Reviewerly/exHarmony huggingface dataset:
Anomaly detection and recognition of video surveillance images based on deep learning

In order to solve the problems of pool generalization ability of traditional algorithms and high cost of manual inspection for abnormal image detection in remote video surveillance, this paper proposes an algorithm for abnormal image detection in video surveillance based on deep learning. First, the convolutional neural network based on VGG-16 uses the he_normal method to initialize the weights, and then the self-made datasets is preprocessed and input into the convolutional neural network for training, and finally an image for detecting video surveillance is obtained Model of abnormal interference. Experimental results show that this method can detect abnormal interference such as overexposure of brightness, color distortion, and video freezes in video surveillance, with an accuracy rate of 86%.
A real-time high-precision object tracking algorithm based on SiamFC for UAV images

The object tracking algorithms based on deep learning represented by SiamFC have demonstrated promising tracking capabilities. However, convolution networks take up a lot of memory, and it is difficult to run in real-time tracking on UAV platforms. Targeting this issue, we propose an object tracking algorithm called SiamUAV based on the siamese network in this paper. Firstly, based on the backbone network of the SiamFC algorithm, depthwise separable convolution is adopted to improve the tracking speed. Secondly, a spatial and channel squeeze & excitation block is introduced as an attention mechanism so that the backbone network can dynamically adjust to improve the tracking performance. Lastly, the algorithm is deployed on the NVIDIA Jetson AGX Xavier embedded platform with acceleration by TensorRT. The algorithm achieves essentially the same accuracy as the SiamFC algorithm. The tracking speed is improved by more than 70%, reaching 59 FPS on the embedded platform. This provides an excellent tracking speed while ensuring tracking accuracy.
The model construction of cloud computing technology in computer network security

The application of cloud computing technology in the computer network security storage can effectively improve the security of the computer network data. In the cloud computing technology, the encryption algorithm and random sequence can effectively encrypt the data and set the user rights, so that the computer network data is guaranteed by security. The application of cloud MC-R technology and user-side RSA technology confirms the confidentiality of cloud computing technology.
Self-supervised learning with consistency loss for improving GANs

After much research and advancements, GANs have achieved great success but still face many challenges. In this paper, we adopt self-supervised learning based on rotation angles to overcome the catastrophic forgetting of the discriminator. Self-supervision encourages the discriminator to learn meaningful feature representations that are not forgotten during training. Meanwhile, this paper adopts consistent adversarial training to alleviate the mode collapse of the generator. The consistency constraint condition encourages the discriminator to explore more features, which helps the generator achieve more significant improvement space. This deep generative model improves unsupervised image generation tasks by simultaneously alleviating two critical issues in GANs. Experimental results demonstrate that our model achieves competitive scores.
Application analysis of cloud computing technology in computer network security storage

Cloud computing technology integrates computer network technologies such as load balancing, network storage, utility computing and distributed computing, which has scalability, versatility, large-scale, virtualization and can provide reliable and cheap services. The services of cloud computing include IaaS (infrastructure as a service), PaaS (Platform as a service), and SaaS (Software as a service). The society is very concerned about the security problem of the cloud, which specifically involves the security level of the cloud, the application service layer and the infrastructure layer [1]. In view of this problem, China has established BIM cloud computing Center, 360, Green Alliance, Qiming Star Chen, Ruixing and other companies have formulated network security solutions, and appeared Ali Cloud, BIM Cloud and Baidu Cloud, thus providing an important guarantee for the security of cloud computing technology applications. This paper explains the application analysis of cloud computing technology in computer network security storage and provides a series of methods for how to apply it.
Research and implementation of ETL algorithm based on kettle cluster

