Reviewerly/exHarmony huggingface dataset:
Tran-Switch: A transfer learning approach for sentence level cross-genre author profiling on code-switched English–RomanUrdu Text

Cross-genre author profiling aims to build generalized models for predicting profile traits of authors that can be helpful across different text genres for computer forensics, marketing, and other applications. The cross-genre author profiling task becomes challenging when dealing with low-resourced languages due to the lack of availability of standard corpora and methods. The task becomes even more challenging when the data is code-switched, which is informal and unstructured. In previous studies, the problem of cross-genre author profiling has been mainly explored for mono-lingual texts in highly resourced languages (English, Spanish, etc.). However, it has not been thoroughly explored for the code-switched text which is widely used for communication over social media. To fulfill this gap, we propose a transfer learning-based solution for the cross-genre author profiling task on code-switched (English–RomanUrdu) text using three widely known genres, Facebook comments/posts, Tweets, and SMS messages. In this article, firstly, we experimented with the traditional machine learning, deep learning and pre-trained transfer learning models (MBERT, XLMRoBERTa, ULMFiT, and XLNET) for the same-genre and cross-genre gender identification task. We then propose a novel Trans-Switch approach that focuses on the code-switching nature of the text and trains on specialized language models. In addition, we developed three RomanUrdu to English translated corpora to study the impact of translation on author profiling tasks. The results show that the proposed Trans-Switch model outperforms the baseline deep learning and pre-trained transfer learning models for cross-genre author profiling task on code-switched text. Further, the experimentation also shows that the translation of RomanUrdu text does not improve results.
The verbalization of numbers: An explainable framework for tourism online reviews

Online reviews have been found very useful in decision-making. It is important to design and implement accurate systems to analyze the reviews and, based on textual information, predict their ratings. Given the different sources, languages and evaluating systems, intelligent systems are needed to use textual and numerical reviews to better understand the evaluation of the tourist experience and derive useful information to improve the offer. This paper aims to present an eXplainable Artificial Intelligence framework that contributes to the discussion on numerical and textual evaluations of the hospitality experience. It combines sentiment analysis and machine learning to accurately model and explain the evaluation of the tourist experience. The main findings are that review ratings should be used with caution and accompanied by a sentiment evaluation and explainability plays a central role in identifying which are the key concepts of positive or negative ratings, providing invaluable intelligence about the tourist experience.
Privacy-Preserving Searchable Encryption Scheme Based on Deep Structured Semantic Model over Cloud Application

With the continuous development of cloud computing and storage, searchable encryption as a key technology has attracted wide attention. However, existing statistical-based models face long document feature vectors and can not fully catch the document semantics information. This leads to inefficient and unsatisfactory retrieval results. In this paper, we propose a deep structured semantic model of ranked search over encrypted data (DSRSE) based on a convolutional neural network for the first time. In this scheme, document index vectors and query trapdoors are generated by the convolutional neural network model (CNNM) and encrypted by the secure kNN algorithm. In order to protect documents’ privacy security, we use private cloud servers to train the CNNM distributively. All parameters are updated constantly, and the CNNM is updated regularly. Furthermore, a clustering tree based index structure is proposed. Through inner product of document vectors, semantically similar files are clustered together for building a bottom-up index tree which enhances the retrieval efficiency. Analysis and experiments on real datasets illustrate that our schemes perform well in terms of privacy security, search efficiency and accuracy.
Towards Differentially Private Contrastive Learning

With the surge of unlabeled data, increasing efforts have been put into unsupervised learning. As one of the most representative branches of unsupervised learning, contrastive learning has achieved great progress by the high efficiency. Unfortunately, privacy threats on contrastive learning have become sophisticated, making it imperative to develop effective technologies that are able to deal with such threats. To alleviate the privacy issue in contrastive learning, we propose some novel techniques based on differential privacy, which aim at reducing the high sensitivity of gradient in the private training caused by interactive contrastive learning. Specifically, we add differentially private protection to the connection point related to different per-example gradients, which decreases the sensitivity of the gradients significantly. Our experiments on SimCLR demonstrate the superiority of our approach with higher accuracy under the same privacy protection.
A Complete Information Detection Method for Vehicle CAN Network Gateway Based on Neural Network

