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| 1 | An Abnormal Network Flow Feature Sequence Prediction Approach for DDoS Attacks Detection in Big Data Environment显示文摘Distributed denial-of-service(DDoS)is a rapidly growing problem with the fast development of the Internet.There are multitude DDoS detection approaches,however,three major problems about DDoS attack detection appear in the big data environment.Firstly,to shorten the respond time of the DDoS attack detector;secondly,to reduce the required compute resources;lastly,to achieve a high detection rate with low false alarm rate.In the paper,we propose an abnormal network flow feature sequence prediction approach which could fit to be used as a DDoS attack detector in the big data environment and solve aforementioned problems.We define a network flow abnormal index as PDRA with the percentage of old IP addresses,the increment of the new IP addresses,the ratio of new IP addresses to the old IP addresses and average accessing rate of each new IP address.We design an IP address database using sequential storage model which has a constant time complexity.The autoregressive integrated moving average(ARIMA)trending prediction module will be started if and only if the number of continuous PDRA sequence value,which all exceed an PDRA abnormal threshold(PAT),reaches a certain preset threshold.And then calculate the probability that is the percentage of forecasting PDRA sequence value which exceed the PAT.Finally we identify the DDoS attack based on the abnormal probability of the forecasting PDRA sequence.Both theorem and experiment show that the method we proposed can effectively reduce the compute resources consumption,identify DDoS attack at its initial stage with higher detection rate and lower false alarm rate. | Jieren Cheng Ruomeng Xu Xiangyan Tang Victor S.Sheng Canting Cai | 2018 | Computers, Materials & Continua2018，，4： | 15 |
| 2 | 基于上下文感知和个性化度量嵌入的下一个兴趣点推荐显示文摘随着基于位置的社交网络推荐系统的逐步发展,兴趣点推荐成为了研究热门。兴趣点推荐的研究旨在为用户推荐兴趣点,并且为商家提供广告投放和潜在客户发掘等服务。由于用户签到行为的数据具有高稀疏性,为兴趣点推荐带来很大的挑战。许多研究工作结合地理影响、时间效应、社会相关性等方面的因素来提高兴趣点推荐的性能。然而,在大多数兴趣点推荐的工作中,用户访问的周期性习惯和伴随用户偏好的上下文情境信息没有被深度地挖掘。而且,下一个兴趣点推荐中一直存在着数据的高稀疏度。基于以上考虑,针对用户签到的数据稀疏性问题,将用户周期性行为模式归结为上下文情境信息,提出了一种基于上下文感知的个性化度量嵌入推荐算法,同时将用户签到的上下文情境信息考虑进来,从而丰富有效数据,缓解数据稀疏性问题,提高推荐的准确率,并且进一步优化算法,降低时间复杂度。在两个真实数据集上的实验分析表明,本文提出的算法具有更好的推荐效果。 | 鲜学丰 陈晓杰 赵朋朋 杨元峰 Victor S.Sheng | 2018 | 计算机工程与科学2018，40，4： | 10 |
| 3 | A Method for Improving CNN-Based Image Recognition Using DCGAN显示文摘Image recognition has always been a hot research topic in the scientific community and industry.The emergence of convolutional neural networks(CNN)has made this technology turned into research focus on the field of computer vision,especially in image recognition.But it makes the recognition result largely dependent on the number and quality of training samples.Recently,DCGAN has become a frontier method for generating images,sounds,and videos.In this paper,DCGAN is used to generate sample that is difficult to collect and proposed an efficient design method of generating model.We combine DCGAN with CNN for the second time.Use DCGAN to generate samples and training in image recognition model,which based by CNN.This method can enhance the classification model and effectively improve the accuracy of image recognition.In the experiment,we used the radar profile as dataset for 4 categories and achieved satisfactory classification performance.This paper applies image recognition technology to the meteorological field. | Wei Fang Feihong Zhang Victor S.Sheng Yewen Ding | 2018 | Computers, Materials & Continua2018，，10： | 3 |
| 4 | Multi-label Image Classification via Coarse-to-Fine Attention显示文摘Great efforts have been made by using deep neural networks to recognize multi-label images.Since multi-label image classification is very complicated,many studies seek to use the attention mechanism as a kind of guidance.Conventional attention-based methods always analyzed images directly and aggressively,which is difficult to well understand complicated scenes.We propose a global/local attention method that can recognize a multi-label image from coarse to fine by mimicking how human-beings observe images.Our global/local attention method first concentrates on the whole image,and then focuses on its local specific objects.We also propose a joint max-margin objective function,which enforces that the minimum score of positive labels should be larger than the maximum score of negative labels horizontally and vertically.This function further improve our multi-label image classification method.We evaluate the effectiveness of our method on two popular multi-label image datasets(i.e.,Pascal VOC and MS-COCO).Our experimental results show that our method outperforms state-of-the-art methods. | LYU Fan LI Linyan Victor S.Sheng FU Qiming HU Fuyuan | 2019 | Chinese Journal of Electronics2019，28，6： | 3 |
