Reviewerly/exHarmony huggingface dataset:
Event detection model based on graph attention network and graph convolutional network

During extracting dependencies, the graph convolutional network (GCN) cannot filter information according to the different importance of words for event detection, which will cause information redundancy. As the number of GCN layers increases, the difference between nodes becomes smaller and smaller which will lead to the over-smoothing. In order to solve these problems, an event detection model combining Graph Attention Network (GAT) and Graph Convolutional Network was proposed. Firstly, the text was encoded and represented by pre-training BERT (Bidirectional Encoder Representations from Transformers) model. Then GAT and GCN were constructed in parallel to extract sentence dependencies and feature vectors and mine the different degrees of importance between each word and the trigger words. After that, the redundant information of different dependent syntactic structures were filtered through the gating mechanism. Finally, the trigger word extraction result was obtained based on the fusion vector of GAT and GCN. In the public ACE-2005 English dataset, the F1 value of the event detection experiment using the proposed model is 75.2%, which is 1 percentage point higher than that of BGCN (trigger detection based on Bert and Graph Convolution Network), and is 6.4 percentage points higher than the result of the classic model Cross-Event. By this, the validity of the model is verified.
DeepFFMS: A Parallel Model for Advertise Click-Through Rate Prediction

As a metric to measure the effect of advertising, the click-through rate has always been concerned by the company's operators, and the algorithm for estimating the click-through rate (CTR) of the advertisement can judge the expected benefits brought by the advertisement in advance. In this paper, we propose a parallel-structure click-through rate prediction model that enables selection of combined features. The proposed model, DeepFFMS, is mainly composed of the optimized field factorization machines and the popular neural network structure in recent years. Compared with other parallel-structure click-through rate prediction models, DeepFFMS uses parameters similar to attention mechanism training to eliminate useless feature interactions or feature interactions that have a negative impact on click-through rate prediction, thereby improving the accuracy of the model. After comprehensive experiments on open source advertising data sets, it is proved that DeepFFMS is superior to other similar models in the problem of advertising click-through rate estimation.
Algorithm Negative Effects and the Governance in Digital Platforms

Algorithm governance is the key to building a sustainable digital platform. The rise of Bayesian prediction to artificial neural network model shows that algorithm design is entering the stage of intelligence, autonomy and high complexity. As the core technology supporting the digital platform, the algorithm has rich application scenarios, such as: supply and demand matching mode based on algorithm push, gig labor mode based on algorithm control, and life scenarios based on algorithm design. However, difficulties such as algorithm black box, algorithm bias and the digital divide also hinder the healthy development of digital platforms. Algorithm governance is to build a digital governance system in which multiple agents participate, aiming at the negative effects brought by the application of the above algorithm technology.
Anomaly Detection of Credit Data based on Sparse Subspace Clustering Undersampling

The promotion of digitalization has brought many emerging risks to the internet finance and other fields. For example, the fraudulent behavior in credit data can be regarded as outliers, which means that outliers themselves have very important significance. However, due to the high dimension of the real data set and the small number of outliers, most anomaly detection algorithms directly based on clustering are not effective, so it is necessary to find a method that can effectively solve the anomaly detection of non-balanced credit data sets in high-dimensional space. This paper detects outliers in credit data based on sparse subspace clustering undersampling, uses it to cluster high-dimensional and unbalanced credit data sets, uses clustering results as undersampling means to construct balanced data sets, and then uses classifier to detect outliers. Finally, the effectiveness of the proposed algorithm in credit data outlier detection is verified by comparative experiments, which makes up for the shortcomings of traditional clustering and high-dimensional space outlier detection algorithms.
Lightweight and robust MQTT protocol authentication model suitable for connected portals

One of the most used protocols in smart homes is the MQTT protocol. This protocol was developed to answer the issues of limited resources of connected objects by enabling data exchanges between objects in IoT networks. However a huge problem that IoT devices face is security. Due to the sensitive information that is transmitted and stored in IoT devices and the limitation in computational resources (CPU, memory), they are exposed to several security threats. Thus, any compromise happens to these devices, will affect the user. Although the protocol incorporates some security bricks such as pair authentication (username, password), access control lists (ACLs) and SSL/TLS encryption, its implementation in smart home networks presents many vulnerabilities related to the only authentication mechanism it offers. In this paper we propose a dynamic authentication model of the MQTT protocol taking into account the resource limitations of connected objects. This protocol is based on dynamic one-time passwords (OTP). (OTP) dynamic passwords. For the robustness of our authentication algorithm, we exploit the random number generation and processing functions, the key exchange principle and the HMAC-SHA256 hash function.
Region Portrait Segmentation Method based on boundary Search Network