Aiming at the problem of inaccurate bus anomaly detection due to the poor effect of information feature extraction in the complete information detection of automotive can network gateway, a new neural network-based complete information detection method of automotive can network gateway is proposed. Use high-speed CAN, low-speed CAN, LIN and central gateway to establish a vehicle CAN network architecture, calculate the information entropy of the vehicle CAN network gateway according to the probability of gateway information appearing in a specific time period, and extract the vehicle CAN network by establishing a structural model of the vehicle CAN network database. Introduce neural network, the information characteristic of CAN network gateway is substituted into the neural network, and the complete detection algorithm of vehicle CAN network gateway information is designed to realize the complete detection of vehicle CAN network gateway information. The experimental results show that the shorter the detection time of the detection method, the greater the difference between the information entropy value and the normal value, which is more conducive to the abnormal detection of the CAN bus. The detection method can effectively detect the bus anomalies at the time intervals of 15s, 10s, 5S, 3S, 1s and 0.5s, which is feasible and effective.
A SAR Image Preprocessing Algorithm Based on Improved Homomorphic Wavelet Transform and Retinex

Synthetic Aperture Radar (SAR) imaging technology is gradually applied in the field of security inspection. However, the existence of speckle noise in SAR image will seriously affect its image interpretation and post-processing. To solve this problem, a SAR image preprocessing algorithm based on improved homomorphic wavelet transform (HWT) and Retinex is proposed. Firstly, the HWT is improved. The SAR images obtained after denoising by homomorphic wavelet threshold denoising method and total variation (TV) model denoising method were decomposed by $${\text{coi}}f4$$ wavelet basis, and the low-frequency parts obtained by homomorphic wavelet threshold denoising method and the high-frequency parts obtained by TV model denoising method are recombined; Then, the recombined sub-image is reconstructed by inverse wavelet transform to achieve better denoising effect while retaining the effective boundary information; Finally, the single-scale Retinex (SSR) algorithm is used to enhance the reconstructed SAR image to further improve the image quality. Experimental results show that the proposed algorithm can effectively remove noise while enhancing the edge and detail information of the image, and improve image readability and the accuracy of security check target detection and recognition.
PAMP: A New Atomic Multi-Path Payments Method with Higher Routing Efficiency

The payment channel networks (PCN) technique effectively improves the transaction efficiency of a blockchain system, further promotes its practical application. Atomic Multi-Path Payments (AMP) are usually used, in payment channel networks, to divide transactions to improve routing efficiency, thereby improving the transaction throughput. Improper transaction division, however, may increase the occurrence of routing failure. Therefore, how to perform efficient transaction partitioning is an urgent problem to be solved. In this work, we propose an improved transaction partition method, named Proportional Atomic Multi-Path Payments (PAMP), which can enhance the efficiency of transaction routing. The key insight of PAMP is that, when a transaction is executed, the trade share can be well divided by the remaining capacity in multiple channels, which can greatly improve the routing efficiency and maintain the balance of channel capacity in the network. Simulation results show that, in contrast to traditional routing algorithms, the transaction success rate is increased by 2.3%, and the average execution time is reduced by 75.09 ms. PAMP improves the transaction routing efficiency, and also promotes the balance of network channel capacity.
Research on Intrusion Prevention Optimization Algorithm of Power UAV Network Communication Based on Artificial Intelligence

Aiming at the problem of poor intrusion prevention in power UAV network communication, this paper proposes an optimization algorithm for power UAV network communication intrusion prevention based on artificial intelligence. Detect the abnormal data of power UAV network communication, strengthen the data filtering and management, and carry out adaptive filtering for abnormal areas to achieve effective data preprocessing. Furthermore, it captures communication intrusion data, transmits data filter signal, adjusts detection algorithm, monitors internal signal and avoids bad signal. The optimal intrusion threshold is calculated, and the collected data is adjusted on the basis of artificial intelligence algorithm to obtain the final parameters of the algorithm. Experimental results show that the convergence value of the algorithm is 3 × 105, which has low convergence. Strong anti invasion ability and good practical application prospects. The results show that the algorithm based on artificial intelligence has good performance.
Research on Secure Cloud Storage of Regional Economic Data Network Based on Blockchain Technology

In order to provide secure network storage space for regional economic data and ensure the integrity, correctness and security of regional economic data, a secure cloud storage method for regional economic data network based on blockchain technology is proposed. According to the distribution of regional economic data network nodes, the relevant network model is constructed. Under this model, the regional economic data are collected in real time, and the preprocessing of the collected data is realized through the two steps of data removal and normalization. The blockchain technology is used to determine the data storage structure, encrypt and process the regional economic data, and realize the secure cloud storage of the regional economic data network in combination with the identity authentication mechanism. The experimental results show that compared with the traditional methods, the data loss and the proportion of tampered data are reduced by 7.32 GB and 16.15% respectively, and the storage task execution time of the optimized cloud storage method is higher. It shows that this method can solve the problems existing in the traditional cloud storage of regional economic data network security, maximize the storage security of regional economic data, and make outstanding contributions to the rapid development of the field of data security.
Vehicle CAN Network Intrusion Detection Model Based on Extreme Learning Machine and Feature Analysis

