Reviewerly/exHarmony huggingface dataset:
Channel Shuffle Neural Architecture Search for Key Word Spotting

The evolution of Network Architecture (NA) allowed Key-Word Spotting (KWS) to exhibit high performance. Generally, NA for KWS is required to have low parameter and computation complexity maintaining high classification performance. Most of the attempts so far have been based on manual approaches, and often the architectures developed from such efforts dwell in the balance of the performance and the network complexity. Then, several KWS models based on Neural Architecture Search (NAS) technique have been proposed. However, these methods do not consider the number of parameters and FLOPs for NA in the search process and manually adjusted the complexity of NA by reducing the number of cells. It may not produce optimized NA with a balance between network complexity and performance. To develop effective network architecture for KWS, network complexity and performance must be considered. In this paper, we propose Channel Shuffle Neural Architecture Search (CSNAS) with channel weights. CSNAS selects whether each channel of the input feature is reflected in the computation or not in the search process and simultaneously controls the number of parameters, FLOPs, and performance. Experiment results show that CSNAS can generate NA that satisfies complexity and performance conditions, and NAs generated by CSNAS outperform state-of-the-art KWS methods.
Design and implementation of road surveillance fusion system

Highway Monitoring System is an important part of The Intelligent Highway. And the basic highway monitoring system consists of fisheye camera and gun camera as the data acquisition side. Since the gun camera has a limited viewing angle, the fisheye camera is used as a supplement. To eliminate these blind areas in the monitoring system, we propose a system that merge the fisheye image and the two guns images to obtain a global view under the monitor pole. At the same time, it provides video cutting and video annotation functions to meet the practical needs of users in use. Our experimental results show that the system provides high convenience in terms of performance and functionality.
Variance analysis based distinguisher for template attack

In practice, different styles of side channel attacks can utilize the leakages of a crypto device to recover the used secret key, which can pose a serious threat on the physical security of a crypto device. Among different styles of side channel attacks, template attack can be information theoretically the strongest attack style. However, numerical problems can seriously influence the key-recovery efficiency of template attack in practice, which can make template attack useless in practice. In light of this, the variance analysis based distinguisher is proposed for template attack. Compared with the classical template attack, variance analysis based template attack can reduce the computational complexity of template attack from <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$O(d^{3})$</tex> to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$O(d)$</tex> , where <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$d$</tex> denotes the number of interesting points. Besides, numerical problems do not exist anymore. Therefore, a large number of interesting points can be chosen to enlarge the leakage exploitation and accordingly optimize the key-recovery efficiency of template attack. The key-recovery efficiency of variance analysis based template attack is evaluated in both simulated and real scenarios, and the evaluation results show that compared with the classical template attack, variance analysis based template attack can maintain a high key-recovery efficiency while significantly decrease the number of traces that should be used in the profiling phase of template attack.
Differential evolution algorithm with survival of fitness strategy

In order to overcome the inferiority that the Differential Evolution algorithm is easy to fall into the local optimum, an improved Differential Evolution algorithm is proposed. On the basis of Differential Evolution, an eliminating mechanism of biological competition is introduced; on the basis of differential evolution, an elimination mechanism of biological competition is introduced. The framework consists of two categories: The survival of the fitness strategy and the population grading framework with gray wolves optimizer. The survival of the fitness strategy improves the situation of falling into local optimization. This mechanism eliminates m individuals by comparing the fitness values of individual populations after evolutionary variation and randomly generates individuals with the same number of eliminated individuals. In this paper, using the survival of the fitness strategy makes those individuals who fall into local optimization and are at a lower level in the process of exploration no longer participate in the exploration and withdraw from their life cycle in advance. In the population renewal stage, the individuals that are also eliminated no longer participate. The CEC2017 competition on single objective bound-constrained numerical optimization and four state-of-the-art DE variants are employed to investigate the effectiveness of the proposed algorithm. Experimental results show that Differential Evolution with Survival of Fitness Strategy (SFDE) is competitive in terms of accuracy and convergence.
Vehicle Violation Detection System Based on Improved YOLOv5 Algorithm

This paper proposes a vehicle violation determination system based on improved YOLOv5 algorithm, which performs vehicle violation determination on a single unit at a single intersection, and displays illegal photos and license plates of illegal vehicles on the webpage. Using the network structure of YOLOv5, modifying the vector output of the Head module, and modifying the rectangular frame detection of the target object to quadrilateral detection, the system can identify vehicles and lane lines with more flexibilities.
Two-stage hierarchical clustering based on LSTM autoencoder