| 5 | Empirical Comparisons of Deep Learning Networks on Liver Segmentation显示文摘Accurate segmentation of CT images of liver tumors is an important adjunct for the liver diagnosis and treatment of liver diseases.In recent years,due to the great improvement of hard device,many deep learning based methods have been proposed for automatic liver segmentation.Among them,there are the plain neural network headed by FCN and the residual neural network headed by Resnet,both of which have many variations.They have achieved certain achievements in medical image segmentation.In this paper,we firstly select five representative structures,i.e.,FCN,U-Net,Segnet,Resnet and Densenet,to investigate their performance on liver segmentation.Since original Resnet and Densenet could not perform image segmentation directly,we make some adjustments for them to perform live segmentation.Our experimental results show that Densenet performs the best on liver segmentation,followed by Resnet.Both perform much better than Segnet,U-Net,and FCN.Among Segnet,U-Net,and FCN,U-Net performs the best,followed by Segnet.FCN performs the worst. | Yi Shen Victor S.Sheng Lei Wang Jie Duan Xuefeng Xi Dengyong Zhang Ziming Cui | 2020 | Computers, Materials & Continua2020，，3： | 1 |
| 6 | Research of Insect Recognition Based on Improved YOLOv5显示文摘Insects play an important role in the natural ecology,it is of great significance for ecology to research on insects.Nowadays,the invasion of alien species has brought serious troubles and a lot of losses to local life.However,there is still much room for improvement in the accuracy of insect recognition to effectively prevent the invasion of alien species.As the latest target detection algorithm,YOLOv5 has been used in various scene detection tasks,because of its powerful recognition capabilities and extremely high accuracy.As the problem of imbalance of feature maps at different scales will affect the accuracy of recognition,we propose that adding an attention mechanism based on YOLOv5.The channel attention module and the spatial attention module are added to highlight the important information in the feature map and weaken the secondary information,enhancing the recognition ability of the network.Through training on self-made insect data sets,experimental results show that the mAP@0.5 value reaches 92.5%and the F1 score reaches 0.91.Compared with YOLOv5,the map has increased by 1.7%,and the F1 score has increased by 0.02,proving the effectiveness of insect recognition based on improved YOLOv5.In conclusion,we provide effective technical support for insect identification,especially for pest identification. | Zhong Yuan Wei Fang Yongming Zhao Victor S.Sheng | 2021 | Journal on Artificial Intelligence2021，3，4： | 1 |
| 7 | Blockchain Security Threats and Collaborative Defense:A Literature Review显示文摘As a distributed database,the system security of the blockchain is of great significance to prevent tampering,protect privacy,prevent double spending,and improve credibility.Due to the decentralized and trustless nature of blockchain,the security defense of the blockchain system has become one of the most important measures.This paper comprehensively reviews the research progress of blockchain security threats and collaborative defense,and we first introduce the overview,classification,and threat assessment process of blockchain security threats.Then,we investigate the research status of single-node defense technology and multi-node collaborative defense technology and summarize the blockchain security evaluation indicators and evaluation methods.Finally,we discuss the challenges of blockchain security and future research directions,such as parallel detection and federated learning.This paper aims to stimulate further research and discussion on blockchain security,providing more reliable security guarantees for the use and development of blockchain technology to face changing threats and challenges through continuous updating and improvement of defense technologies. | Xiulai Li Jieren Cheng Zhaoxin Shi Jingxin Liu Bin Zhang Xinbing Xu Xiangyan Tang Victor S.Sheng | 2023 | Computers, Materials & Continua2023，76，9： | 1 |