With the continuous development of big data technology, it is widely used in different computer engineering projects, and the use of data warehouses to complete the data exchange and integration of different data sources is the first step in the follow-up work. The core of the entire big data platform is Data-based. ETL (transformation and loading, extraction) technology is the basic technology for building a data warehouse [1]. Kettle is a very popular open source ETL tool. Using ETL tools can greatly improve production efficiency. ETL task scheduling is the most important part of Kettle to ensure performance. At present, ETL task scheduling in Kettle has the following problems: First, as the amount of ETL task data continues to increase and the data continues to increase, single-machine scheduling ETL results in many tasks that cannot run in time or can not run; secondly, the deployment of ETL task scheduling algorithms for Kettle application clusters is more researched. In the end, Kettle uses round-robin scheduling by default when scheduling, and the performance of all nodes in the cluster is the same by default. It is easy to cause nodes with fewer resources to run more tasks, and nodes with more resources to run less tasks. Load imbalance, which affects the running of tasks Time, causing delay. This paper studies the ETL task scheduling algorithm based on Kettle, and proposes an initial weighted task scheduling (IWTS) algorithm and a task scheduling algorithm based on threshold optimization ant colony algorithm (Task scheduling algorithm based on threshold optimization ant colony algorithm, TSTOACA). ), to solve the problem of task scheduling cluster load balancing. The initial weighted task scheduling algorithm is based on node resource weights at the beginning of task scheduling, while taking into account load balance and smoothness; threshold-based optimization ant colony algorithm is to dynamically seek through optimized ant colony algorithm when the entire cluster resource usage exceeds the threshold Global optimal task scheduling strategy to achieve load balance. Compared with the default scheduling, the adaptive load balancing algorithm proposed in this paper improves the efficiency by about 8%~12%.
NDANN: efficient SSD-based approximate nearest neighbor search through navigation

Graph-based Approximate Nearest Neighbor Search (ANNS) algorithms have attracted more attention due to their better performance. Numerous ANNS optimization methods have been proposed by researchers. However, Current graph-based ANNS algorithms still cannot index billion-scale datasets on a single server with 256GB RAM. Although several researchers have investigated on this problem, we believe there is still an improvement in reducing disk accesses. In this paper, we provide a fast navigation layer to decrease the number of disk accesses by assisting query points in swiftly reaching the range of strongly connected components. Compared with the state-of-the-art ANNS algorithms, NDANN reduces the mean latency by about 20% under the same recall.
A multi-view face detection and expression recognition method with improved RetinaFace

To address the problems of large detection error and low recognition accuracy of multi-view face expression recognition, this paper proposes a multi-view face detection and expression recognition method based on RetinaFace. Firstly, it relies on ResNet-50 as the backbone feature extraction network of RetinaFace for multi-view face detection. This effectively prevents overfitting problem caused by the heavyweight network ResNet-152 and the false detection problem caused by the lightweight network MobileNet-0.25 in the original RetinaFace algorithm. Secondly, the multi-task loss function is adjusted to ensure accurate detection rate while speeding up the detection rate. Finally, the detected face images are fed into the ResNet-50 network for expression feature extraction and classification. In comparison with the baseline algorithm, the improved RetinaFace algorithm shows good robustness in terms of detection accuracy as well as detection time; it also shows good generalization in terms of expression recognition rate on the Multi-PIE facial expression datasets.
Detection algorithm for anomaly sub-sequences in energy consumption data stream

For the data streams retrieved from the various sensors in energy cyber physical system, an anomaly detection algorithm based on time series extremum was proposed. Firstly, for a single data stream, the algorithm identifies the extreme points of the data stream in the sliding window, and marks off the abnormal sub-sequences according to these points. Then the eigenvalues of the sub-sequences are calculated to establish the feature space. Several anomaly measures are defined, so the sub-sequences can be sorted by the measures in the feature space. Finally, based on the correlation between the sensors points, the anomaly points on each work section are detected. The algorithm can make up for the limitation of other point anomaly detection algorithms by detecting the abnormal sub-sequences, and reduces the probability of false alarm and missing alarm in data stream analysis. Experiments show that the algorithm is effective and feasible.
Research on path tracking technology of mobile robot

In the application practice of robot autonomous navigation, the path tracking algorithm as the control layer is the key technology to realize the intelligence and practicability of robot, which directly determines the bionic level of robot self perception and autonomous action. Path tracking can be regarded as a control problem. In this paper, the control problem of mobile robot path tracking is studied. Firstly, the multi degree of freedom kinematics model of robot mobile mechanism is established, the path tracking control system is decoupled into speed following closed-loop control and steering angle control subsystems, and a path tracking controller based on optimization theory is proposed, The control algorithms are designed for the tasks of robot tracking specific target points and tracking arbitrary path curves, and the tracking performance of the controller based on the optimization theory is verified by simulation analysis. The simulation results show that the control algorithm has the advantages of simple algorithm, good real-time performance and strong autonomous tracking and navigation ability in robot path tracking.
Semantics-enhanced algorithm for skeleton-based action recognition