Hierarchical clustering algorithm has low accuracy when processing high-dimensional data sets. In order to solve the problem, this paper presents a two-stage hierarchical clustering algorithm based on a Long Short Term Memory (LSTM) autoencoder. Firstly, we use LSTM autoencoder to learn the potential low-dimensional feature representation of the data. Secondly, we use the proposed two-stage hierarchical clustering algorithm to cluster the low-dimensional features. In the first stage, this algorithm divides the data points that are closest to each other into one cluster, for the data points have been clustered, their corresponding reverse nearest neighbors are also divided into the same cluster. In the second stage, the average density is defined as a measure, in each iteration, the cluster with the highest average density is merged with its nearest cluster until the threshold of the number of clusters is reached. Experiments on the UCI dataset show a significant improvement in the accuracy of the proposed algorithm when compared to the PERCH, BIRCH, CURE, SRC and RSRC algorithms.
A novel match point mapping algorithm based on dominant point for large-scale mlcs problem

Finding the Multiple Longest Common Subsequences (MLCS) is a fundamental problem in many fields such as bioinformatics, computational genomics and pattern recognition. Existing algorithms for finding MLCSs from sequences are not suitable for the long and large-scale sequences due to their high time and space consumption. To overcome this problem, a new DAG (directed acyclic graph) model and a Novel Match Point Mapping algorithm (NMPM) based on dominant point are proprosed in this paper. In the DAG, there is no duplicate match point and each match point is mapped to a unique integer identifier. The DAG can be efficiently built by continually calculating successors of each match point. What is more, the high-dimensional match points can be removed if they are no longer used during the construction of DAG. Therefore, a great deal of memory space will be saved. The experiment results reveal that our new algorithm outperforms other leading algorithms, especially for large-scale MLCS problem.
A study of the privacy perspective on principal component analysis via a realistic attack model

Anonymization methods based on principal component analysis (PCA), one of dimensionality reduction methods, have been well studied. These combine PCA with noise addition. Some of these methods theoretically guarantee privacy but decrease utility because of noise addition. Chen et al. proposed a PCA-based privacy-enhancing method adding noise to com-pressed data and assumed an attack model for privacy evaluation where an attacker can obtain the transformation matrix used to transform the original data matrix into the compressed data matrix. However, it is not always possible for an attacker to obtain the transformation matrix in practice. To the best of our knowledge, there is no study about privacy of the regular PCA. In this study, we propose an attack model for PCA that restricts the information that the attacker can obtain, and under this model, we perform an attack on plain PCA, which doesn't add noise. We conducted experiments to see how our model affects privacy. We evaluated privacy using Re-identification Attack, in which an attacker tries to link records in compressed data to corresponding records in original data. We found that the accuracy of the attack was significantly lower than when using the usual model while keeping the utility high. These results suggest that PCA can sufficiently protect privacy when a realistic attacker is assumed in the proposed model.
A bi-level training approach based on extreme learning machine autoencoder for data classification

Single-hidden layer feedforward neural network (SLFN) has attracted much attention because of its simple network structure and excellent performance. Extreme learning machine (ELM) provides a fast training scheme for a SLFN through pseudo-inverse solution of output parameters. However, its performance is very unstable due to its randomly initialized input weights and hidden layer biases. Therefore, more and more work is devoted to improving the performance of the ELM. In this work, a bi-level programming based on extreme learning machine autoencoder (ELM-AE), namely BIELMAE is proposed to optimize the structure and parameters of SLFN hierarchically. In BIELMAE, a binary differential evolution (DE) algorithm is designed as the upper-level optimizer for network structure, while the ELM-AE algorithm as the lower-level optimizer is utilized for optimizing connection parameters of the SLFN. Experimental results show that BIELMAE can obtain a more compact network while maintaining excellent performance in a shorter training time when compared with other advanced method.
CPU tracking algorithm for lightweight vehicles based on deepsort

In order to overcome the problems of low speed of existing vehicle detection and tracking models and large size of vehicle tracking networks, a DeepSort multi-target vehicle tracking algorithm based on lightweight CPU improvement is proposed. The LCNet network is introduced to optimize the backbone network in the original YOLOv5 detection model, simplifying the network model; the convolutional attention mechanism module is introduced to overcome the problem that the traditional tracking network tends to ignore the position information in the spatial direction of the target, improving the tracking accuracy; We used a modified yolov5 network as the DeepSort front-end target detector, predictive target tracking using the Kalman filter algorithm, and cascade matching using the Hungarian algorithm. The experimental results show that, compared with the original YOLOv5- DeepSort, the detection rate of our proposed method is 12.5% of the original detection rate, the tracking rate is 17% of the original tracking rate, and the network volume is reduced by 79.61 MB, which is only 10% of that before the improvement.
Docscanner: document location and enhancement based on image segmentation

