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5th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2022

作者： Sun, Jiaze Long, Siyuan Ma, xianyan Tang, Yanmei Xi'an University of Posts and Telecommunications Shaanxi Provincial Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an Key Laboratory of Big Data and Intelligent Computing Xi'an710121 China Xi'an University of Posts and Telecommunications Xi'an710121 China

ISBN: (纸本)9781450396899

Deep neural networks are extremely vulnerable due to the existence of adversarial samples. It is a challenging problem to optimize the robustness of the model to protect deep neural networks from the threat of adversarial samples. To improve the model robustness, an integrated detection model of adversarial samples is designed to detect the existence of adversarial samples, which consists of a multi-classification detector and five single-classification detectors to perform double-layer detection of adversarial samples and intercept the adversarial samples finally sent to the image classification model. The detection experiments were conducted on the CIFAR-10 dataset for the adversarial samples generated by five attack algorithms: FGSM, BIM, DeepFool, JSMA, and C&W, and the detection success rate for all types of adversarial samples reached over 98.96%. After that, secondary attack experiments were conducted on the model, and the detection success rate of the model for the second attack adversarial samples reached over 92.36%. It provides an efficient and robust optimization method for deep neural network models in adversarial environments. © 2022 ACM.

关键词： Deep neural networks

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KAN-Face: Efficient Resource Usage and Precision Lip-Sync in Talking Head Generation

KAN-Face: Efficient Resource Usage and Precision Lip-Sync in...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Feng, Guanwen Jin, Siyu Qian, Zhihao Li, Yunan Miao, Qiguang School of Computer Science and Technology Xidian University Shaanxi Xi'an710071 China Xi'an Key Laboratory of Big Data and Intelligent Vision Xidian University Shaanxi Xi'an710071 China Key Laboratory of Collaborative Intelligence Systems Ministry of Education Xidian University Xi'an710071 China

ISBN: (纸本)9798350368741

Despite significant progress in NeRF-based talking head generation, problems like poor lip synchronization and inefficient resource usage remain. To solve these, we propose KAN-Face, a lightweight framework. In preprocessing, we introduce a Lip-Sync Enhancement Module that uses Wav2Lip to extract high-resolution audio features and map them to an explicit intermediate representation, ensuring precise lip movement alignment with the speaker's identity. These predicted lip-sync features are combined with fundamental audio-extracted lip features and injected into the rendering module to improve synchronization. For rendering, we introduce FastKAN to map spatial points to color values. As a variant of KAN, FastKAN's sensitivity to 3D scenes and efficient structure enable precise, fast color prediction. Our framework reduces resource consumption while enhancing lip-sync accuracy and facial reconstruction, making it ideal for talking head generation tasks in resource-limited settings. Project: https://***/KAN-Face/. © 2025 IEEE.

关键词： Face Animation KAN Network Low Resource Neural Radiance Fields Talking Head Generation

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An Evolutionary Multitasking Algorithm for Efficient Multiobjective Recommendations

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第3期6卷 518-532页

作者： Tian, Ye Ji, Luke Hu, Yiwei Ma, Haiping Wu, Le Zhang, xingyi Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Hefei230601 China Anhui University Institutes of Physical Science and Information Technology Hefei230601 China Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Hefei230029 China

Represented by evolutionary algorithms and swarm intelligence algorithms, nature-inspired metaheuristics have been successfully applied to recommender systems and amply demonstrated effectiveness, in particular, for multiobjective recommendation. Owing to the population-based search paradigm, these algorithms can produce a number of recommendation lists, making diverse tradeoffs between multiple metrics and meeting the requirements of accuracy, novelty, diversity, and other user preferences. However, these algorithms are criticized for the low efficiency of the optimization process, especially when the number of users is large. To address this issue, this article proposes an evolutionary multitasking-based recommendation method, where each task corresponds to a user and all the tasks are optimized simultaneously, thus highly improving the efficiency of recommendation. To enhance the convergence speed, all the users are divided into multiple populations according to the similarity between their preferences, where each population evolves with internal knowledge transfer between users, and all the populations evolve with external knowledge transfer between populations. Experimental results on various datasets verify that the proposed method can better balance between multiple metrics than classical and deep neural network-based recommendation methods and exhibits significantly higher efficiency than evolutionary multiobjective optimization-based recommendation methods. © 2024 IEEE. All rights reserved.

关键词： Multiobjective optimization

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Type 1 Diabetes Blood Glucose Management Based on Fuzzy Logic and Reinforcement Learning 4

Type 1 Diabetes Blood Glucose Management Based on Fuzzy Logi...