Human image segmentation is widely used in automatic driving, pedestrian detection, medical imaging and other fields. In the actual segmentation, the key of human image segmentation is to determine the human body region and precisely define the edge contour of the body. From the perspective of human body region, the approximate region of human body is firstly constructed by human body boundary contour, and then the edge region is modified by region growth method, which can solve the problems such as segmentation noise and edge roughness generated in the segmentation process. Based on this, we use a boundary and region segmentation fusion algorithm to test the person classification image on the VOC2007 set, which proved the effectiveness of the segmentation algorithm.
A Query Optimization Method for Blockchain-Based Traceability System

Blockchain has been widely applied in many fields such as traceability systems and supply chain management due to its characteristics of traceability and non-tamperability. Compared with other application scenarios, the query requirements in the traceability scenario are more complex. The use of traditional blockchain query methods in traceability applications suffers from problems such as low performance, and the existing query optimization methods on blockchain are unable to improve the query performance because they do not consider the needs of complex queries in traceability scenarios. In this paper, we propose a query optimization method for fast traceability on the blockchain by improving the traditional block structure and developing a new query algorithm. A traceability system for Chinese herbal medicine is developed for experiments, and the proposed method is compared with the traditional query method on this system. The experimental results show that the query performance of the method is better than that of the traditional query method.
Learning Representation for Clustering via Dual Correlation

Contrastive learning is a self-supervised learning method used to learn the general features of a dataset without labels by letting the model learn which data points are similar or different. It constructs labeled samples through data augmentation. Therefore, the core and difficulty of self-supervised contrastive learning are to construct high-quality contrastive samples, especially negative sample pairs. In this paper, a cluster-based contrast sample selection method is proposed, which selects the positive contrast samples from the same cluster and the negative contrast samples from different clusters. By minimizing the forward contrast sample loss and maximizing the reverse sample loss, the deep representation clustering result of the image is obtained. The experimental results show the excellence and adaptability of the algorithm.
Research on high-precision lightweight speech recognition model with small training set in Multi-person conversation scenario

It is easy to overfit the neural network and the accuracy of the speech recognition model is not high when the neural network is trained on small data sets in multi-person conversation scenarios. This paper proposes a model of Densely connected feedforward convolutional networks (DCFCN). The deep features are gradually extracted by dense connection compression network, and the shallow features and deep features are combined to solve the overfitting problem of deep neural network under the training condition of small data sets. A feedforward neural channel is added to input the initial information of text content into the decoding structure to solve the problem of error chain propagation caused by the character prediction error of LAS starting position. Finally, the multi person conversation scene and TIMIT speech are synthesized and tested. The results show that DCFCN algorithm effectively solves the over fitting problem, and the recognition accuracy is improved by 20% compared with the traditional LAS algorithm.
Design of Handheld PTZ Stabilizer Based on Arduino

The handheld stabilized PTZ can eliminate the change of PTZ’s attitude caused by the user’s movement relative to the external environmental parameters. The PTZ stabilizer can enable the object on the carrier platform to achieve attitude stability control, which can automatically keep the camera, mobile phone and other devices stable and balanced during the shooting process, so that the captured images are clearer and the picture quality is better. In this paper, a low-cost, multi-functional, low energy consumption three-degree-of-freedom handheld PTZ stabilizer is designed to solve the problems such as picture shaking of small handheld devices when in motion. The IMU component of the MPU 6050 is used as the angle detection component of the handheld PTZ stabilizer. The Arduino Nano MG996R is used as the microcontroller to process the collected data into angle data, and the PWM signals are outputted by the pins to control the steering gears. The Blender software is used to model and design the 3D model of the PTZ shell, and the Arduino is used to integrate the PTZ control software and hardware, and finally the overall design is optimized. The experiment shows that the design meets the requirement that the carrier coordinate system is stable relative to the geographical coordinate system. Although there is a certain angle error, the overall operation is in good condition. At the same time, it can also suppress the output of a certain axial direction to achieve the function of a two-degree-of-freedom handheld PTZ stabilizer.
Virtualization Solution for Fast Storage Based on RoCE+P2P Technology in Heterogeneous Environment

