Reviewerly/exHarmony huggingface dataset:
Research on Power Communication Defect Diagnosis Technology based on Unsupervised Learning

In order to study the power communication defect diagnosis technology based on unsupervised learning, four methods of alarm merging technology, artificial intelligence technology, unsupervised learning technology and graph data mining technology were analyzed. Four technical routes were positioned and graded, and the subject was tested and studied. For alarm data, a self-learning algorithm based on unsupervised clustering and frequent subgraph mining to realize alarm merging and defect pattern discovery is proposed, and a framework for automatic defect diagnosis and disposal is designed. The architecture has good scalability and iterative update ability, and is verified by experiments on real scene datasets, and the results show good performance.
Information System Analysis And Design For Mobile-Based Homain Applications

Builders are workers with specific expertise in development. Community needs for workers with special skills to repair parts of homes remain difficult to find due to space and space constraints. In the current era of the Covid-19 pandemic, there are many people like construction workers who want to find work, and many people who need construction workers to repair parts of their homes but are not sure if construction workers are performing well or not well recruited by them. In this study, the authors propose an application to understand how construction workers can be found through an online application, and how potential clients can find suitable and trustworthy contractors. Smartphone users only need a smartphone and an internet connection to access this HoMain app. The purpose of this study is to design an online application for a mobile-based construction service ordering system to support the needs of the community and builders during construction work. With this app, people can easily order construction services based on online location. The HoMain application development method adopts the Rapid Application method, and the application development is relatively fast and efficient.
Multi-stage Enhanced Denoising Network on Hyperspectral Image

Hyperspectral images (HSIs) will experience noise throughout the data collection process due to the imaging system's limitations, which will make it challenging to extract the image's crucial information. In this paper, a multi-stage enhanced HSI denoising network (MED-Net) is proposed. Our core concept is to process the hyperspectral noise image iteratively using a multi-stage network. A similar network structure's first and second phases are employed for the denoise process. To achieve cross-stage information transfer, we use CSFF (Cross-stage Feature Fusion) mechanism and SAM (Supervised Attention Module). AN (Additive Network) and MN (Multiplicative Network) are used to remove additive and multiplicative noise. Then, we restore the background based on the residual network and attention mechanism. The results of our experiments demonstrate the superiority of our approach over the actual HSIs data recovery, and the restored image has good visual clarity and detail.
Research on Underwater Measurement-Device-Independent Quantum Key Distribution

Focusing on the application scenario where the underwater mobile platforms wirelessly access the cabled underwater information networks to achieve communication, an underwater access scheme for measurement-device-independent quantum key distribution is proposed to ensure communication security. Based on the analysis of the optical characteristics of the seawater channel, an underwater access model of measurement-device-independent quantum key distribution is constructed, simulated and analyzed to verify the feasibility and effectiveness of the scheme. The simulation results show that the maximum secure access distance of the scheme (under extreme conditions) can be extended from 147 meters to 230 meters or even 451 meters as the seawater type changes from turbid seawater to moderately turbid seawater to clear seawater. The vacuum + weak decoy state scheme can obtain performance that is very close to this limit. After considering the effect of finite data-set size, the performance of the scheme is reduced, but it can still meet the application requirements of underwater mobile platform access within a certain range. In practical applications, measures such as deploying wired communication buoys at network nodes can be used to further expand the effective access range.
TFPNet: Twice Fused Pyramid Lightweight Convolution Neural Network for Classification and Semantic Segmentation

At present, the backbone network structure of the classification model is mostly linear, although it can expand the receptive field to learn high-level semantic information with the increasing number of convolution layers. However, they did not fully consider the different levels of information representation. Therefore, we propose a twice-fused pyramid convolution neural network. First, according to the features of different convolution layers, we divided the network into three levels: shallow, medium, and deep. Then, through the reverse pyramid connection, the features are secondary learned under the guidance of high-level semantics. At the same time, to bridge the feature differences of direct fusion at different feature levels, we use the method of twice fusion to connect the pyramid layers. We know that multi-scale context information is important for semantic segmentation, so we add an augmentation module between the pyramid feature layers that is denser than ASPP feature extraction. In experiments, our model also uses depthwise separable convolutions, which outperforms the linear structure MobileNetV1 in classification results on CIFAR and SVHN datasets while maintaining light weight. At the same time, it also achieved good results in semantic segmentation tasks, surpassing some mainstream models on Cityscapes and CamVid datasets.
Integration of Multiple Time Embedding and GLU for Sequential Recommendation