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4th International Symposium on Artificial Intelligence and intelligent Manufacturing, AIIM 2024

作者： Liu, Xuan Hu, Feng Su, Zuqiang Yang, Fusong Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and Telecommunications Chongqing China Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and Telecommunications Key Laboratory of Big Data Intelligent Computing Chongqing China

ISBN: (纸本)9798331541729

Developing a personalized insulin regimen for the treatment of Type 1 Diabetes (T1D) is a healthcare task with very high safety requirements. Compared to traditional control algorithms, current reinforcement learning-based methods for blood glucose control can increase the duration of blood glucose within the target range while reducing risks. However, they do not account for the uncertainties of factors affecting blood glucose levels, such as diet, age, weight, etc. These factors do not have a simple linear relationship with blood glucose changes. For example, consuming a certain amount of carbohydrates does not always immediately result in the same degree of blood glucose increase, and this variability is precisely the key to personalized insulin regimens. To address this issue, this work proposes a personalized insulin regimen based on fuzzy logic reasoning and reinforcement learning, namely the Fuzzy Rule-Based TD3-BC model (FRBTD3-BC). This regimen learns from real-world treatment data of T1D patients, uses membership functions to obtain the initial conditions and outcome categories of the data, and then calculates the weight of each rule based on confidence levels. Subsequently, a fuzzy rule base is obtained and embedded into the Actor module of the TD3-BC algorithm in the form of a fuzzy reasoning logic layer. By interacting with the FDA-approved UVA/Padova glucose kinetics simulator, the utility of FRBTD3-BC in blood glucose control was studied in 30 virtual patients. The results show that the FRBTD3-BC model outperforms other comparable methods, with the TIR indicator increasing by 5.53 ± 0.64% compared to the original TD3-BC. © 2024 IEEE.

关键词： Reinforcement learning

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DCEL:classifier fusion model for Android malware detection

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Journal of Systems Engineering and Electronics 2024年第1期35卷 163-177页

作者： XU xiaolong JIANG Shuai ZHAO Jinbo WANG xinheng Jiangsu Key Laboratory of Big Data Security&Intelligent Processing Nanjing University of Posts and TelecommunicationsNanjing 210023China School of Computer Science Nanjing University of Posts and TelecommunicationsNanjing 210023China School of Computing and Engineering University of West LondonLondon W55RFUK

The rapid growth of mobile applications,the popularity of the Android system and its openness have attracted many hackers and even criminals,who are creating lots of Android ***,the current methods of Android malware detection need a lot of time in the feature engineering ***,these models have the defects of low detection rate,high complexity,and poor practicability,*** analyze the Android malware samples,and the distribution of malware and benign software in application programming interface(API)calls,permissions,and other *** classify the software’s threat levels based on the correlation of ***,we propose deep neural networks and convolutional neural networks with ensemble learning(DCEL),a new classifier fusion model for Android malware ***,DCEL preprocesses the malware data to remove redundant data,and converts the one-dimensional data into a two-dimensional gray ***,the ensemble learning approach is used to combine the deep neural network with the convolutional neural network,and the final classification results are obtained by voting on the prediction of each single *** based on the Drebin and Malgenome datasets show that compared with current state-of-art models,the proposed DCEL has a higher detection rate,higher recall rate,and lower computational cost.

关键词： Android malware detection deep learning ensemble learning model fusion

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GLSNet: A Global Guided Local Feature Stepwise Aggregation Network for polyp segmentation

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Biomedical Signal Processing and Control 2024年 87卷

作者： Pan, xiaoying Ma, Chenyang Mu, Yaya Bi, Manrong Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi‘an710121 China Xi'an Key Laboratory of Big Data and Intelligent Computing Xi‘an710121 China School of Computer Science & Technology Xi'an University of Post & Telecommunications Xi‘an710121 China

Accurate polyp segmentation is of great significance for the prevention and diagnosis of early colon cancer. Transformer-based image segmentation models have been proposed for polyp segmentation with good results, however, these methods do not sufficiently consider the negative impact of background noise in different levels of features, resulting in the loss of local details. To alleviate this problem, we propose a GLSNet (A Global Guided Local Feature Stepwise Aggregation Network for polyp segmentation) network, including a spatial feature enhancement (SFE) module, a globally guided local feature enhancement (GLFE) module, and a feature stepwise aggregation (FSA) module. SFE can enhance the spatial feature representation of polyps to better capture the polyp information in the features. GLFE uses high-level features to filter noise in low-level features to capture polyp information hidden in shallow features;FSA fuses the semantic and positional information of polyps across scales to obtain the final segmentation results. Qualitative and quantitative experiments were conducted on 7 benchmark datasets, and the experimental results demonstrated that GLSNet outperforms other existing methods and has stronger generalization performance. In particular, we achieved a mean Dice of 92.9% on the large-scale Kvasir dataset, and mean Dice of 81.3% and 81.6% on CVC-ColonDB and ETIS, respectively, which are significantly higher than those of the competing methods. © 2023

关键词： Benchmarking

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IGED:Towards intelligent DDoS Detection Model Using Improved Generalized Entropy and DNN

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Computers, Materials & Continua 2024年第8期80卷 1851-1866页

作者： Yanhua Liu Yuting Han HuiChen Baokang Zhao xiaofengWang ximeng Liu College of Computer and Data Science Fuzhou UniversityFuzhou350108China Engineering Research Center of Big Data Intelligence Ministry of EducationFuzhou350108China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou UniversityFuzhou350108China College of Computer National University of Defense TechnologyChangsha410073China

As the scale of the networks continually expands,the detection of distributed denial of service(DDoS)attacks has become increasingly *** propose an intelligent detection model named IGED by using improved generalized entropy and deep neural network(DNN).The initial detection is based on improved generalized entropy to filter out as much normal traffic as possible,thereby reducing data *** the fine detection is based on DNN to perform precise DDoS detection on the filtered suspicious traffic,enhancing the neural network’s generalization *** results show that the proposed method can efficiently distinguish normal traffic from DDoS *** with the benchmark methods,our method reaches 99.9%on low-rate DDoS(LDDoS),flooded DDoS and CICDDoS2019 datasets in terms of both accuracy and efficiency in identifying attack flows while reducing the time by 17%,31%and 8%.