The vehicle can bus is vulnerable to network attack, and the attack mode is complex, and the attack characteristics are relatively hidden. In order to ensure the safety and stability of automobile can network system, the limit learning machine and feature analysis algorithm are used to optimize the intrusion detection model of automobile can network. Simulate various intrusion behaviors of automotive CAN network, and set up intrusion detection standard features. Build the car CAN network structure, collect and process the car CAN network traffic data. The extreme learning machine is used to analyze the network traffic characteristics, and through the matching with the set standard characteristics, the intrusion detection results of the automobile CAN network are obtained. Through the performance test experiment, it is concluded that compared with the traditional intrusion detection model, the error detection rate and missed detection rate of the optimized design model are reduced by 0.86% and 0.52% respectively, and the running delay of the model is effectively reduced.
Analysis Method of Abnormal Traffic of Teaching Network in Higher Vocational Massive Open Online Course Based on Deep Convolutional Neural Network

The running security and stability of massive open online course's teaching network directly affect the implementation process of massive open online course's teaching tasks. In order to provide valuable reference data for the management and maintenance of the network, this paper puts forward an analysis method of abnormal traffic of higher vocational massive open online course's teaching network based on deep convolution neural network. According to the structure of the teaching network of massive open online course in higher vocational colleges, a network model is built. Under this model, the flow data is collected, and the preprocessing of the initial flow data is completed through data cleaning, standardized conversion and clustering. The deep convolution neural network is established, and the characteristics of network traffic data are extracted through back propagation iteration. After discrete detection and feature matching, the abnormal traffic in the teaching network of higher vocational massive open online course is detected, and the visual analysis results are obtained. Compared with the traditional network abnormal traffic analysis method, it is found that the detection error and missed detection rate of the optimized design method are reduced by 3.75MB and 1.02% respectively, the accuracy of traffic abnormal type analysis is increased by 1%, and the analysis speed is obviously improved.
Dynamic Momentum for Deep Learning with Differential Privacy

Deep learning models are often incompetent to privacy attacks, resulting in the leakage of private data. Recently, Differentially-Private Stochastic Gradient Descent (DP-SGD) has emerged as a prime method for training deep learning models with rigorous privacy guarantee, and has been widely adopted in both academic and industrial research. However, using the DP-SGD optimizer will make the model converge slower and worse, so improving the utility of the model while maintaining privacy becomes a challenge. In non-private training, setting momentum to the SGD optimizer is a common method to improve the utility of the model, but the performance of this method in DP-SGD optimizer is not yet known. In this paper, we empirically study the impact of momentum setting on the optimization of DP-SGD models. With extensive experiments, we were able to gain some fresh insights and proposed a method to dynamically set the momentum for DP-SGD to achieve better utility. The results showd that we achieved the new state-of-the-art on MNIST, Fashion-MNIST, CIFAR-10 and Imagenette datasets without any modification of differential-privacy analysis.
Spatio-Temporal Context Modeling for Road Obstacle Detection

Road obstacle detection is an important problem for vehicle driving safety. In this paper, we aim to obtain robust road obstacle detection based on spatio-temporal context modeling. Firstly, a data-driven spatial context model of the driving scene is constructed with the layouts of the training data. Then, obstacles in the input image are detected via the state-of-the-art object detection algorithms, and the results are combined with the generated scene layout. In addition, to further improve the performance and robustness, temporal information in the image sequence is taken into consideration, and the optical flow is obtained in the vicinity of the detected objects to track the obstacles across neighboring frames. Qualitative and quantitative experiments were conducted on the Small Obstacle Detection (SOD) dataset and the Lost and Found dataset. The results indicate that our method with spatio-temporal context modeling is superior to existing methods for road obstacle detection.
F2DLNet: A Face Forgery Detection and Localization Network Based on SSIM Error Maps