| 8 | GrCol-PPFL:User-Based Group Collaborative Federated Learning Privacy Protection Framework显示文摘With the increasing number of smart devices and the development of machine learning technology,the value of users’personal data is becoming more and more important.Based on the premise of protecting users’personal privacy data,federated learning(FL)uses data stored on edge devices to realize training tasks by contributing training model parameters without revealing the original data.However,since FL can still leak the user’s original data by exchanging gradient information.The existing privacy protection strategy will increase the uplink time due to encryption measures.It is a huge challenge in terms of communication.When there are a large number of devices,the privacy protection cost of the system is higher.Based on these issues,we propose a privacy-preserving scheme of user-based group collaborative federated learning(GrCol-PPFL).Our scheme primarily divides participants into several groups and each group communicates in a chained transmission mechanism.All groups work in parallel at the same time.The server distributes a random parameter with the same dimension as the model parameter for each participant as a mask for the model parameter.We use the public datasets of modified national institute of standards and technology database(MNIST)to test the model accuracy.The experimental results show that GrCol-PPFL not only ensures the accuracy of themodel,but also ensures the security of the user’s original data when users collude with each other.Finally,through numerical experiments,we show that by changing the number of groups,we can find the optimal number of groups that reduces the uplink consumption time. | Jieren Cheng Zhenhao Liu Yiming Shi Ping Luo Victor S.Sheng | 2023 | Computers, Materials & Continua2023，，1： | 1 |
| 9 | A Modified PointNet-Based DDoS Attack Classification and Segmentation in Blockchain显示文摘With the rapid development of blockchain technology,the number of distributed applications continues to increase,so ensuring the security of the network has become particularly important.However,due to its decentralized,decentralized nature,blockchain networks are vulnerable to distributed denial-of-service(DDoS)attacks,which can lead to service stops,causing serious economic losses and social impacts.The research questions in this paper mainly include two aspects:first,the classification of DDoS,which refers to detecting whether blockchain nodes are suffering DDoS attacks,that is,detecting the data of nodes in parallel;The second is the problem of DDoS segmentation,that is,multiple pieces of data that appear at the same time are determined which type of DDoS attack they belong to.In order to solve these problems,this paper proposes a modified PointNet(MPointNet)for the classification and type segmentation of DDoS attacks.A dataset containing multiple DDoS attack types was constructed using the CIC-DDoS2019 dataset,and trained,validated,and tested accordingly.The results show that the proposed DDoS attack classification method has high performance and can be used for the actual blockchain security maintenance process.The accuracy rate of classification tasks reached 99.65%,and the accuracy of type segmentation tasks reached 85.47%.Therefore,the method proposed in this paper has high application value in detecting the classification and segmentation of DDoS attacks. | Jieren Cheng Xiulai Li Xinbing Xu Xiangyan Tang Victor S.Sheng | 2023 | Computer Systems Science & Engineering2023，47，10： | 1 |
| 10 | Forecasting Model Based on Information-Granulated GA-SVR and ARIMA for Producer Price Index显示文摘The accuracy of predicting the Producer Price Index(PPI)plays an indispensable role in government economic work.However,it is difficult to forecast the PPI.In our research,we first propose an unprecedented hybrid model based on fuzzy information granulation that integrates the GA-SVR and ARIMA(Autoregressive Integrated Moving Average Model)models.The fuzzy-information-granulation-based GA-SVR-ARIMA hybrid model is intended to deal with the problem of imprecision in PPI estimation.The proposed model adopts the fuzzy information-granulation algorithm to pre-classification-process monthly training samples of the PPI,and produced three different sequences of fuzzy information granules,whose Support Vector Regression(SVR)machine forecast models were separately established for their Genetic Algorithm(GA)optimization parameters.Finally,the residual errors of the GA-SVR model were rectified through ARIMA modeling,and the PPI estimate was reached.Research shows that the PPI value predicted by this hybrid model is more accurate than that predicted by other models,including ARIMA,GRNN,and GA-SVR,following several comparative experiments.Research also indicates the precision and validation of the PPI prediction of the hybrid model and demonstrates that the model has consistent ability to leverage the forecasting advantage of GA-SVR in non-linear space and of ARIMA in linear space. | Xiangyan Tang Liang Wang Jieren Cheng Jing Chen Victor S.Sheng | 2019 | Computers, Materials & Continua2019，，2： | 1 |
