption of the circuit. The designed circuit is based on the XC7VX485TFFG1157 comprehensive simulation from Xinlinx and consumes 1349 Slices, and 4700 Slices LUT, which can meet the requirements of wireless mobile networks and other resource-constrained environments.
Applying Autoencoder to Automated Theorem Proving

Interactive Theorem Provers (ITPs) provide a set of formalized theorems and proof techniques that allow humans to interact with them by choosing problem-solving tactics. The prover continuously transforms the current proof goal according to the input tactics, and finally realizes the proof of the overall goal. In recent years, there have been many attempts to apply deep neural networks to automated theorem proving. Most of them take the current proof goal and premises as input to the neural network and output the proof tactic to choose at the current step. The current context information is usually encoded by a structured neural network. We introduce an autoencoder in the encoding process to encourage the model to extract the semantic-level information of terms and filter out the syntactic-level information. In addition, during the decoding process, we added skip-connection to the attention mechanism, so that the model can obtain the target information more directly. Experiments show that our model accelerates the convergence of the training process, reduces the loss on the validation set, and slightly improves the success rate of theorem proving when using a pure neural network model.
Improved Transformer-Based SSD Detector for Airborne Object Detection

Object detection tasks based on UAV -captured images are often made quite challenging because their images contain great scale variance, a high number of interferers and complex backgrounds. In our work, a modified CSP-DarkNet backbone is designed to extract features with greater variance while ensuring sufficient global information. We introduce an improved BiFPN for more effective multi-scale feature fusion. Our experimental results show that the SSD-VIT significantly outperforms state-of-the-art detectors and achieves excellent results in the VisDrone-DET2021 challenge.
Knowledge Helps Pretrained Model: An Ensemble Model of Knowledge Model and CLIP for Zero-shot Image Recognition

The zero-shot image classification task is to recognize image without training samples. Knowledge based method is explored in this field, for knowledge graph has the advantage of utilize explicit knowledge to transfer knowledge from seen set to unseen set. Recently, large scale pretrained model is demonstrated to have more powerful image-text alignment capabilities which is help for zero-shot image classification task. However, the pretrained model is trained without explicit knowledge. Therefore, in this paper, we propose an ensemble method Knowledge and Pretrained Ensemble (KPE) model that utilize the strength of both of the knowledge graph and large scale pretrained model. Experimental results show the effectiveness of our model.
A New Method to Improve the Accuracy of the Chinese Language Text Classification Based on Big Data Text

The role of computers in modern society is very important. Natural Language Processing (NLP) is an important direction in the field of computer science and artificial intelligence. Most of the modern NLP fields are dominated by European and American countries. In addition, our Chinese language is very different from the phonetic characters in Europe and America. Although the development of Chinese NLP is in the ascendant, there are still many problems. As The Belt and Road Initiative is widely accepted and welcomed in the world, political, economic, cultural and other fields are accompanied by its deepening exchanges and development in the countries along the way. This paper chooses the topic of overseas Chinese text classification to carry out research, hoping to share its modest power for the development of The Belt and Road Initiative. We designed three experiments: THUCNEWS text classification based on THUCTC, the Chinese Language (CL) text classification directly using THUCTC, the CL text classification using THUCTC with CL wordlist. Although it costs us a lot of energy to build a CL wordlist, it is effective to improve the accuracy of CL text classification by 1.8%.
A Receptive Fields Enhancement YOLO V4 on Pedestrian Detection