Aiming to counteract the negligence of structural distortion of face swapped videos in the existing face forgery methods, a novel face forgery detection and localization network named as F2DLNet is proposed based on structural similarity (SSIM) error maps. It consists of Adjacent Layer Aggregation Module (ALAM), Dilated Convolution Module (DCM), and Gradient-enhanced Block (GEB). The ALAM is proposed to extract features from multiple levels and estimate SSIM error maps through attention residual learning. The DCM is introduced to deal with multi-scale manipulation regions. Finally, the Sobel stream which consists of three GEBs is capable to enhance forgery artifact and manipulation region contours. Experimental results and analysis on large-scale benchmark datasets show that F2DLNet can effectively perceive artifacts and their strength of Deepfake videos and thoroughly predict manipulation regions. It outperforms the existing methods in face forgery detection and localization accuracy. It has great potential to be applied for forgery detection and localization of Deepfake videos.
Extracting Random Secret Key Scheme for One-Time Pad Under Intelligent Connected Vehicle

With the rapid development of vehicle intelligence, the in-vehicle network is no longer a traditional closed network. External devices can be connected through Bluetooth, WiFi or OBD interfaces, so that attackers can remotely attack vehicles through these channels. Hence we create one-time pads to protect the in-vehicle network. Intelligent connected vehicle (ICV) is an information physical system, thus finding a suitable entropy source from its physical properties to extract true random numbers as a one-time pad can well ensure the security of ICV. During the driving process of ICV, the driving decision will change in real time, and these changes will directly act on the generator of the vehicle’s power system, causing the voltage to change in real time. Therefore, we observe that the on-board power voltage of ICV is a very useful source of entropy. We propose a scheme to extract random numbers from the voltage entropy source. First, we filter the weak periodicity in the voltage signal using wavelet variations. After obtaining the non-periodic voltage signal, we fuse the high voltage time interval with it as a second entropy source to improve the extraction efficiency of the random numbers. Secondly, we build Markov chains by analysing the partial autocorrelation coefficient of the quantized bits of one trace. Finally, we extract perfect random numbers from the Markov chain by using cascaded XOR and hash function. Extensive realistic experiments are conducted to validate our scheme.
Research on Potential Threat Identification Algorithm for Electric UAV Network Communication

Potential threat identification of network communication can improve the reliability of power grid, and an algorithm for potential threat identification of power UAV (Urban Assault Vehicle) network communication is designed. In order to identify the potential threat of power UAV network communication, we should collect the data of power UAV network communication, establish the evaluation index of potential threat, decompose the data, establish attack graph and identify malicious node. Experimental results show that the proposed algorithm can improve the recognition accuracy, and the recognition rate of errors and omissions is low, which meets the design requirements.
A Medical Image Segmentation Method Based on Residual Network and Channel Attention Mechanism

In the past years, semantic segmentation method based on deep learning, especially Unet, have achieved tremendous success in medical image processing field. However, due to the limitation of traditional convolution operations, Unet cannot realize global semantic information interaction. To address this problem, this paper proposes a deep learning model based on Unet. The proposed model takes the Residual network as the image feature extraction layer to alleviate the problem of gradient degradation and obtain more effective features. Besides, we add Squeeze-and-Excitation block to the encoder layer, which helps the whole network get the importance of each feature channel, and then improve the useful features and suppress the less useful features according to the importance, so as to improve the segmentation accuracy. According to the experiments on two medical image datasets, our method achieved better segmentation performance than other deep learning-based algorithms, which verified the effectiveness and efficiency of our method.
Security Evaluation Method of Distance Education Network Nodes Based on Machine Learning

In the distance education network, the node security problem is often not guaranteed. Therefore, a machine learning based distance education network node security evaluation method is designed to ensure the node security problem through the node security evaluation, and extract the attack characteristic attributes of the distance education network from the traffic and log information. According to this characteristic attribute, a network wide attack model of distance education network nodes is constructed based on hierarchical expansion. This paper uses AHP method to build a distance education network node security evaluation model based on machine learning, and tests the performance of this method. The test results show that this method has high accuracy and low error rate of network node security evaluation, which proves the evaluation performance of this method.
Machine Learning Based Security Situation Awareness Method for Network Data Transmission Process

With the popularization and development of computer technology, network has become an essential element in people's daily production and life. Due to the openness of the network and the large number of users, the network data transmission process is vulnerable to attacks, resulting in data loss, tampering and other phenomena. Therefore, this research designs a security situation awareness method for network data transmission process based on machine learning. Firstly, a three-level model of security situation awareness is built, based on which security situation data are collected. Then the sparse self encoder is used to reduce the dimension of security situation data and extract its features. Based on this, the security situation is evaluated based on machine learning support vector machine, and the hierarchical genetic algorithm is used to predict the security situation of data transmission process. The experimental results show that the delay and error of security situational awareness are less than the given maximum limit, and the security situational awareness results are consistent with the actual results, which fully proves that the application effect of this method is good.