| 11 | An Improved BPNN Prediction Method Based on Multi-Strategy Sparrow Search Algorithm显示文摘Data prediction can improve the science of decision-making by making predictions about what happens in daily life based on natural law trends.Back propagation(BP)neural network is a widely used prediction method.To reduce its probability of falling into local optimum and improve the prediction accuracy,we propose an improved BP neural network prediction method based on a multi-strategy sparrow search algorithm(MSSA).The weights and thresholds of the BP neural network are optimized using the sparrow search algorithm(SSA).Three strategies are designed to improve the SSA to enhance its optimization-seeking ability,leading to the MSSA-BP prediction model.The MSSA algorithm was tested with nine different types of benchmark functions to verify the optimization performance of the algorithm.Two different datasets were selected for comparison experiments on three groups of models.Under the same conditions,the mean absolute error(MAE),root mean square error(RMSE),andmean absolute percentage error(MAPE)of the prediction results of MSSA-BPwere significantly reduced,and the convergence speed was significantly improved.MSSA-BP can effectively improve the prediction accuracy and has certain application value. | Xiangyan Tang Dengfang Feng KeQiu Li Jingxin Liu Jinyang Song Victor S.Sheng | 2023 | Computers, Materials & Continua2023，，2： | 0 |
| 12 | Prediction of Epileptic EEG Signal Based on SECNN-LSTM显示文摘Brain-Computer Interface(BCI)technology is a way for humans to explore the mysteries of the brain and has applications in many areas of real life.People use this technology to capture brain waves and analyze the electroencephalograph(EEG)signal for feature extraction.Take the medical field as an example,epilepsy disease is threatening human health every moment.We propose a convolutional neural network SECNN-LSTM framework based on the attention mechanism can automatically perform feature extraction and analysis on the collected EEG signals of patients to complete the prediction of epilepsy diseases,overcoming the problem that the disease requires long time EEG monitoring and analysis by manual,which is a large workload and relatively subjective,and improving the prediction accuracy of epilepsy diseases by adding the attention mechanism module.Through experimental tests,the algorithm of SECNN-LSTM can effectively predict the EEG signal of epilepsy disease,and the correct recognition rate is improved.The experiment has some reference value for the subsequent research of EEG signals in other fields in deep learning. | Jian Qiang Wang Wei Fang Victor S.Sheng | 2022 | Journal of New Media2022，4，2： | 0 |
| 13 | Air Pollution Prediction Via Graph Attention Network and Gated Recurrent Unit显示文摘PM2.5 concentration prediction is of great significance to environmental protection and human health.Achieving accurate prediction of PM2.5 concentration has become an important research task.However,PM2.5 pollutants can spread in the earth’s atmosphere,causing mutual influence between different cities.To effectively capture the air pollution relationship between cities,this paper proposes a novel spatiotemporal model combining graph attention neural network(GAT)and gated recurrent unit(GRU),named GAT-GRU for PM2.5 concentration prediction.Specifically,GAT is used to learn the spatial dependence of PM2.5 concentration data in different cities,and GRU is to extract the temporal dependence of the long-term data series.The proposed model integrates the learned spatio-temporal dependencies to capture long-term complex spatio-temporal features.Considering that air pollution is related to the meteorological conditions of the city,the knowledge acquired from meteorological data is used in the model to enhance PM2.5 prediction performance.The input of the GAT-GRU model consists of PM2.5 concentration data and meteorological data.In order to verify the effectiveness of the proposed GAT-GRU prediction model,this paper designs experiments on real-world datasets compared with other baselines.Experimental results prove that our model achieves excellent performance in PM2.5 concentration prediction. | Shun Wang Lin Qiao Wei Fang Guodong Jing Victor S.Sheng Yong Zhang | 2022 | Computers, Materials & Continua2022，，10： | 0 |
| 14 | Few-Shot Object Detection Based on the Transformer and High-Resolution Network显示文摘Now object detection based on deep learning tries different strategies.It uses fewer data training networks to achieve the effect of large dataset training.However,the existing methods usually do not achieve the balance between network parameters and training data.It makes the information provided by a small amount of picture data insufficient to optimize model parameters,resulting in unsatisfactory detection results.To improve the accuracy of few shot object detection,this paper proposes a network based on the transformer and high-resolution feature extraction(THR).High-resolution feature extractionmaintains the resolution representation of the image.Channels and spatial attention are used to make the network focus on features that are more useful to the object.In addition,the recently popular transformer is used to fuse the features of the existing object.This compensates for the previous network failure by making full use of existing object features.Experiments on the Pascal VOC and MS-COCO datasets prove that the THR network has achieved better results than previous mainstream few shot object detection. | Dengyong Zhang Huaijian Pu Feng Li Xiangling Ding Victor S.Sheng | 2023 | Computers, Materials & Continua2023，，2： | 0 |
