Reviewerly/exHarmony huggingface dataset:
Bias-tolerant track association based on multiple model features

In the distributed fusion system, the local tracks generated by each local sensors enter the fusion center for track association. When the target has a strong maneuverability and the system bias is large, local tracks’ information is not accurate, and the real state of the local track cannot be accurately represented by the simple use of information like position, speed, etc. Data association performance with these motion state features will inevitably be restricted. In this paper, the interactive multi-model (IMM) algorithm used in tracking maneuvering target is used to represent the motion pattern or characteristics of the local tracks for association. Simulation results show that the proposed algorithm can obtain better results compared with traditional methods when the tracks are complex and the system bias is large.
Design and Implementation of IT Health Diagnosis System Based on Knowledge Graph and Artificial Intelligence Technology

This paper proposes a knowledge modeling and analysis method of IT equipment health management based on knowledge graph. The ontology framework of maintenance knowledge map is constructed according to entity relationship model. Then this paper integrated knowledge graph, text classification, Bayesian network and other technologies applied to IT equipment fault knowledge management, fault location, fault diagnosis reasoning. At the same time, based on the actual maintenance work order data of IT enterprises, good practice results have been obtained from the aspects of statistical analysis and correlation analysis. The relational database (RDB) data is converted into Resource Description Framework (RDF) quadruple for storage. Visual analysis and management of knowledge graph are realized. This study shows that the integrated approach provides effective guidance for IT equipment fault knowledge management and diagnosis practice. The research results of this paper can provide decision support for maintenance personnel and management personnel to carry out equipment health management.
UAV path planning based on GA search

In order to ensure that the UAV cluster in the attempted formation flight, as far as possible to maintain electromagnetic silence to reduce the emission of electromagnetic wave signals, the positioning of the UAV is particularly important, and the existence of factors affecting the flight cost and flyable time of the UAV makes the UAV path optimization has some significance. The UAV position is scattered in a three-dimensional right-angle coordinate system, this paper assumes that there are three UAVs with known position and number as the signal source to transmit signals, and these three UAVs are used as mass points to determine a plane, and the direction of flight of the UAV that needs to be displaced to the plane is calculated according to the angle. The remaining UAVs are oriented and displaced to the previously determined plane in the same way. In this paper, a genetic algorithm is used to minimize the path of the UAV flying to a fixed point in the same plane, to optimize the solution of the UAV path, and to simulate the optimal path of the UAV flight.
Aspect-level Sentiment Analysis Based on Static Pruning of Syntactic Trees

Aspect word sentiment analysis is a hot research topic in the direction of natural language processing, and many researchers have used syntactic structural information to establish the connection between aspect words and context in recent years. However, there are some shortcomings of the aspectual word sentiment analysis method using fused dependency syntax. For example, word nodes unrelated to specific aspectual words still exist in the syntactic dependency tree, and some noise is still introduced when encoding the context based on the dependency tree structure using the graph structure encoder. At the same time, the syntactic tree structure itself is sparse, which wastes a lot of computational resources when performing parallel computation. Therefore, this paper proposes a syntactic dependency tree pruning method, which centers on the shortest dependency path from the root node to the aspect words, and keeps the nodes directly associated with the aspect words as the pruned syntactic structure. An Edge-enhanced Graph Attention Convolutional Networks (EGAT) is also used to encode the dependency structure of the dependency syntax and the class of edges. The model achieves competitive results on the publicly available dataset, 14lap, 14res, 15res and 16res published by Semantic Evaluation.
Establishment and Simulation of Intelligent Control Algorithm Model of Autonomous Power Supply System Based on Random Forest

The solar panel is the core device of photoelectric conversion and thus becomes the front end of the autonomous power supply system. Therefore, this paper constructs a solar energy autonomous power supply system to solve the problem of low power supply efficiency. The MPPT controller is used in the autonomous power supply system, and the maximum power of the solar panel is tracked through the intelligent control algorithm, so that the operation state of the power supply system is stable. In this paper, the random forest is used to improve the fuzzy control algorithm to improve the efficiency of the solar autonomous power supply system by optimizing the tracking ability of the algorithm through the high-precision prediction ability. In this paper, the simulation experiments of three MPPT control algorithms, including the disturbance observation method, the fuzzy control algorithm and the improved fuzzy control algorithm, verify that the improved fuzzy control algorithm of random forest has higher efficiency and speed in MPPT control than the other two algorithms, which can maintain the stability of the autonomous power supply system.
Anti tampering identification algorithm for defect measurement data based on big data