With the emergence of a large number of applications based on high-performance computing, such as VR/AR, intelligent voice interaction, automatic driving, and intelligent medical treatment, these applications have brought explosive growth of data and great challenges to the processing capacity of data centers and computer-rooms. The paper proposed a fast storage access scheme across nodes based on RoCE+P2P that is not only compatible with the center computer-room, but also effectively solves the problem that the edge computer-room needs local expansion at the resource and environment constraints. The results show that compared with read/write of the local NVMe SSDs, the performance of RoCE+P2P based storage access across servers and RoCE+P2P based local storage capacity expansion proposed in this paper is equivalent to that of local storage, which illustrates the effectiveness and high performance of the proposed solution.
Risk status assessment of transmission lines based on big data platform

The health status of transmission lines directly affects the stability of the system, so how to accurately assess the risks in line operation is very important. Based on the proportional hazard model, the paper constructs a risk probability model of overhead lines by comprehensively considering the climate and the health probability of individual equipment; builds a big data platform, and uses the numerical fitting method to solve the parameters of the above probability model; finally, uses the above model to carry out risk assessment on transmission lines. The simulation results show that the construction of the big data platform has improved the computing speed, and the obtained model parameters are more accurate; the use of the comprehensive probability model can accurately evaluate the status of the transmission line, which has guiding significance for the operation and maintenance personnel to maintain the equipment.
A Contrast-Enhanced Graph Neural Network Recommendation Algorithm

The recommendation algorithm based on graph neural network focuses on encoding the embedded representation of users and items through interactive data, ignoring the interference of uninteracted data, resulting in inaccurate recommendations. To solve the above problems, firstly, the graph convolution encoder is used to generate the vector representations of the users and the items. Secondly, contrastive learning is carried out in each training batch, so that the user vector is close to the interactive item and far away from the non-interactive item in the representation space, and the distribution of the user vector tends to be scattered to alleviate the mutual interference between users. In order to verify the effectiveness of the algorithm, experiments were carried out on the datasets Yelp2018 and Amazon-Book, and the recall rate was increased by 6.07% and 3.35% compared with the advanced model, respectively.
What lies behind a Facebook page? Insights from an action research project in rural Bangladesh

This investigation explores ambiguities, limitations, constraints, and ethical issues related to the collection and use of online social data for research purposes from a Facebook community fostered by developmental bodies in rural Bangladesh. Even though there have been prior studies of information and communication technology use in vulnerable communities in development contexts, such research has not taken sufficient account of cultural and power dynamics at the coalface of social research. This investigation also examines the positionality of the researchers, including relationships to patriarchal and communal structures in Bangladesh. In this context, it examines the Facebook posts by women of a village. In particular, it finds that the desire to be seen as a “good woman” acts to regulate personal agency and expression of the village women.
Kubernetes-based Scripted Remote Sensing Process Service Platform

With the formation of the high-resolution satellite system, the information carried in remote sensing data is becoming more and more abundant, and the types of data are also increasing. In order to realize the efficient processing of remote sensing data, the construction of remote sensing process is very important. At the same time, higher requirements are put forward for the remote sensing system in terms of dynamic resource scheduling, easy maintenance, elastic expansion, controllable complexity, and flexible deployment. In response to the above problems, this paper first designs a scripted process construction technology, which realizes the efficient construction of various complex processes. And introduce Docker container technology and Kubernetes container cluster management technology, split the remote sensing system into different micro-service modules, run independently in mutually isolated containers, so that the system has the characteristics of flexible load scheduling and elastic scaling.
Blockchain Technology based Metaverse Development Application

The distributed services of the blockchain derive many applications. Combining the latest VR/AR related virtual reality technology, the application scenarios are linked to the decentralized servers, which greatly increase the accessibility of Metaverse. By using the blockchain browser to access the dapp, the accessibility of the content is further improved. More virtual worlds can be developed in combination with the actual application scenarios. Users can closely participate in the re development of the scenarios to achieve user centered essential services. In the application of the meta universe, smart contracts enable the economic development of virtual scenes with tokens. As the underlying network structure of the meta universe, the blockchain network is landing at an unprecedented speed. It is developing the meta universe in the network structure logic related to digital wallets, distributed autonomous organizations (DAOs), distributed storage, decentralized public transactions, and heterogeneous authentication (NFT). It has multiple advantages. First, it realizes participation in predictable scenarios and reduces labor consumption. Sec ond, the interaction process realized the alternation of reality and virtual, and increased diversified creation. Third, use device terminals and mobile terminals to access applications, accelerate point-to-point interaction, and achieve decentralized services.
Default Risk Assessment of Internet Financial Enterprises Based on Graph Neural Network

