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DDCM: a decentralized density clustering and its results gathering approach

NEURAL COMPUTING & APPLICATIONS 2023年第35期35卷 24743-24754页

作者： Zou, Lida Shandong Univ Finance & Econ Sch Comp Sci & Technol Jinan 250014 Peoples R China Key Lab Machine Learning & Financial Data Min Univ Jinan 250014 Peoples R China

The use of distributed clustering is an important method of solving large-scale data mining problems. There are still some problems associated with distributed clustering, such as a performance bottleneck on the master node and network congestion caused by global broadcasting. This paper proposes a decentralized clustering method based on density clustering and the content-addressable network technique. It can form a cluster with excellent scalability and load balancing capabilities based on several surrounding nodes. In addition, a method is presented for optimizing the way clustering results are gathered in different application scenarios. Based on our extensive experiments, the proposed approach performs three times better than benchmark algorithms in terms of efficiency and has a stable expanding ratio of about 0.6 for large-scale data sets.

关键词： Distributed clustering Decentralization Results gathering Density clustering Content addressable network

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An Intelligent Improvement Method Of Classroom Cognitive Efficiency Based On Multidimensional Interactive Devices

JOURNAL OF APPLIED SCIENCE AND ENGINEERING

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JOURNAL OF APPLIED SCIENCE AND ENGINEERING 2023年第3期26卷 445-454页

作者： Zou, Lida Shandong Univ Finance & Econ Sch Comp Sci & Technol Jinan 250014 Peoples R China Key Lab Machine Learning & Financial Data Min Uni Jinan 250014 Peoples R China

With the development and maturity of intelligent interactive devices, introduction of devices into classroom has become inevitable tendency in order to enhance the teaching effect. At present there exist two difficulties when using devices in engineering. It is hard to get cognitive state of students based on multiple monitoring devices. Moreover, how to interact with students using devices intelligently, personally and automatically is challenging in order to improve student's cognition. To solve the above problems, in the paper we design an intelligent management solution about multidimensional interactive devices and propose an active learning algorithm. We aim to improve the judgment and intervention of students' cognition. The extensive experiments demonstrate that the proposed method performs 21% and 43% better than benchmark algorithm, respectively in prediction accuracy and intervention effect for students' cognition.

关键词： Semi-Supervised learning Deep Network Cluster Multidimensional Interactive Classroom Cognitive Efficiency

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Information Security Control Engineering for an Industrial Company with '1C' Software

Information Security Control Engineering for an Industrial C...

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2023 IEEE International Conference on Electrical, Computer and Energy Technologies, ICECET 2023

作者： Zalozhnev, Alexey Yu. Chistov, Dmitry V. V.A. Trapeznikov Institute of Control Sciences Russian Academy of Sciences Active Systems Laboratory Moscow Russia Financial University Department of Data Analysis and Machine Learning Moscow Russia

ISBN: (纸本)9798350327816

The issues related to information security control engineering for an industrial company with '1C' software are considered in this paper. The article discusses the types of threats to the company's information resources, including collective knowledge stored on electronic media. The paper contains standards and other documents governing the development and operation of an industrial company's information security. A brief description of the 1C software platform and 1C ERP is given. To prevent unauthorized access to the IT infrastructure and misappropriation of information important to the client, a set of needed organizational and technical measures with the use of 1C software products is considered. Some practical conclusions are also given. © 2023 IEEE.

关键词： industrial company information security information security control security engineering software software system

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Rapid Discovery Approach of Abnormal Stocks Based on Temporal Convolutional Autoencoder

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AUTOMATIC CONTROL AND COMPUTER SCIENCES 2022年第3期56卷 209-220页

作者： Zou, Lida Shandong Univ Finance & Econ Sch Comp Sci & Technol Jinan 250014 Shandong Peoples R China Key Lab Machine Learning & Financial Data Min Uni Jinan 250014 Shandong Peoples R China

Temporal convolutional autoencoder has an important value of application in time-series analysis. In the paper we aim to use temporal convolutional autoencoder to help find out abnormal stocks quickly in the scenario of financial trading market. However, there are still two critical problems to solve during the application of temporal convolutional autoencoder. First, trading data of each stock are multidimensional time-series data, while classical temporal convolutional autoencoder only applies to one-dimension data. Second, stock trading data in a market are huge and their analysis consumes a long time, which contradicts the demand of quick decision in stock trading. To solve the above problems, we improve temporal convolutional autoencoder based on multidimensional sampling, convolution kernel generated by prior knowledge, temporal feature reuse, parallel training on clouds. All the techniques help temporal convolutional autoencoder find abnormal stocks quickly and well. Extended experiments demonstrate that our proposed temporal convolutional autoencoder could raise F1 score to more than seventy percent. The largest time efficiency of finding abnormal stocks can be increased by ninety percent as well.