Self-attention has made great progress in the field of sequential recommendation. The traditional attention-based sequential recommendation model usually adopts position embedding or simple time embedding, which is difficult to use the user’s time information to obtain the changes between the long-term and short-term interests of users, which makes the sources of user interest information single and insufficient. In order to solve the above problems, the self-attention sequential recommendation algorithm is proposed, which combines with multiple timing characteristic embedding and gated linear feed-forward network. The feed-forward neural network with gated linear units (GLU) is used to better optimize the role of transformer model in sequential recommendation, and multiple time tag embedding methods are used to fully obtain the changing trend of user interest over time, so as to improve the accuracy of recommendation. Experiments on multiple datasets indicate that we solve the problems better and improve the predictive performance of the model compared with the traditional model.
High Value Payment System Data Inquiry Using a NL2SQL Framework

When applying the popular deep learning based NL2SQL models directly in a specific scenario, problems arise due to the characteristics rooted in background knowledge. In our case, the terminologies and abbreviations in the high value payment system database are the main obstacles. In this paper, a framework that is compatible with BERT-CN and RAT-SQL is proposed for data inquiry tasks within the high value payment system, in which both BERT and RAT-SQL are state of the art models achieved great performance in many tasks. Besides that, NER and data preprocessing toolkits are introduced to align the terminologies and abbreviations with the columns and tables. Both the training and testing stages show acceptable results and the reasons are well discussed. This framework has great potential to be extended to other application scenarios with minimal modifications.
Fast Radix-2 Montgomery Modular Multiplication on FPGA Using Ternary Adder

The most time-consuming operation in Elliptic Curve Cryptography (ECC) is modular multiplication. A series of FPGA implementations of various modular multiplication algorithms have been developed thus far, aiming at reducing the computation time. In this paper, a radix-2 Montgomery multiplication architecture is presented, the main element of novelty being the use of a single ternary (i.e. three-input) adder instead of the pair of adders used in other architectures. The proposed design was implemented on a Xilinx Virtex-7 FPGA, obtaining maximum multiplication times of 0.62 µs, 0.79 µs, 0.94 µs, 1.51 µs and 2.18 µs for operands of 192, 224, 256, 384 and 521 bits, respectively. In a comparative analysis with other radix-2 architectures, the experimental results show that the proposed design brings a notable improvement in multiplication time, throughput and maximum operating frequency, while having a high area-time efficiency.
A Multi-level Complex Feature Mining Method Based on Deep Learning for Automatic Modulation Recognition

The previous modulation recognition models based on deep learning ignore the signal's complex characteristics and only consider the information carried by the signal in a single dimension, resulting in poor performance. Aiming at the complex characteristics of in-phase/quadrature (I/Q) data, this paper adopts a combination of complex convolution and one-dimensional real convolution, emphasizing the feature interaction between I and Q and enriching the feature representation of the signal. Besides, a multi-level complex attention block is introduced to enhance the informative representation of the entire feature space. Experimental results indicate that the proposed method's recognition accuracy of MQAM is significantly improved. Furthermore, the proposed method also alleviates the poor performance under a low signal-to-noise ratio, which is overall better than other deep learning-based modulation recognition models.
An Improved HRNET and its application in crowd counting

Aiming at the low accuracy of crowd counting caused by scale change and occlusion in dense scenes, this paper proposes to generate the truth map into non overlapping independent areas in HRNet to facilitate the crowd location statistics of network density map; Then the 3D attention mechanism is introduced to make the network focus on the useful information of the feature map; Finally, during the training, the mean square error loss (MSE loss), L1 loss and cross entropy loss are combined into the total loss function to optimize the generalization ability of the model; The combination of the above methods improves the accuracy of the model in crowd counting and crowd location. Compared with the main methods in recent years in the public datasets NWPU, Shanghai Tech, the experimental results show that the proposed model can effectively improve the accuracy and robustness of crowd location counting.
Mobile Augmented Reality Serious Game Design Based on Bloom’s Taxonomy