Document scanning aims to transfer the captured photographs documents into scanned document files. However, current methods based on traditional or key point detection have the problem of low detection accuracy. In this paper, we were the first to propose a document processing system based on semantic segmentation. Our system uses OCRNet to segment documents. Then, perspective transformation and other post-processing algorithms are used to obtain well-scanned documents based on the segmentation result. Meanwhile, we optimized OCRNet's loss function and reached 97.25 MIoU on the test dataset.
Human motion detection based on Transformer spatiotemporal feature fusion

For the problems of insufficient extraction and fusion of spatio-temporal features in short video sequences and large number of model parameters, this paper proposes a Transformer-based spatio-temporal feature fusion method. First, the method combines the motion information of multiframe images to compensate for the problem of insufficient motion information extraction under single-frame spatial features. Secondly, the attention mechanism in Transformer is used to fully fuse the spatio-temporal information from the mutual anisotropic features, analyze the dependencies between local and global features existing in the image, and improve the recognition accuracy while reducing the number of parameters of the fusion model. Experiments show that the method outperforms the benchmark method on two publicly available datasets, JHMDB-21 and UCFI0l-24, and exhibits some applicability and robustness.
Adaptive asynchronous federated learning for heterogeneous clients

Federated learning is a method of distributed machine learning based on privacy protection, which is characterized by a large number of heterogeneous clients, and often these clients have great differences in communication and computation capabilities, such as servers and smartphones. In this work, we propose a new federated learning algorithm named AAFL, which is based on asynchronous communication and can adaptively assign training models based on the capabilities of different clients to solve the communication efficiency and privacy problems caused by a large number of heterogeneous clients. Our proposed solution can assign training models with different computational complexity according to clients with different capabilities, and reduce the problem that some clients with poor capabilities slow down the training speed of the global model by means of synchronous communication. AAFL can allocate sub-models of different sizes and still aggregate a single global model. The experimental results show that assigning models of different scales according to the capabilities of different clients and using asynchronous communication can improve the training speed and accuracy of the global model to a certain extent, and has a good effect on protecting the privacy of user data.
Image-oriented ciphertext-policy attribute-based encryption system

Ciphertext-Policy Attribute-Based Encryption technology binds ciphertext to attribute sets, which can realize fine-grained access control centered on data users. At present, research on attribute encryption mostly focuses on ordinary data, and there is no research on multimedia such as images. Therefore, an image-oriented Ciphertext-Policy Attribute-Based Encryption (CP-ABE) scheme is proposed. Firstly, the image is processed by wavelet decomposition, and four groups of wavelet coefficient matrices are obtained, which will be scrambling encrypted respectively, and then the encrypted image is obtained through wavelet reconstruction. Secondly, the key used for scrambling encryption is encrypted by Ciphertext-Policy Attribute-Based Encryption respectively. Finally, build a demo system on this basis. The experimental results show that there are differences in the quality of the images that can be accessed depending on different user attributes, which realize the fine-grained control of the users' decryption abilities.
A particle swarm optimization with fitness-distance balance strategy

The particle swarm optimization algorithm is a population-based optimization technique that has been applied in many fields. However, due to the simple learning strategy of the original particle swarm optimization algorithm, there are many drawbacks, such as premature population convergence and insufficient population diversity in the later stage. Many scholars have proposed various particle swarm variants to improve the performance of the algorithm. To alleviate these shortcomings, a particle swarm optimization algorithm with fitness-distance balance (FDB) selection is proposed in this paper. Different from the traditional particle swarm optimization algorithm, the FDB selection strategy is introduced to improve the diversity of particle selection learning instances in particle velocity update, and the learning instances selected according to the FDB strategy replace the social learning instances in the traditional particle swarm optimization; in addition, two crossover operators are introduced to improve the efficiency and diversity of particle search. The comparison with other excellent PSO variants and metaheuristic algorithms in the CEC2017 benchmark test function shows that FDB-PSO achieves significant performance in solving different feature functions and ranks first in the results.
A lightweight YOLOv5 model used for safety helmet and reflective clothing detection