关键词： temporal convolutional autoencoder anomaly detection a priori knowledge time-series feature reusing parallel training

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Predicting gastric cancer response to anti-HER2 therapy or anti-HER2 combined immunotherapy based on multimodal data

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Signal Transduction and Targeted Therapy 2024年第9期9卷 4137-4148页

作者： Zifan Chen Yang Chen Yu Sun Lei Tang Li Zhang Yajie Hu Meng He Zhiwei Li Siyuan Cheng Jiajia Yuan Zhenghang Wang Yakun Wang Jie Zhao Jifang Gong Liying Zhao Baoshan Cao Guoxin Li Xiaotian Zhang Bin Dong Lin Shen Center for Data Science Peking UniversityBeijingChina Department of Gastrointestinal Oncology Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education)Peking University Cancer Hospital and InstituteBeijingChina Department of Pathology Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education)Peking University Cancer Hospital and InstituteBeijingChina Department of Radiology Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education)Peking University Cancer Hospital and InstituteBeijingChina National Biomedical Imaging Center Peking UniversityBeijingChina Department of General Surgery Nanfang HospitalSouthern Medical UniversityGuangzhouChina Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor GuangzhouChina Department of Medical Oncology and Radiation Sickness Peking University Third HospitalBeijingChina National Engineering Laboratory for Big Data Analysis and Applications Peking UniversityBeijingChina Beijing International Center for Mathematical Research(BICMR) Peking UniversityBeijingChina Center for Machine Learning Research Peking UniversityBeijingChina

The sole use of single modality data often fails to capture the complex heterogeneity among patients,including the variability in resistance to anti-HER2 therapy and outcomes of combined treatment regimens,for the treatment of HER2-positive gastric cancer(GC).This modality deficit has not been fully considered in many ***,the application of artificial intelligence in predicting the treatment response,particularly in complex diseases such as GC,is still in its ***,this study aimed to use a comprehensive analytic approach to accurately predict treatment responses to anti-HER2 therapy or anti-HER2 combined immunotherapy in patients with HER2-positive *** collected multi-modal data,comprising radiology,pathology,and clinical information from a cohort of 429 patients:310 treated with anti-HER2 therapy and 119 treated with a combination of anti-HER2 and anti-PD-1/PD-L1 inhibitors *** introduced a deep learning model,called the Multi-Modal model(MuMo),that integrates these data to make precise treatment response *** achieved an area under the curve score of 0.821 for anti-HER2 therapy and 0.914 for combined ***,patients classified as low-risk by MuMo exhibited significantly prolonged progression-free survival and overall survival(log-rank test,P<0.05).These findings not only highlight the significance of multi-modal data analysis in enhancing treatment evaluation and personalized medicine for HER2-positive gastric cancer,but also the potential and clinical value of our model.

关键词： immunotherapy HER2 cancer

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GraphMorph: Tubular Structure Extraction by Morphing Predicted Graphs 38

GraphMorph: Tubular Structure Extraction by Morphing Predict...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zhang, Zhao Zhao, Ziwei Wang, Dong Wang, Liwei Center for Data Science Peking University China Yizhun Medical AI Co. Ltd. China State Key Laboratory of General Artificial Intelligence School of Intelligence Science and Technology Peking University China Center for Machine Learning Research Peking University China Guangdong Guangzhou China

Accurately restoring topology is both challenging and crucial in tubular structure extraction tasks, such as blood vessel segmentation and road network extraction. Diverging from traditional approaches based on pixel-level classification, our proposed method, named GraphMorph, focuses on branch-level features of tubular structures to achieve more topologically accurate predictions. GraphMorph comprises two main components: a Graph Decoder and a Morph Module. Utilizing multi-scale features extracted from an image patch by the segmentation network, the Graph Decoder facilitates the learning of branch-level features and generates a graph that accurately represents the tubular structure in this patch. The Morph Module processes two primary inputs: the graph and the centerline probability map, provided by the Graph Decoder and the segmentation network, respectively. Employing a novel SkeletonDijkstra algorithm, the Morph Module produces a centerline mask that aligns with the predicted graph. Furthermore, we observe that employing centerline masks predicted by GraphMorph significantly reduces false positives in the segmentation task, which is achieved by a simple yet effective post-processing strategy. The efficacy of our method in the centerline extraction and segmentation tasks has been substantiated through experimental evaluations across various datasets. Source code will be released soon. © 2024 Neural information processing systems foundation. All rights reserved.

