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Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence最新文献

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Optical Flow-Attention Fusion Model for Deepfake Detection 用于深度伪造检测的光流-注意力融合模型
Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2022-12-23 DOI: 10.1145/3579731.3579810
Z. Jiang, Pengsen Zhao, Zhonglong Zheng
{"title":"Optical Flow-Attention Fusion Model for Deepfake Detection","authors":"Z. Jiang, Pengsen Zhao, Zhonglong Zheng","doi":"10.1145/3579731.3579810","DOIUrl":"https://doi.org/10.1145/3579731.3579810","url":null,"abstract":"With the development of deepfake technology, fake videos are being widely spread on media, which has caused serious social attention. Deepfake detection task has become a hot topic in the field of computer vision. In this paper, we propose a deepfake detection method that combines RGB images under the attention mechanism and optical flow characteristics to enhance the generalization of deepfake detection. In the RGB images module, we focus on the local area most relevant to tampering by erasing the most sensitive area of the attention block. In the optical flow module, the optical flow between frames is extracted and input into the backbone as the basis for classification. We compare our approach with state-of-the-art methods on FF++ and Celeb-DF. Experiment results have shown that our method achieves the same performance on the same dataset as state-of-the-art. In the Cross-dataset, our method outperforms most deepfake detection approaches.","PeriodicalId":146783,"journal":{"name":"Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117354008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Dynamic Graph Representation Based on Temporal and Contextual Contrasting 基于时间和上下文对比的动态图表示
Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2022-12-23 DOI: 10.1145/3579654.3579771
Wen-Yuan Zhu, Ke Ruan, Jin Huang, Jing Xiao, Weihao Yu
{"title":"Dynamic Graph Representation Based on Temporal and Contextual Contrasting","authors":"Wen-Yuan Zhu, Ke Ruan, Jin Huang, Jing Xiao, Weihao Yu","doi":"10.1145/3579654.3579771","DOIUrl":"https://doi.org/10.1145/3579654.3579771","url":null,"abstract":"Dynamic graph representation learning is critical for graph-based downstream tasks such as link prediction, node classification, and graph reconstruction. Many graph-neural-network-based methods have emerged recently, but most are incapable of tracing graph evolution patterns over time. To solve this problem, we propose a continuous-time dynamic graph framework: dynamic graph temporal contextual contrasting (DGTCC) model, which integrates temporal and topology information to capture the latent evolution trend of graph representation. In this model, the node representation is first generated by a self-attention–based temporal encoder, which measures the importance weights of neighbor nodes in temporal sub-graphs and stores them in the contextual memory module. After sampling the node representation from the memory module, the model maximizes the mutual information of the same node that occurred in two nearby temporal views by the contrastive learning mechanism, which helps track the evolutional trend of nodes. In inductive learning settings, the results on four real datasets demonstrate the advantages of the proposed DGTCC model.","PeriodicalId":146783,"journal":{"name":"Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence","volume":"185 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123089343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Design of a continuous and stable feeding system for cut tobacco storage cabinet 烟丝储藏柜连续稳定进料系统的设计
Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2022-12-23 DOI: 10.1145/3579654.3579709
Shaolong Han, Jianlin Zhang, Yalin Wu, Zhao Liu, Songjin Zheng
{"title":"Design of a continuous and stable feeding system for cut tobacco storage cabinet","authors":"Shaolong Han, Jianlin Zhang, Yalin Wu, Zhao Liu, Songjin Zheng","doi":"10.1145/3579654.3579709","DOIUrl":"https://doi.org/10.1145/3579654.3579709","url":null,"abstract":"In order to solve the problems of poor continuity, unstable flow and low automation in the feeding process of traditional cut tobacco storage cabinets, a continuous and stable feeding system based on feed-forward and feed-back control mode is proposed in this paper, the system is composed of quantitative device, cut tobacco stock sensor, volume flow sensor, controller, frequency converter, bottom belt motor, etc. Through the flow disturbance judgment algorithm, the storage of high and low cut tobacco levels in the storage cabinet can be predicted, and effective feed-forward control strategy can be adopted in time to control the frequency conversion of the bottom belt of the storage cabinet, so as to greatly reduce the impact of high and low cut tobacco levels in the storage cabinet on the stability of feed flow; Through the comparative analysis of the detection signals of the cut tobacco stock sensor and the volume flow sensor, the controller timely adopts an effective feed-back control strategy to control the frequency conversion of the bottom belt motor and other belt motors, so that the feeding flow of the storage cabinet tends to be consistent with the set flow value. The feed-forward and feed-back control system works in coordination and complements each other, it effectively improves the stability, continuity and automation level of the cut tobacco supply flow. The production data shows that the average value of CPK(Process capability index) has increased by 14.88, the average value of range has decreased by 96.91%, and the average value of standard deviation has decreased by 97.14%.","PeriodicalId":146783,"journal":{"name":"Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128893154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Deep Knowledge Tracing Integrating Multiple Learning Behaviors 集成多种学习行为的深度知识追踪
Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2022-12-23 DOI: 10.1145/3579654.3579772
Longhai Zhu, Yang Ji
{"title":"Deep Knowledge Tracing Integrating Multiple Learning Behaviors","authors":"Longhai Zhu, Yang Ji","doi":"10.1145/3579654.3579772","DOIUrl":"https://doi.org/10.1145/3579654.3579772","url":null,"abstract":"By analyzing students' external learning behaviors, knowledge tracing quantifies students' latent knowledge state on this learning task, so as to further develop targeted learning and teaching plans and promote personalized learning. Students' learning behaviors in online learning platforms are diverse, such as exercise, exam and tutorial browsing. However, most of the existing knowledge tracing models only consider exercise and do not fully utilize other behaviors that also reflect students' learning process. In order to solve this problem, this paper proposes a deep knowledge tracing with multiple learning behaviors model (DKT-MLB), which combines multiple learning behaviors with knowledge concepts. The effectiveness of the proposed model is verified by experiments in a dataset built in real online learning platforms.","PeriodicalId":146783,"journal":{"name":"Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126743658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Optimization Design of Ammunition Scheduling Scheme for Carrier-based Aircraft based on Improved DPSO Algorithm 基于改进DPSO算法的舰载机弹药调度方案优化设计
Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2022-12-23 DOI: 10.1145/3579654.3579691
Liting Wang, Fuqiang Li, Jing-lian Huang, Xiao-Na Zheng
{"title":"Optimization Design of Ammunition Scheduling Scheme for Carrier-based Aircraft based on Improved DPSO Algorithm","authors":"Liting Wang, Fuqiang Li, Jing-lian Huang, Xiao-Na Zheng","doi":"10.1145/3579654.3579691","DOIUrl":"https://doi.org/10.1145/3579654.3579691","url":null,"abstract":"Abstract: In order to minimize the execution time of ammunition scheduling scheme, an ammunition scheduling model for carrier-based aircraft is established in this paper, and a improved discrete particle swarm optimization (IDPSO) algorithm is designed for multiple links, multiple ammunition points and multiple demand points in the ammunition scheduling process. The IDPSO algorithm overcomes the disadvantage that the basic particle swarm is hard to deal with the discrete problem, by discretizing the particle speed and coordinates. The simulation results show that the IDPSO algorithm has faster convergence speed and global optimization ability. At the same time, the applicability of the scheduling model and the effectiveness of the IDPSO algorithm are verified. CCS CONCEPTS • Computing methodologies • Modeling and simulation • Model development and analysis","PeriodicalId":146783,"journal":{"name":"Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131926096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Applying Embedding Methods to Process Mining 嵌入方法在过程挖掘中的应用
Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2022-12-23 DOI: 10.1145/3579654.3579730
Aleksei Pismerov, M. Pikalov
{"title":"Applying Embedding Methods to Process Mining","authors":"Aleksei Pismerov, M. Pikalov","doi":"10.1145/3579654.3579730","DOIUrl":"https://doi.org/10.1145/3579654.3579730","url":null,"abstract":"The performance of process mining algorithms on a particular event log highly depends on the number of different processes present in the logs. Prior event log clustering can help find out which processes certain events in the logs belong to. Since log clustering is not always a simple task, a preliminary transition from logs to log embeddings can be an important step in solving process mining problems. In this paper, we apply different embedding methods to a dataset of event logs. By transitioning to log embeddings and applying clustering methods we improve the efficiency of process mining. The experiment results suggest that embeddings capturing events order perform better than others.","PeriodicalId":146783,"journal":{"name":"Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133700547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A Tailored Physics-informed Neural Network Method for Solving Singularly Perturbed Differential Equations 求解奇摄动微分方程的定制物理信息神经网络方法
Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2022-12-23 DOI: 10.1145/3579654.3579674
Yiwen Pang, Ye Li, Sheng-Jun Huang
{"title":"A Tailored Physics-informed Neural Network Method for Solving Singularly Perturbed Differential Equations","authors":"Yiwen Pang, Ye Li, Sheng-Jun Huang","doi":"10.1145/3579654.3579674","DOIUrl":"https://doi.org/10.1145/3579654.3579674","url":null,"abstract":"Physics-informed neural networks (PINNs) have recently been demonstrated to be effective for the numerical solution of differential equations, with the advantage of small real labelled data needed. However, the performance of PINN greatly depends on the differential equation. The solution of singularly perturbed differential equations (SPDEs) usually contains a boundary layer, which makes it difficult for PINN to approximate the solution of SPDEs. In this paper, we analyse the reasons for the failure of PINN in solving SPDE and provide a feasible solution by adding prior knowledge of the boundary layer to the neural network. The new method is called the tailored physics-informed neural network (TPINN) since the network is tailored to some particular properties of the problem. Numerical experiments show that our method can effectively improve both the training speed and accuracy of neural networks.","PeriodicalId":146783,"journal":{"name":"Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence","volume":"218 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131405100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Infrared detector fault classification and prediction technology based on sensitive parameter learning 基于敏感参数学习的红外探测器故障分类与预测技术
Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2022-12-23 DOI: 10.1145/3579654.3579665
Linlin Shi, Peiliang Yang, Pengfei Yu, Canxiong Lai, Zhenwei Zhou, Danni Hong
{"title":"Infrared detector fault classification and prediction technology based on sensitive parameter learning","authors":"Linlin Shi, Peiliang Yang, Pengfei Yu, Canxiong Lai, Zhenwei Zhou, Danni Hong","doi":"10.1145/3579654.3579665","DOIUrl":"https://doi.org/10.1145/3579654.3579665","url":null,"abstract":"Infrared detector is an important device with a wide range of applications. Based on the fault sensitive parameter data of infrared detectors, this paper studies the fault classification and fault prediction model of infrared detectors by using machine learning methods such as neural network BPNN and long and short term memory network LSTM. Through the establishment and verification analysis of the fault classification model, it provides a model reference and basis for the multi-type fault diagnosis of infrared detectors. Through the establishment and analysis of the fault prediction model, it provides a modeling method for the lifetime prediction of infrared detectors. The application of infrared detector fault classification and prediction technology can improve the reliability of infrared detector products.","PeriodicalId":146783,"journal":{"name":"Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134126068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Cyber Threat Indicators Association Prediction Based on Weighted Fusion of Semantic and Topological Information 基于语义和拓扑信息加权融合的网络威胁指标关联预测
Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2022-12-23 DOI: 10.1145/3579654.3579690
Yansong Wang, Bo Lang, Nan Xiao, Yikai Chen
{"title":"Cyber Threat Indicators Association Prediction Based on Weighted Fusion of Semantic and Topological Information","authors":"Yansong Wang, Bo Lang, Nan Xiao, Yikai Chen","doi":"10.1145/3579654.3579690","DOIUrl":"https://doi.org/10.1145/3579654.3579690","url":null,"abstract":"Nowadays, Cyber Threat Intelligence (CTI) has become increasingly important for detecting and defending against cyber threats. Researchers often construct CTI heterogeneous graphs to describe threat indicators and their associations. However, most existing link prediction methods of normal heterogeneous graphs show poor performance on CTI graphs, as they mainly focus on the topological features and ignore the attributes of the threat indicators. To address this limitation, this paper proposes Ctiap, a Cyber Threat Indicators Association Prediction model based on weighted fusion of the semantic and topological information. The model firstly aims at the semantic characteristics of threat indicators and the topology of CTI graph. We collected more than 20,000 samples through open web platforms to construct a real-word heterogeneous graph dataset of threat indicators. The experimental results show that the accuracy of our model reaches 93.08%, which is better than the state-of-the-art baseline methods.","PeriodicalId":146783,"journal":{"name":"Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115580752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A Method for the Reliability Prediction of Wind Power System based on Generation-Load Balance 一种基于发电负荷平衡的风电系统可靠性预测方法
Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2022-12-23 DOI: 10.1145/3579654.3579745
Xinlei Cai, Jinzhou Zhu, Xu Lin, Yanlin Cui, Xinglang Xie
{"title":"A Method for the Reliability Prediction of Wind Power System based on Generation-Load Balance","authors":"Xinlei Cai, Jinzhou Zhu, Xu Lin, Yanlin Cui, Xinglang Xie","doi":"10.1145/3579654.3579745","DOIUrl":"https://doi.org/10.1145/3579654.3579745","url":null,"abstract":"Wind power is safe and can be developed and utilized sustainability. It is one of the new energy power generation methods to deal with the depletion of fossil energy and the pollution of thermal power and nuclear power. However, the randomness of natural wind is large, which brings challenges to the safe operation of wind power system and the grid connection of wind energy. Reliability prediction is an effective method to improve the safe operation ability of wind power system. Therefore, this paper proposes a prediction and analysis method based on the risk and reliability of wind power system. Through a set of historical wind turbine power generation and power load data, the balance relationship between wind power generation and load is obtained through prediction; From the perspective of balance between power generation and load, the load demand of the next year is predicted. Finally, an example is simulated, and the results are compared with the real-time data of the wind farm. The results of simulation and actual data verify the effectiveness of this method, which shows that the method proposed in this paper can effectively predict the power load demand and the reliability of wind power system.","PeriodicalId":146783,"journal":{"name":"Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124220306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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