In order to improve the ability to detect and identify the defect measurement data of the cloud composite service function chain platform and prevent tampering, a tamper resistant identification algorithm for defect measurement data based on big data is proposed. Construct a state parameter distribution and fusion model for the defect measurement data of the cloud composite service function chain platform, combine fuzzy composite parameter identification to conduct the fusion scheduling of the cloud composite service function chain platform, and construct a joint state parameter identification model for anomaly detection of the defect measurement data of the cloud composite service function chain platform. By short-term state parameter identification, adopt fuzzy dynamic feature reorganization and spectral feature extraction methods, Build an encryption model for preventing tampering of defect measurement data, use combination coding and key control methods to achieve dynamic link transmission control of big data, and achieve anti-tamper identification of defect measurement data on the cloud composite service function chain platform by big data detection and encoding encryption results. The simulation test results show that using this method to perform tamper proof identification of defect measurement data on the cloud composite service function chain platform has good decryption performance and encryption performance, reduces the probability of attack on the defect measurement data, improves information security and encryption transmission capabilities, and has good application value in the construction of the cloud composite service function chain platform and data encryption transmission control.
Image Style Transfer Based on Cyclegan

Generative Adversarial Networks (GAN) have powerful adversarial learning capabilities and are currently being used by more and more researchers. The style transfer of images is an important field in deep learning. This paper proposes an improved algorithm based on the classic Cyclegan network, adds a bottleneck structure, optimizes the network layer structure inside the generator, and compares it with the original Cyclegan network. Compared with the training, the results show that the improved Cyclegan network obtains more realistic images and more visual effects than the original Cyclegan network after training.
Security risk analysis and protection scheme of video transmission system

With the development of the information age, video transmission systems have been widely used. Video transmission systems are widely used in daily life, especially in large conferences, government systems, and military command systems. At the same time, for video transmission systems The attack methods of the video transmission system are also gradually developed, which poses a huge threat to the information security of the video transmission system. Combining with the composition of the video transmission system, the paper analyzes the security threats of the video transmission system from the preset backdoor attack of the video transmission system, electromagnetic leakage attack, power line carrier attack and transmission line attack, and aims at the current command hall and other needs to have multiple security levels. A protection scheme is put forward for the situation that the equipment is connected to the same large screen for display at the same time, which provides a reference for the design and protection of the future video transmission system.
Fusion Multi-scale Features and Attention Mechanism for Multi-view 3D Reconstruction

In order to solve the problem that it is difficult to carry out the reconstruction accurately and completely in the process of multi-view 3D reconstruction, we puts forward to a 3D reconstruction method that combined with multi-scale features and attention mechanisms. To begin with, multi-scale features are extracted from the input source and reference images using a Feature Pyramid Network that adds a bottom-up structure to obtain the connections between the different scales of the feature map. What’s more, the attention mechanism is used to optimize the cost volume regularization process and improve the accuracy of depth estimation. In the end, the improved network is trained. About the experimental results of DTU dataset, it can be seen that the 3D reconstruction results of this network reached 0.358mm and 0.366mm in Completeness (Comp) and Overall respectively, which are higher than traditional methods as well as some 3D reconstruction methods that are based on deep learning methods. At the same time, the results of visualization in the point cloud model have been significantly improved. In addition, experimental results on Tanks and Temples dataset show that this network also have a better generalization performance.
A two-dimensional convolutional gated recurrent unit based method for classifying malicious encrypted traffic

With the development of traffic encryption technology, more and more malicious traffic adopts encryption technology to hide their activities. To address the problems of existing deep learning models such as low training efficiency and poor real-time classification due to deeper hierarchy, this paper proposes a malicious encrypted traffic classification method based on Two-Dimensional Convolution Gate Recurrent Unit (TDCGRU). Firstly, the spatial features of individual packets are extracted using two-dimensional convolution; then the time series features between packets at the traffic level are extracted using the gated recurrent unit, so that the spatio-temporal features are extracted at the packet level and the flow level of the traffic respectively. Based on the public datasets CTU-13 and CICAndMal2017, the experimental results show that the accuracy of the method in this paper reaches 99.2%, and compared with the BGRUA model, the classification delay and training time are saved by 11.2% and 12%, respectively, while improving the classification effect.
Research on the Development and Programming of Experimental Learning Software Based on Computer Big Data Technology