Target detection models are widely used for helmet and reflective clothing detection under intelligent surveillance systems, but the current models are large in size, difficult to deploy in embedded devices with limited computing power and memory, and poor in robustness. To address these problems, a lightweight helmet and reflective clothing wearing detection model is proposed for security inspection robots, named YOLOSE. a dataset with complete categories and close to real construction scenes is constructed, and the generalization ability of the model is improved by various image enhancement methods; on the basis of YOLOv5s algorithm, the backbone network is replaced by ShuffleNet v2, a lightweight network, to reduce the number of parameters. reduce the number of parameters; introduce α-IoU loss function and h-swish activation function to improve the operation speed of the low computing power platform; and improve the size of the initial anchor frame to adapt to specific detection tasks. Experimental results show that YOLOSE has better robustness for different environments and maintains good accuracy with reduced volume compared to YOLOv5s.
Word Pair Relation Classification Model for Nested Named Entity Recognition

Nested named entity recognition is aimed at overlapping entities. The span-based approach considers nested named entity recognition as a two-stage task: span extraction and span classification. However, this method has some problems such as error propagation and long entity recognition. Therefore, this paper put forward a word pair relationship classification model, which describes nested entities as words and multi relationship classification tasks between words. Firstly, the grid matrix representation of word pairs is constructed by traversing the text. Then, in order to enhance the connection between word and word, the distance information and position information of word pairs are fused into the grid matrix representation of word pairs. Finally, through the grid matrix to prediction category relationships of all pairs of words. The experimental results show that the F1-score of the proposed word pair classification model reaches 77.3% on the nested entity recognition dataset GENIA, and its nested entity recognition ability is superior to the existing models.
Network traffic adversarial example generation method based on one pixel attack

Deep learning has been widely used for network traffic classification, but they are vulnerable to well-designed adversarial examples. From the attacker's point of view, a new method is proposed to attack network traffic by combining one-pixel modification of the tabu search algorithm (OPTS). Through OPTS algorithm, one pixel interference is added to the gray image of network traffic, which is difficult to be recognized manually, and makes the LeNet-5 convolutional neural network used by defender output wrong classification results. A series of simulation experiments based on the Moore dataset show that the OPTS method can efficiently generate network adversarial examples with one pixel changed. Compared with the mainstream adversarial attacks, the OPTS algorithm achieves better performance with less modified bytes, which provides a new method for generating adversarial examples.
A Knowledge Semantic Representation Method Combined with Path Information

As an important research direction of knowledge reasoning, the research on knowledge mapping embedding and path information reasoning has made continuous progress. In recent years, many scholars have tried to combine the two algorithms, and the performance of the algorithm has been improved accordingly. However, most of these algorithms only slightly modify the way of calculating the weighted average of other models and path information in the design of scoring function, ignoring the different degrees of influence of different paths on reasoning results. To solve this problem, a new embedding method PTransX is proposed. In the original PTransE algorithm, the graph attention network is introduced to assign different weights to different path resources to distinguish the contribution of different paths to the inference result. In this way, while considering all the relevant paths, some of the critical paths can contribute more to the inference result because of their high reliability. Finally, the experimental results show that the performance of PTransX is improved compared with the original algorithm.
Segmentation Algorithm Based on Multi-Path Refinement Feature Fusion

The visual perception ability of vehicles in the automatic driving systems provides important support for their behavior decision-making, and deep learning technology is becoming more and more mature in the field of computer vision application. Aiming at the problems of complex background, high target similarity, and fuzzy boundary features in traffic scenes, a semantic segmentation algorithm for autonomous driving based on Multi-Scale thinning feature fusion, Swin-CFPN, is proposed. A feature pyramid structure integrating the Swin Transformer module and the attention mechanism of the mobile network is designed. The improvement measures take into account the relationship between pixels and the information of long-distance positions and improve the feature extraction ability of the Multi-Scale semantic information. Lovász Softmax is used to split the loss term of the aspect ratio, which accelerates the convergence ability of the model. The experimental results show that the Swin-CFPN semantic segmentation algorithm achieves 75.8% MIoU on the traffic scene Cityscapes dataset, which is 9.37% and 4.09% higher than the FCN algorithm and UperNet algorithm, respectively. It verifies the validity and effectiveness of the proposed algorithm.
A primary study on diversity of question generation

Question Generation aims to generate fluent and standard questions provided the context and the questions. The previous methods of question generation mainly generated top1 to get the target question. But there are some of the generated questions are less related to the reference questions. So we try to use diversity to improve the performance of question generation. First, we compared BLEU scores and consistency in Human Evaluation between generated 10 questions and generated 1 question; Second, we use a better pre-trained model to adjust the hyper-parameters to generate better questions. The experimental results show that our method outperforms top1 significantly and further demonstrates the advantage of diversity.