Skeleton-based action recognition has attracted the attention of many researchers. In the process of extracting skeleton features, most methods use the first-order features (the joint position of the skeleton) or the second-order features (the length and direction of the skeleton) to represent the skeleton, while ignoring the importance of skeleton semantic features to action. In this paper, a new skeleton appearance semantic feature is proposed to describe the appearance feature of human skeleton. An attention mechanism of skeleton appearance semantic features is further proposed, which integrates channel features to highlight the overall difference. In addition, a method is used to integrate the semantic features of skeleton appearance with the position and velocity features of skeleton joints, as well as the constructed joints type and frame index, so as to improve the representation method of skeleton semantic features. The experiments were carried out in two public skeleton action recognition data sets (NTU-RGB+D 60 and NTU-RGB+D 120), and the recognition accuracy was higher than that of the baseline model SGN.
An enhancement method for underwater polymetallic nodule images

Due to the absorption and scattering of light in the water environment, the polymetallic nodule images were of low quality, such as color deviation, uneven illumination and blur, which was negative for the extraction and abundance calculation of polymetallic nodules. An improved image color correction method based on the dual transmission underwater imaging model is proposed. Its key lies in the estimation of direct component transmission (DCT) and backscattering component transmission (BCT) in the model. BCT is estimated in red, green and blue color channels, which are no longer considered equal. The DCT is estimated based on the mean pixel value and BCT of the background light region. In addition, the retinex algorithm is used to eliminate the illumination image and solve the imbalance of illumination and shadows caused by photoshooting with supplementary lighting. The image contrast is enhanced by using the contrast limited adaptive histogram equalization (CLAHE) algorithm. The polymetallic nodule images are used to evaluate the proposed method qualitatively and quantitatively. The results demonstrate that the proposed method has high performance in image visibility enhancement and color restoration. It is beneficial to the extraction and distribution analysis of polymetallic nodules.
Local privacy protection system framework based on encryption algorithm library

How to effectively protect the privacy of data, establish a data center platform and solve the problem of vulnerable deep learning has become an urgent problem to be solved. In particular, desensitization of data is an effective way to avoid data privacy disclosure. This study puts forward a new solution. Firstly, this article identifies the sensitive data, then extracts the sensitive data from the original data, constructs the desensitization technology library of the sensitive data, then desensitizes the sensitive data, and finally transfers the data to the model for training, so as to deliver the deep learning model for training without changing the original distribution of the data, it avoids the problem of privacy disclosure caused by model attack.
Monitoring image anomaly detection based on lightweight network MobileNet

At present, there are problems in remote video surveillance that it is difficult to detect image anomalies in complex scenes, and traditional algorithm functions are single (only for a certain specific scene or a certain type of abnormal image). Moreover, the currently commonly used convolutional neural network is deployed on the monitoring device, there are problems such as insufficient device memory and low computing power. In response to the above problems, this paper proposes a recognition method that applies the MobileNet convolutional neural network to the detection of abnormal images in surveillance video. The off-line enhancement method is used to reverse and translate the abnormal images in the surveillance video. The data set can be reasonably expanded, the problem of data imbalance can be solved, and the detection accuracy and generalization performance of abnormal images can be improved. The results show that this method can detect abnormalities such as freeze, overexposure, blur, and colorific distortion in remote video surveillance, with an accuracy rate of 91.75%, and can reduce the amount of model parameters and facilitate model deployment.
Improved artificial potential field algorithm for intelligent vehicle path planning

Artificial Potential Field (APF) algorithm is one of the algorithms in the field of intelligent vehicle path planning. It can plan a safe path without collision for intelligent vehicle during driving from the start station to target station. However, the original APF algorithm also has some limitations. By changing the direction of the repulsive force and setting the initial conditions, this paper solves the inherent shortcoming of original APF algorithm, such as the target position is unreachable and easily fall into the local optimum during path planning. And due to the actual obstacle is not a particle, the obstacle safety distance is also proposed in this paper. Simulation results in MATLAB software show that the improved APF algorithm is effective. Then the improved APF algorithm is applied to the field of the intelligent vehicles path planning. The path following simulations in CARSIM software verify the followability of the target path which is planned by the improved APF algorithm. Therefore, the improved APF algorithm is effective and feasible for intelligent vehicle path planning.
Federated learning based multi-domain image-to-image translation