In this paper, a computer network hardware virtual experiment system is developed using virtual reality technology, which has many characteristics, especially strong interactivity. The platform can provide a unified direct platform for computer programming in digital circuits, computer composition, operating systems, compiler principles, embedded systems, and interfaces, all of which involve the cultivation of computer system capabilities. The automatic determination of parameters and the practical application of model parameters are realized using multimode technology. The simulation layer dynamically selects several NOS as active states, provides services in parallel, and then determines the final effective response based on the processing results of each NOS, returning it to the underlying switch. Experimental evaluation shows that MNOS can effectively reduce the probability of successful SDN control layer attacks with limited latency overhead
Research on distributed model forecasting parallel framework based on adaptive genetic algorithm

With the progress of the Internet information age and the rapid development of Internet technology, adaptive genetic algorithms have been applied in various industries to process data in various fields. However, the traditional adaptive genetic algorithm has problems such as slow convergence speed and easy falling into local optimum. Based on the above background, this paper mainly studies an improved strategy of adaptive genetic algorithm based on a density parameter optimization algorithm. In addition, the proposed improved strategy mainly uses the nearest neighbor collaborative filtering algorithm to improve the average distributed parallel quality of the algorithm, and optimizes the distributed parallel mode and distributed parallel characteristics of the adaptive genetic algorithm respectively. Therefore, the improved strategy has the features of simplicity and high efficiency, fast convergence speed, and strong global search ability, having important research significance in the field of data analysis.
Pedestrian segmentation and shadow removal based on Bezier curve fitting for video image

Video pedestrian detection technology can be used to grasp crowd flow information in real time, which plays an important role in security, commerce, transportation and other aspects. This paper focuses on the problems of crowd occlusion and shadow interference in pedestrian detection. Firstly, a foreground extraction algorithm is developed which combines the Mixed Gaussian Model and the mean background model to obtain a complete initial foreground. Secondly, the pedestrian segmentation and shadow removal method based on Bessel curve fitting was developed according to the characteristics of the vertical projection accumulation curve of adhesion. The method proposed in this paper is tested by campus surveillance video, and the results show that the proposed method can effectively realize the segmentation and shadow removal of the adhesive crowd. Therefore, the individual person can be better obtained, which is conducive to the pedestrian statistics.
Application of Computer Aided Intelligent Translation in English Training System

In this paper, an English machine translation system based on human-computer interaction enhancement algorithm is designed. The hardware part uses the sub board as the processing core. Design Internet data controller. An 8b/10b translation decoder was used to implement the automatic translation process. The software part recognizes ambiguous words and builds ambiguous word matching function. English translation rules are formed by automatically matching the best meaning of ambiguous words. The M1 model is used to calculate the possible pairing probability between words in parallel corpora. The EM algorithm is used to obtain the maximum value of parameters under unknown data condition. The experimental results show that the proposed model has better performance than the existing methods, and can better meet the actual demand.
Imbalanced Word Counting Using MapReduce in Serverless Platform

MapReduce is one of the most widely used programming models for analyzing large-scale datasets. In recent years, serverless computing, especially Function-as-a-Service (FaaS), has received a lot of attention due to its advantages of scalability, low cost, and compatibility. In the serverless environment provided by the AliCloud platform, this paper investigates the different performances of MapReduce when processing massive files under different resource configurations, and proposes a solution to the data skewing problem of MapReduce. The results show that given the same other configurations, CPU cores and WORKERS have a significant impact on function response time: the larger the CPU core in the function configuration, the shorter the function response time, and the high concurrency of WORKERS can also significantly reduce the function response time. In addition, the size of the memory configuration has a negligible effect on the response time of the function. In addition, an optimization scheme is further proposed for the common data skew problem in MapReduce and explore its effect. This scheme can greatly improve the overall task execution efficiency when the data is seriously unevenly distributed.
Research on the Translation System of Spoken English Situational Dialogue Based on Computer Artificial Intelligence

In this paper, a sequence-to-sequence model is proposed to deal with continuous emphasis levels, and machine translation and emphasis translation are combined into a spoken English situational translation system. In the aspect of hardware design, the peripheral circuit is built with the chip as the core to form the speech recognition module. In the process of software design, relaxation endpoint method is used to improve the operation speed to realize floating point operation in the kernel. A database suitable for spoken translation system is constructed by using complex feature vectors, and remote data synchronization is realized by using SQL Server Mobile 2015 database merge replication technology. The system uses a translation engine to translate English and Chinese. There are nearly 200,000 bilingual sentences debugging in the system, which improves the language translation quality of the system.
Regularity Learning via Explicit Distribution Modeling for Skeletal Video Anomaly Detection