| 15 | Gate-Attention and Dual-End Enhancement Mechanism for Multi-Label Text Classification显示文摘In the realm of Multi-Label Text Classification(MLTC),the dual challenges of extracting rich semantic features from text and discerning inter-label relationships have spurred innovative approaches.Many studies in semantic feature extraction have turned to external knowledge to augment the model’s grasp of textual content,often overlooking intrinsic textual cues such as label statistical features.In contrast,these endogenous insights naturally align with the classification task.In our paper,to complement this focus on intrinsic knowledge,we introduce a novel Gate-Attention mechanism.This mechanism adeptly integrates statistical features from the text itself into the semantic fabric,enhancing the model’s capacity to understand and represent the data.Additionally,to address the intricate task of mining label correlations,we propose a Dual-end enhancement mechanism.This mechanism effectively mitigates the challenges of information loss and erroneous transmission inherent in traditional long short term memory propagation.We conducted an extensive battery of experiments on the AAPD and RCV1-2 datasets.These experiments serve the dual purpose of confirming the efficacy of both the Gate-Attention mechanism and the Dual-end enhancement mechanism.Our final model unequivocally outperforms the baseline model,attesting to its robustness.These findings emphatically underscore the imperativeness of taking into account not just external knowledge but also the inherent intricacies of textual data when crafting potent MLTC models. | Jieren Cheng Xiaolong Chen Wenghang Xu Shuai Hua Zhu Tang Victor S.Sheng | 2023 | Computers, Materials & Continua2023，77，11： | 0 |
| 16 | PoEC: A Cross-Blockchain Consensus Mechanism for Governing Blockchain by Blockchain显示文摘The research on the governing blockchain by blockchain supervision system is an important development trend of blockchain technology.In this system there is a supervisory blockchain managing and governing the supervised blockchain based on blockchain technology,results in a uniquely cross-blockchain demand to consensus mechanism for solving the trust problem between supervisory blockchain and supervised blockchain.To solve this problem,this paper proposes a cross-blockchain consensus mechanism based on smart contract and a set of smart contracts endorse the crossblockchain consensus.New consensus mechanism called Proof-of-EndorseContracts(PoEC)consensus,which firstly transfers the consensus reached in supervisory blockchain to supervised blockchain by supervisory nodes,then packages the supervisory block in supervisory blockchain and transmits it to the smart contract deployed in the supervised blockchain,finally miners in supervised blockchain will execute and package the new block according to the status of the smart contract.The core part of the consensus mechanism is Endorse Contracts which designed and implemented by us and verified the effectiveness through experiments.PoEC consensus mechanism and Endorse Contracts support the supervised blockchain to join the governing blockchain by blockchain system without changing the original consensus mechanism,which has the advantages of low cost,high scalability and being able to crossblockchain.This paper proves that our method can provide a feasible crossblockchain governance scheme for the field of blockchain governance. | Jieren Cheng Yuan Zhang Yuming Yuan Hui Li Xiangyan Tang Victor S.Sheng Guangjing Hu | 2022 | Computers, Materials & Continua2022，，10： | 0 |
| 17 | Research on Rainfall Estimation Based on Improved Kalman Filter Algorithm显示文摘In order to solve the rainfall estimation error caused by various noise factors such as clutter,super refraction,and raindrops during the detection process of Doppler weather radar.This paper proposes to improve the rainfall estimation model of radar combined with rain gauge which calibrated by common Kalman filter.After data preprocessing,the radar data should be classified according to the precipitation intensity.And then,they are respectively substituted into the improved filter for calibration.The state noise variance Q(k)and the measurement noise variance R(k)can be adaptively calculated and updated according to the input observation data during this process.Then the optimal parameter value of each type of precipitation intensity can be obtained.The state noise variance Q(k)and the measurement noise variance R(k)could be assigned optimal values when filtering the remaining data.This rainfall estimation based on semiadaptive Kalman filter calibration not only improves the accuracy of rainfall estimation,but also greatly reduces the amount of calculation.It avoids errors caused by repeated calculations,and improves the efficiency of the rainfall estimation at the same time. | Wen Zhang Wei Fang Xue leiJia Victor S.Sheng | 2022 | Journal of Quantum Computing2022，4，1： | 0 |