Multi-domain image-to-image translation is widely used in many areas. However, existing methods need to collect data from different organizations which may violate privacy. Moreover, data silos and data protection regulation make it more difficult to centralize data. To solve this problem, this paper proposes federated learning based multi-domain image-toimage translation, which takes advantage of available data owned by different organizations while protecting data privacy. To improve the quality of translated images, a multi-stage multi-domain translation model is also proposed in this paper. The model uses encoder to extract domain-invariant features and domain specific decoders to generate target domain images. Experiments show that the proposed method achieves better translation results without compromising data privacy.
SDM: A frequent subgraph mining algorithm for sequential directed graphs

Current research on frequent subgraph mining mainly focuses on the mining of undirected graphs, but in fact, the research on the mining of directed graphs has more practical significance. A new algorithm for mining frequent subgraphs based on directed graphs, SDM, is proposed. This algorithm uses breadth-first search strategy to mine frequent subgraphs by establishing a hierarchical tree space. And provide an efficient method in the process of frequent subgraph generation and counting. Experimental research shows that compared with other frequent subgraph mining algorithms, SDM has achieved a significant performance improvement.
Attention based CNN-LSTM network for video caption

Due to the demand and wide application of video caption in various fields such as video retrieval, content recommendation, risk management, etc., how to extract a comprehensive and highly generalized description of the information has been an active research area for many years. In this paper, we propose a new model that includes the fusion of convolutional neural network and attention mechanism. The special features are extracted from multiple perspectives such as scene, target and behavior in the video, and combined with key frame semantic information to reduce the interference of redundant information and complete the feature representation of the information, and the attention-weighted fusion of the input of the above four features is input to LSTM decoding to finally generate the video content title. A multi-baseline comparison of the two public datasets is performed. Multiple evaluation metrics prove that our model outperforms other models and also show that the information representation of this paper.
Coordinated and specific autoencoder for cross-modal retrieval

This paper considers the problem of cross-modal retrieval, e.g. using a text query to search for images and vice-versa. Existing approaches usually learn a common subspace where the shared parts of different modalities can be directly compared. However, no previous works explicitly show that the learned space contains only the common information but without the modality-specific information. And the division between these two types of information would benefits the task of cross-modal retrieval. In this paper, we present a COordinated and Specific autoEncoder (a.k.a. COSE) that can distinguish the common part from modality-specific part of different modalities. The proposed model COSE consists of two subnetworks, each with two representation layers. The common representation layer learns the common patterns shared within different modalities. And the modality-specific representation layer learns the modality-specific patterns owned by individual modalities. We evaluate our model on three publicly real-world datasets with the task of cross-modal retrieval. The extensive experiments demonstrate the effectiveness of our COSE.
Design of college mental health online service platform based on cloud computing technology

The university online college mental health service platform is based on cloud computing technology, so it can effectively conduct data collection and data processing. Using the Internet of Things sensing equipment and sensors, students' psychological test data can be uploaded to the database of the university mental health online service platform. Moreover, using the K-means clustering algorithm. In the results of the simulation experiment, it can be seen that the online mental health service platform of universities has certain effectiveness, and the mental health management system and the online mental health consultation service system have also improved the mental health services of universities to a certain extent.
Database programming technology based on computer software engineering analysis

This paper systematically analyzes the specific application of database programming technology in computer software engineering, so as to provide technical support for computer software engineering and realize the intelligent development goal of computer software engineering. Using database programming technology can drive the database system to run at high speed, so as to maximize the value of computer software engineering data. Database programming technology can efficiently store software data information and establish a comprehensive database information system, so as to improve the overall performance of computer software engineering. Therefore, increasing the application and optimization of database programming technology can further develop the core technology of computer software engineering and promote the technological innovation of various industries.
The effective application of computer database technology in information management

With the development of society, the use of computer technology is becoming more and more common, and it has had its own place in various fields and has a wide range of applications [1]. The application of computers can effectively improve people's work efficiency, and also have unique advantages in the field of information management. Through the development of computer technology, the information can be effectively controlled, so as to certain control and regulate the network. This paper effectively discusses and expounds the application of computer technology in information management.