The limitations of real learning resources, the less attractive presentation of learning, and the mismatch of material delivery with the level of students’ thinking abilities were challenges in implementing learning that needed to be addressed immediately. This study aimed to develop learning applications using the principle of augmented reality serious games based on taxonomy bloom as an alternative solution to this problem. The method used was the ADDIE development cycle. This cycle consists of five stages, namely Analysis, Design, Development, Implementation, and Evaluation. The product produced in this study was a serious game application with the concept of augmented reality based on bloom’s taxonomy on computer network material. This application was tested on five teachers and thirty students. Next, an evaluation of the application was evaluated in terms of four aspects, namely functionality, reliability, usability, and efficiency. The evaluation results show that the application developed meets the criteria well in the four aspects, with details of 58% of users giving good ratings on the function aspect, 55% on the reliability aspect, 50% on the usability aspect, and 47% on the efficiency aspect.
A Video Bitrate Adaptive Algorithm for Public Network Digital Trunking Terminals

Concerning the fact that the real-time video communication of mobile terminals is prone to video freeze due to the mobility of public network digital trunking terminal, a video bitrate adaptive algorithm for public network digital trunking terminals is proposed. Firstly, the mobile terminal detects the current geographic location and bandwidth situation in real time. Secondly, the prediction algorithm based on unscented Kalman filtering is used to predict the geographic location of the mobile terminal at the next moment. At the same time, the bandwidth situation corresponding to the geographic location at the next moment in the database is searched. Finally, dynamically adjust the video bit rate according to the current bandwidth situation and the bandwidth situation of the geographic location at the next moment. The experimental results show that, the proposed algorithm can accurately predict the geographic location at the next moment, and the geographic location prediction error is 1.89 meters; Compared with the video bitrate adaptation algorithm based on the current bandwidth information, the proposed algorithm can effectively reduce the video freeze and improve the video quality.
A mod and xor coupling encryption algorithm for image based on 2D-LSM

In recent years, how to ensure the secure transmission of images in the network has been the focus of scholars. Since chaos has good ergodicity, it can generate a long pseudo-random key stream, which is very suitable for participating in the design of cryptographic systems. Aiming image encryption with the low security, image encryption algorithm with a high-security is proposed. First, the key of the cryptosystem is generated from the original plaintext. Next, using the generated secret key and iteratively generates the key stream required by the cryptosystem with the 2D-LSM, Finally, get the ciphertext image using the encryption algorithm that scramble and diffusion simultaneously, and each ciphertext value is calculated from the key stream and the plaintext. The results show that ciphertext of the encryption algorithm has better correlation, and a good security.
Optimal Collision Avoidance Strategy for UAVs Against Dynamic Obstacles

The ability of autonomous obstacle avoidance in complex dynamic environments has become a key problem to be solved urgently for UAVs. Aiming at the shortcomings of existing obstacle avoidance algorithms that avoid obstacles by adjusting the direction or speed, based on the speed obstacle method, a hybrid obstacle avoidance strategy with simultaneous direction and speed adjustment is proposed. First, according to the principle of the speed obstacle method, it is judged whether there is a flight conflict between the UAV and the dynamic obstacle; then in the scenario of a single dynamic obstacle, the mathematical formula is used to accurately derive the difference between the heading and the speed that the UAV needs to adjust to avoid obstacles. Under the constraints of the objective function, the optimal obstacle avoidance strategy is solved, and then the 3rd B-spline is used to perform the path smoothing operation; the final simulation shows that under the threat of a single dynamic obstacle, the proposed method in this paper The algorithm is effective and efficient.
Research on image preprocessing based on 3D reconstruction of sequential X-ray images

The original X-ray images have problems such as high noise, narrow gray range, low contrast, and blurred edges, which seriously affect the reconstruction accuracy when using them for 3D reconstruction. Therefore, this paper explores the image preprocessing method of three-dimensional reconstruction of sequential X-ray images, an improved median filtering algorithm with adaptive neighborhood threshold is proposed, which effectively solves the problem of rapidly processing high pulse noise in X-ray images while retaining the edge detail features of the target, and then highlight the difference between the image target and the background through the histogram equalization method. Finally, the Canny operator is used to extract the edge of the target. Experiments show that the preprocessing process can effectively remove the noise of the X-ray image, better highlight the edge details of the target, and can extract clear edge information, which is convenient for subsequent 3D reconstruction work.
Intelligent Recognition Algorithm of Specific Pattern Content Information based on Mobile Terminal Equipment