Since different illumination condition, viewpoint change, local occlusion, and scale effect make the appearance features of pedestrian change, field enhancement is essential for pedestrian detection using the deep learning method. Deep learning-based approach to further enhance pedestrian detection into a semantic segmentation problem, but distant pedestrians often disappear in the feature extraction stage because the target is too small. Also, distant pedestrian detection becomes increasingly tricky because similar images or marks can vary in shape and pose. A pedestrian detection model based on YOLO V4 is proposed to address the above problems by studying the similarity between people. The key idea of the model is to further improve the accuracy of YOLO V4 by marking full use of the relationship and distribution law of the appearance features between people. Specifically, more extensive kernel size convolution operation is used to enlarge the perceptual field and make the feature distinct. With a quantization method that can balance accuracy and speed, a module can enhance the convolution neural network's remote dependency modeling capability. Then, the accuracy of pedestrian detection can be improved combining proposed module with YOLO V4. Finally, comparative experiments on the COCO dataset are verified. The results show that the accuracy precision of the proposed model is up to 77.25% and the detection speed is up to 34.33 fps.
Sparse Attribute Network Representation Learning Method

In recent years, the network representation learning (NRL) technology have been widely used in learning low-dimensional vectors for graph structures. Although much previous work has been done in the joint analysis of node attributes and network structure, most of them do not consider the weighted correlation between nodes and attributes and have difficulty in identifying the structural role proximity of nodes. This is especially true when the nodes have very sparse attributes.. In response to this, this paper proposes a sparse attribute NRL method. Firstly, to mitigate the sparsity, a deep self-encoder is used to capture the higher-order proximity and structural role proximity of the network, A pairwise nodal algorithm is then introduced to obtain the interaction between sparse attributes and nodes, and to mitigate sparsity again by aggregating information about the nodes' neighbourhood attributes. Finally, the similarity between the central node and the context node is calculated and assigned using the attention mechanism, based on the principle that the higher the similarity is with similar attributes. The attention mechanism is used in conjunction with the CBOW model to learn the correlation of the network topologies and node properties. Our approach achieves better performance on each of the selected mainstream baseline methods.
A Network Security Risk Management Model Based on ATT&amp;CK for ICS

The security situation of industrial control system is becoming increasingly severe. ATT&CK for ICS is widely used to describe and classify the attack behavior and risk identification of industrial control environment. This paper first discusses the network security risk elements of industrial control system, analyzes the vulnerability and threats faced by industrial control system, and further proposes a network security risk management model based on ATT&CK for ICS. Based on the risk identification results, the network security risk management model formulates corresponding security defense strategies and measures, and conducts risk closed-loop review and disposal, effectively improving the network security defense capability of the industrial control system.
Super-resolution reconstruction of remote sensing images based on Local Discriminative Learning and Pyramid Squeeze Attention

Due to the physical factors such as imaging distance and imaging period of remote sensing system, the resolution of remote sensing images is usually low. The existing image super-resolution reconstruction methods cannot effectively use multi-scale information, and the reconstructed image is prone to visual artifacts, loss of high-frequency information and other problems. To solve this problem, a super-resolution reconstruction network of remote sensing images is designed based on generative adversarial network, which combines local discriminative learning and pyramid squeezing attention. The network is composed of local discriminative learning (LDL), pyramid squeeze attention module and residuals dense connection. The generated network extracts the detailed features of remote sensing images through residual dense connection blocks and adds a pyramid squeeze attention mechanism (PSA) to effectively extract multi-scale spatial information. Real details are generated through local discriminative learning, which effectively suppressed the generation of visual artifacts. The experimental results demonstrate that the proposed method can effectively improve the resolution of remote sensing images and suppress visual artifacts.
3D Registration Method Based On MVFNet And Super4PCS For Nonhomologous Face Pointclouds

To find an economical and accurate reconstruction method for face efficacy evaluation in the medical field, especially for clinics environment can be a challenge because the comparison before and after operation is always involved. In this paper, a MVFNet-based 3D face reconstruction for efficacy evaluation is proposed. However, it is not practical yet since the MVFNet reconstructed pointcloud is far different from accurate pointcloud reconstructed by professional equipment. Therefore, the Super4PCS algorithm is used, which can be used to register nonhomologous pointclouds with high difference. The registration experiments are designed and conducted to evaluate the performance of Super4PCS, and we analyze the accuracy of MVFNet reconstructed pointcloud. The results show that MVFNet reconstructed pointcloud is accurate enough and Super4PCS can register two pointclouds well.