关键词： DDoS real-time improved generalized entropy DNN

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Text Matching application for integration between industry and education 14

Text Matching application for integration between industry a...

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14th International Conference on Measuring Technology and Mechatronics Automation, ICMTMA 2022

作者： Luo, Yuxuan Li, xiaoge School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Shaanxi Xi'an710121 China Xi'an Key Laboratory of Big Data and Intelligent Computing Shaanxi Xi'an710121 China

ISBN: (纸本)9781665499781

A new talent training mode of collaboration between industry and education was proposed, which aims to reduce the gaps of talent definition between enterprise demand and college education. We formulates scenario as a text matching task of similarity between courses details and job position information, developed a ERNIE+DPCNN based model for the task. The model has been benchmarked on LCQMC dataset, the results show that it achieves the art-of state performance of Acc 0.8809, Recall 0.8745, and FX score 0.8739, We conducted many experiments about the text similarity calculation between domains of industry and educations. The experiment results show our approach has much potential to optimize the curriculum system while applying in this field. © 2022 IEEE

关键词： Industries Training Mechatronics Automation Collaboration Benchmark testing Task analysis

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EEG-Based Fatigue Detection Using PLI Brain Network and Relief Algorithm

EEG-Based Fatigue Detection Using PLI Brain Network and Reli...

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2022 International Conference on Networking and Network Applications, NaNA 2022

作者： He, Yan Wang, Zhongmin Zhao, Yupeng Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an Key Laboratory of Big Data and Intelligent Computing Shaanxi Xi'an710121 China School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Xi'an710121 China

ISBN: (数字)9781665461313

ISBN: (纸本)9781665461313

EEG-based fatigue driving monitoring has important application value in road traffic safety, and the ultimate goal of the research is the development and use of wearable devices, and too many EEG channels in practical application scenarios is detrimental to device portability, and it will lead to problems such as large amount of data, complex calculation and long processing time, so it is especially important to study how to select the EEG channels highly correlated with fatigue. In this paper, a PLI-Relief-based channel selection algorithm by combining the PLI functional connectivity and the weighting idea of Relief algorithm is proposed, and it is applied to the channel selection of fatigue driving EEG. First, the PLI functional connectivity matrix is constructed for the EEG signals after preprocessing, and the binarized PLI matrix is mapped into a brain functional network, and the prime channels are selected by the degree property of the brain network. Then, the power spectral density features are extracted from the EEG signals of the prime channels, and the weights of each prime channel are obtained using the relief algorithm, then the number and the names of optimal channels are determined according to the recognition accuracy of different channel combinations. The proposed method was validated on the publicly available SEED-VIG dataset, and the data of the seven optimal channels is finally selected and obtains a classification accuracy of 81.25%. The framework proposed in this paper takes into account both the correlation between channels and the characteristics of the channel signals themselves in channel selection, which is a reference value for the development and application of wearable devices. © 2022 IEEE.

关键词： Wearable technology

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Agent Planning with World Knowledge Model 38

Agent Planning with World Knowledge Model

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Qiao, Shuofei Fang, Runnan Zhang, Ningyu Zhu, Yuqi Chen, xiang Deng, Shumin Jiang, Yong xie, Pengjun Huang, Fei Chen, Huajun Zhejiang University China National University of Singapore NUS-NCS Joint Lab Singapore Alibaba Group China Zhejiang Key Laboratory of Big Data Intelligent Computing China

Recent endeavors towards directly using large language models (LLMs) as agent models to execute interactive planning tasks have shown commendable results. Despite their achievements, however, they still struggle with brainless trial-and-error in global planning and generating hallucinatory actions in local planning due to their poor understanding of the "real" physical world. Imitating humans' mental world knowledge model which provides global prior knowledge before the task and maintains local dynamic knowledge during the task, in this paper, we introduce parametric World Knowledge Model (WKM) to facilitate agent planning. Concretely, we steer the agent model to self-synthesize knowledge from both expert and sampled trajectories. Then we develop WKM, providing prior task knowledge to guide the global planning and dynamic state knowledge to assist the local planning. Experimental results on three complex real-world simulated datasets with three state-of-the-art open-source LLMs, Mistral-7B, Gemma-7B, and Llama-3-8B, demonstrate that our method can achieve superior performance compared to various strong baselines. Other interesting findings include: 1) our instance-level task knowledge can generalize better to unseen tasks, 2) weak WKM can guide strong agent model planning, and 3) unified WKM training has promising potential for further development. © 2024 Neural information processing systems foundation. All rights reserved.

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