In order to solve the problem of poor intelligent recognition effect of specific mode content information, an intelligent recognition algorithm of specific mode content information based on mobile terminal equipment is proposed. The mobile terminal equipment is used to collect network information, identify the unidirectional characteristics of specific mode content information, formulate a specific mode content information feature evaluation system based on single features, mine and classify multi-directional information, and optimize the intelligent recognition steps of specific mode content information. Finally, experiments show that the intelligent recognition algorithm of specific mode content information based on mobile terminal equipment has high practicability and accuracy, Fully meet the research requirements.
Generative Adversarial Networks Based on Human-Computor Interaction

In order to improve the generation quality and personalization of generative adversarial network, this paper proposes an open generative adversarial network (OpenGAN) based on human-computer interaction, which adds human subjective evaluation into the training. A subjective penalty function is added to the original generator loss and the smoothing network layer is designed to reduce the impact of loss mutation in the interaction. Our results show that the IS value on ADE20K, Cityscape and other datasets increases by 61% on average, while KID and LPIPS decrease by 32% and 44%, respectively.
Improved sparrow search algorithm by hybrid equalization optimizer

Aiming at the problems of low population diversity, easy to convergence at a local optimum and slow convergence speed of Sparrow search algorithm (SSA), Improved sparrow search algorithm with hybrid equilibrium optimizer (ISSA) is proposed. Firstly, the information interaction among sparrow and the population diversity is strengthened by fusing equilibrium optimizer (EO) into sparrow algorithm search mechanism. Then, the generation mechanisms of the new population obtained to guide the orientation of the population and to improve the convergence speed of the algorithm. Finally, to verify the performance of our ISSA, four unconstrained classical benchmark functions are adopted, and three algorithms are selected for analysis together. Experimental results are considering both convergence and accuracy demonstrate the effectiveness of ISSA for global numerical sums.
Research on Identity Authentication of IoT Devices based on Blockchain

Identity authentication is one of the methods to protect the security of devices in the Internet of Things. In view of the security and privacy protection problems caused by identity authentication in traditional centralized platforms, this paper combines the blockchain technology with the existing authentication model of the Internet of Things. Firstly, the authentication strategy is formulated according to the attributes of the IoT device; secondly, the use of cryptography technology to ensure transmission security, so as to ensure the data transmission security, reliability and authenticity of the Internet of Things, and realize the transmission security, network security and data security of The Internet of Things devices; finally, the model anti-attack ability is tested and analyzed from the aspects of performance and security.
visual perception preserved denoising network in Image translation

Image denoising is a fundamental problem in computer vision and has received much attention from scholars. With the fast development of convolutional neural networks, more and more deep learning-based noise reduction algorithms have emerged. However, current image denoising networks tend to apply image noise reduction only in the RGB color space, ignoring the information at the visual perception level, making the images generated by these algorithms too smooth and lacking texture and details. Therefore, this paper proposes a novel noise reduction network in the image translation area using deep learning feature space instead of the traditional RGB color space to restore more realistic and more detailed texture information in generated images. The network contains a visual perception generator and a multi-objective optimization network. The generator includes a multiscale encoding-decoding sub-network, which extracts high-level perception features from input images. The optimization network contains content consistency loss, multiscale adversarial generation loss, and discriminator feature alignment loss, which effectively retains detailed texture information in the images. We synthesized noise of suitable intensity based on publicly available data sets and conducted multiple experiments to verify the effectiveness of the algorithm. The experimental results show that the proposed algorithm significantly improves textures and details in denoised images. The algorithm removes a large amount of noise information while preserving lots of perceptual information at the visual level, generating more realistic images with detailed texture features.
Strip stitching algorithm of filter array multispectral image

Aiming at the problem of low image stitching accuracy due to the difficulty of extracting strip features from multispectral images of filter array, a strip stitching algorithm for multispectral images of filter arrays is proposed. Firstly, the effective range of each band of the multispectral image is determined, and the homography matrix is calculated using the geographic coordinate information of the image vertices and the vertex coordinates to project the image. Secondly, Scale-invariant feature transform (SIFT) algorithm was used to extract matching points of projected images, and the mean value of the coordinate difference of matching points was calculated as the translation relation between images. Finally, the projection transformation is performed on the single band bands in turn, and the projected images are stitched with inter-image translation to obtain a large area single band image. Theoretical analysis and experimental results show that this method can effectively improve the the stitching accuracy of multispectral images of filter arrays and has a high image stitching speed.