In recent years, the increasing number of default events happened in the Internet financial enterprises has incurred great financial losses to investors. Early warning to the enterprises with high default risk is of great significance to protecting the benefit of investors. There exist two challenges in traditional default risk assessment methods: poor data availability and neglect of risks from the affiliated entities. In order to address these problems, we collect the Internet financial enterprises’ nonfinancial data and propose a default risk assessment method for Internet financial enterprises based on heterogeneous graph neural network. Extensive experiments on a real-world dataset show that the proposed method outperforms the baseline models on the task of default risk assessment.
Research on Intelligence Mining of Illegal Underground Internet Production on Anonymous Network : Taking Personal Information Trading as an Example

As law enforcement agencies continue to strengthen their efforts to crack down on illegal underground Internet production trading activities, criminals gradually transfer illegal personal information trading and other illegal products, as well as other related crimes, to anonymous networks. Taking Tor dark network as the target, this paper designs active node detection methods and passive node detection methods to obtain criminal intelligence on the dark network. Through the design of the dark web crawler, a large number of personal information transaction related data were obtained. At the same time, the data were analyzed in time, space, type and theme dimensions, forming effective and targeted information. The intelligence mining method proposed in this paper can effectively collect the data related to the subject, form the multi-dimensional analysis results that can be displayed according to the needs, and provide certain technical support for the law enforcement agencies to fight against the hidden network crime.
Design of Intelligent Power Supply Management System for Seismic Monitoring Station Based on Internet of Things

Seismic equipment stability of power supply for geophysical observatory exist hidden dangers, operational mode is not fully realize the status quo of automation, design a kind of intelligent power monitoring system based on Internet of things, the system can collect real-time mains voltage, current, solar power status, professional equipment and lightning status information, etc., when they tested the lightning earthquake early warning signal automatic power-off protection equipment, through more than a cloud platform and mobile phone APP which can realize the seismic equipment power supply remote monitoring, automatic operations and control, change the operational mode for future geophysical observatory from manual operation and maintenance to automatic operation and maintenance.
VTFL: A Blockchain Based Vehicular Trustworthy Federated Learning Framework

With the rapid development of 5G and Internet of Vehicle (IoV) technology, vehicles require a mass of data-sharing to ensure the traffic safety and improve user’s driving experience. However, the traditional way of sharing the original data leads to inefficient communication and the risk of privacy leakage when data leaves the vehicle’s local-storage. The recent advances of privacy-preserving Federated Learning (FL) bring a solution to these challenges, which allows to train models locally based on user’s data and only transmit the parameters with the raw data. But the lack of identity management in the dynamic topology of vehicles makes the FL’s training process vulnerable to be attacked by malicious vehicles, which spoilages the accuracy of the global model seriously. Therefore, how to ensure the security of the FL process has become a crucial challenge. In our work, we propose a trustworthy FL framework based on blockchain, which relies on consensus mechanism to ensure safe aggregation of models, and uses digital signatures to ensure the traceability of malicious vehicles and prevent the malicious vehicles from doing harm again. Our experiment results show that the proposed scheme effectively improves the precision of global model in the malicious scene and the efficiency of model convergence.
Automatic Construction of EV Industry Chain Knowledge Graph based on Big Data

The electric vehicle industry has seen a surge in demand for new energy sources in recent years, making it an attractive area for investment. However, for non-professional investors, it can be difficult to thoroughly analyze all the listed companies and identify investment opportunities without a clear understanding of the industry’s chain structure. In this study, we propose a new approach to building an industrial chain knowledge graph for the electric vehicle industry using big data and natural language processing techniques. These methods allow for the efficient and low-cost construction of an industrial chain system, making it more accessible to non-professional investors. We address the challenges of filtering relevant data from vast amounts of unstructured text data, dealing with diverse product names, and accurately identifying complex relationships in the industrial chain. By developing an ontology structure design and a multivariate relationship recognition model based on both text structural and semantic features, we are able to efficiently and accurately construct a comprehensive industrial chain system that can be used by non-professional investors to identify investment opportunities and make informed decisions.