Anomaly detection in surveillance videos is challenging but important for ensuring public security. Different from pixel-based anomaly detection methods, pose-based methods utilize highly-structured skeleton data, which decreases the computational burden and also avoids the negative impact of background noise. However, pose-based methods lack an alternative dynamic representation akin to the explicit motion features, such as optical flow, employed by pixel-based methods. In this paper, a novel Motion Embedder (ME), a label-efficient scheme without extra annotation efforts, is proposed to provide a pose motion representation for the structured posed data from a probability perspective. Furthermore, a novel task-specific Spatial-Temporal Transformer (STT) is deployed for self-supervised pose sequence reconstruction. These two modules are then integrated into a unified framework for pose regularity learning, which is referred to as Motion Prior Regularity Learner (MoPRL). MoPRL achieves competitive results on multiple challenging datasets while minimizing computational costs. Extensive experiments validate the versatility of the proposed modules and provide insights for future research.
Explainable AI and Blockchain for Metaverse: A Security, and Privacy Perspective

The next-generation digital revolution is anticipated to be the convergence of Consumer Internet of Things (CIoT) platforms and Metaverse. The use of Metaverse in CIoT can offer a hyper-spatiotemporal, self-sustaining 3D virtual shared space for people to interact, work, and play. Despite the hype around CIoT-inspired Metaverse, security and privacy concerns are seen as the two biggest obstacles in the communication infrastructure and information gathering procedures. The eXplainable Artificial Intelligence (XAI) and blockchain have the potential to reshape and transform the CIoT-inspired Metaverse by bringing significant enhancements in terms of explainability, interpretability, transparency, traceability, and immutability regarding data and communications. In this paper, we first discuss about the security and privacy issues in CIoT-inspired Metaverse. Second, we discuss the importance and properties of XAI and blockchain with a use case to demonstrate the benefits of our proposed architecture to tackle the aforementioned obstacles. Finally, we highlight the future research directions in building futuristic CIoT-inspired Metaverse.
Identity Consistency Multi-Viewpoint Generative Aggregation for Person Re-Identification

Person re-identification is the task to retrieve a given person in multiple non-overlapping cameras. Due to viewpoint variation from different cameras, the intra-class variance and inter-class similarity of human images are two critical factors that limit the accuracy of person re-identification. In order to simultaneously resolve these issues, we propose a multi-viewpoint aggregation model, which aims to extend the query method of Re-ID task from single to multi-viewpoints to deal with the various viewpoints and explore inherent gallery information for query optimization. And for constructing multiple auxiliary query images with complementary viewpoints, we design a novel identity consistency pose transfer framework based on a pseudo Siamese structure and trained by a specific Re-ID guided meta-learning pipeline. The goal is to keep the identity consistency between initial query and generated images by enhancing identity-related representation through feature learning and reducing the domain gap between generated and original images. Extensive experiment results indicate our method achieves the Rank-1/mAP performances on Market-1501 (96.62%/93.26%), DukeMTMC-reid (93.45%/87.89%) and CUHK03-labeled (88.26%/87.97%), which outperforms the state-of-the-art based on single viewpoint Re-ID methods.
Postdigital Afterlife

This entry begins by exploring the context, understanding, and the development of postdigital afterlife. It examines postdigital afterlife in relation to symbolic immortality. It describes the shift from digital immortality to postdigital afterlife and then explains current perceptions of postdigital afterlife. It then provides a summary of postdigital afterlife creation and the ways in which this has developed over time. The final section presents some of the ethical and legal complexities of creating and managing a postdigital afterlife and postdigital immortal.
Ah, Alright, Okay! Communicating Understanding in Conversational Product Search

When talking about products, people often express their needs in vague terms with vocabulary that does not necessarily overlap with product descriptions written by retailers. This poses a problem for chatbots in online shops, as the vagueness and vocabulary mismatch can lead to misunderstandings. In human-human communication, people intuitively build a common understanding throughout a conversation, e.g., via feedback loops. To inform the design of conversational product search systems, we investigated the effect of different feedback behaviors on users’ perception of a chatbot’s competence and conversational engagement. Our results show that rephrasing the user’s input to express what was understood increases conversational engagement and gives the impression of a competent chatbot. Using a generic feedback acknowledgment (e.g., “right” or “okay”), however, does not increase engagement or perceived competence. Auto-feedback for conversational product search systems therefore needs to be designed with care.