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Development of Modern Forecasting Models 16

Development of Modern Forecasting Models

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16th International Conference Management of Large-Scale System Development, MLSD 2023

作者： Ivanyuk, Vera Shuvalov, Konstantin Akhobadze, Gurami Malekova, Victoria Mikhailov, Alexey Levchenko, Kiril Financial University under the Government of the Russian Federation Department of Data Analysis and Machine Learning Moscow Russia Bauman Moscow State Technical University Department of Higher Mathematics Moscow Russia V.A.Trapeznikov Institute of Control Sciences of Ras Laboratory of Management of Large-Scale System Development Moscow Russia V. A.Trapeznikov Institute of Control Sciences of Ras Laboratory of Network Systems Control Moscow Russia Financial University under the Government of the Russian Federation Department of Financial Markets and Financial Engineering Moscow Russia Financial University under the Government of the Russian Federation Department of Mathematics Moscow Russia

ISBN: (纸本)9798350337907

关键词： Forecasting

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Overview of Modern Forecasting Methods and Models 16

Overview of Modern Forecasting Methods and Models

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16th International Conference Management of Large-Scale System Development, MLSD 2023

作者： Ivanyuk, Vera Tsvirkun, Anatoliy Sunchalina, Anna Goroshnikova, Tatiana Sunchalin, Andrey Zholobova, Galina Financial University under the Government of the Russian Federation Department of Data Analysis and Machine Learning Moscow Russia Bauman Moscow State Technical University Department of Higher Mathematics Moscow Russia V.A.Trapeznikov Institute of Control Sciences of Ras Laboratory of Management of the Development of Large-Scale Systems Moscow Russia Financial University under the Government of the Russian Federation Faculty of International Economic Relations Moscow Russia Financial University under the Government of the Russian Federation Department of Mathematics Moscow Russia

The development of accurate forecasting models for financial time series is an essential area of research in finance and economics. The article describes modern models and forecasting methods. © 2023 IEEE.

ISBN: (纸本)9798350337907

关键词： Neural networks

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Information Security Control Engineering for an Industrial Company with “1C” Software

Information Security Control Engineering for an Industrial C...

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Electrical, Computer and Energy Technologies (ICECET), International Conference on

作者： Alexey Yu. Zalozhnev Dmitry V. Chistov Active Systems Laboratory V.A. Trapeznikov Institute of Control Sciences Russian Academy of Sciences Moscow Russia Department of Data Analysis and Machine Learning Financial University Moscow Russia

The issues related to information security control engineering for an industrial company with “1C” software are considered in this paper. The article discusses the types of threats to the company's information resources, including collective knowledge stored on electronic media. The paper contains standards and other documents governing the development and operation of an industrial company's information security. A brief description of the 1C software platform and 1C ERP is given. To prevent unauthorized access to the IT infrastructure and misappropriation of information important to the client, a set of needed organizational and technical measures with the use of 1C software products is considered. Some practical conclusions are also given.

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Contrastive learning for Adapting Language Model to Sequential Recommendation 24

Contrastive Learning for Adapting Language Model to Sequenti...

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24th IEEE International Conference on data Mining, ICDM 2024

作者： Liang, Fei-Yao Xi, Wu-Dong Xing, Xing-Xing Wan, Wei Wang, Chang-Dong Chen, Min Guizani, Mohsen School of Computer Science and Engineering Sun Yat-sen University Guangzhou China NetEase Games China UX Center NetEase Games Guangzhou China Guangdong Provincial Key Laboratory of Intellectual Property and Big Data Guangzhou China School of Computer Science and Engineering South China University of Technology Guangzhou China Pazhou Laboratory Guangzhou China Machine Learning Department Abu Dhabi United Arab Emirates

ISBN: (纸本)9798331506681

With the explosive growth of information, recommendation systems have emerged to alleviate the problem of information overload. In order to improve the performance of recommendation systems, many existing methods introduce Large Language Models to extract textual information from description text. However, Large Language Models are trained on large-scale generic textual data and may face a semantic gap for downstream recommendation tasks. To address the above issues, we propose Contrastive learning for Adapting Language Model to Sequential Recommendation (CLA-Rec). In CLA-Rec, we first extract text embeddings from description text using Large Language Models and align the text embeddings learned by Large Language Models with the collaborative information through contrastive learning to obtain high-quality item representations. Through semantic alignment, we bridge the semantic gap between Large Language Models and the recommendation task. To map textual information and collaborative information into user representations, we utilize a Transformer model to learn user representations and capture user preferences by combining the semantically aligned item representations. Extensive experiments on three public datasets demonstrate that our method outperforms state-of-the-art approaches on multiple evaluation metrics, illustrating the effectiveness of the CLA-Rec model in adapting Large Language Models to recommendation tasks. © 2024 IEEE.

关键词： Contrastive learning

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