| 18 | Texture Feature Extraction from Thyroid MR Imaging Using High-Order Derived Mean CLBP显示文摘In the field of medical imaging,the traditional local binary pattern (LBP)and its improved algorithms are often sensitive to noise.Traditional LBPs are solely based on the signal information from local differences,and the binary quantization method oversimplifies the local texture features while disregarding the imaging information from the concaveconvex regions between the high-order pixels and the neighboring sampling points.Therefore,we propose an improved Derived Mean Complete Local Binary Pattern (DM_CLBP) algorithm based on high-order derivatives.In the DM_CLBP method,the grey value of a single pixel is replaced by the mean grey value of the rectangular area block,and the difference between pixel values in the area is obtained using the second-order differentiation method.Based on the calculation concept of the complete local binary pattern (CLBP)algorithm,the cascade signs and magnitudes of the two components are encoded and recombined in DM_CLBP using a uniform pattern.The results from the experiments showed that the proposed DM_CLBP descriptors achieved a classification accuracy of 94.4%.Compared with LBP and other improved algorithms,the DM_CLBP algorithm presented in this study can effectively differentiate between lesion areas and normal areas in thyroid MR,(magnetic resonance)images and shows the improved accuracy of area classification. | Zhe Liu Cheng-Jian Qiu Yu-Qing Song Xiao-Hong Liu Juan Wang Victor S.Sheng | 2019 | Journal of Computer Science & Technology2019，34，1： | 0 |
| 19 | A Survey on Image Semantic Segmentation Using Deep Learning Techniques显示文摘Image semantic segmentation is an important branch of computer vision of a wide variety of practical applications such as medical image analysis,autonomous driving,virtual or augmented reality,etc.In recent years,due to the remarkable performance of transformer and multilayer perceptron(MLP)in computer vision,which is equivalent to convolutional neural network(CNN),there has been a substantial amount of image semantic segmentation works aimed at developing different types of deep learning architecture.This survey aims to provide a comprehensive overview of deep learning methods in the field of general image semantic segmentation.Firstly,the commonly used image segmentation datasets are listed.Next,extensive pioneering works are deeply studied from multiple perspectives(e.g.,network structures,feature fusion methods,attention mechanisms),and are divided into four categories according to different network architectures:CNN-based architectures,transformer-based architectures,MLP-based architectures,and others.Furthermore,this paper presents some common evaluation metrics and compares the respective advantages and limitations of popular techniques both in terms of architectural design and their experimental value on the most widely used datasets.Finally,possible future research directions and challenges are discussed for the reference of other researchers. | Jieren Cheng Hua Li Dengbo Li Shuai Hua Victor S.Sheng | 2023 | Computers, Materials & Continua2023，，1： | 0 |
| 20 | A New Method of Image Restoration Technology Based on WGAN显示文摘With the development of image restoration technology based on deep learning,more complex problems are being solved,especially in image semantic inpainting based on context.Nowadays,image semantic inpainting techniques are becoming more mature.However,due to the limitations of memory,the instability of training,and the lack of sample diversity,the results of image restoration are still encountering difficult problems,such as repairing the content of glitches which cannot be well integrated with the original image.Therefore,we propose an image inpainting network based on Wasserstein generative adversarial network(WGAN)distance.With the corresponding technology having been adjusted and improved,we attempted to use the Adam algorithm to replace the traditional stochastic gradient descent,and another algorithm to optimize the training used in recent years.We evaluated our algorithm on the ImageNet dataset.We obtained high-quality restoration results,indicating that our algorithm improves the clarity and consistency of the image. | Wei Fang Enming Gu Weinan Yi Weiqing Wang Victor S.Sheng | 2022 | Computer Systems Science & Engineering2022，41，5： | 0 |
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