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4th International Conference on Information Science and Education, ICISE-IE 2023

作者： Chen, Hanning Zeng, Xiangrui Jia, Kun Hu, Yabao Wang, Guopeng School of Computer Science and Technology Tiangong University Tianjin China Engineering Research Center of Integration and Application of Digital Learning Technology Ministry of Education Beijing China School of Artificial Intelligence Tiangong University Tianjin China Jingwei Textile Machinery Co. Ltd Beijing China College of Artificial Intelligence University of Science and Technology Tianjin China The Open University of China China

ISBN: (纸本)9798350394610

The indicator-based multi-objective optimization evolutionary algorithm has gained significant attention for its strong performance across various optimization problems. We draw on the advantages and try to make improvements in the environmental selection process based on different selection criteria. From this, we developed a distinctive multi-objective evolutionary algorithm that based on indicator and performing local searches, namely INLS. The INLS algorithm includes a novel selection mechanism that does not rely on weight vectors, which employs a strategy choosing solutions with relatively poor convergence but good diversity to perturb the decision variables of the selected solutions, aiming to explore and discover solutions that demonstrate enhanced convergence and diversity characteristics. The experimental results obtained from conducting tests on the ZDT and DTLZ test suites provide clear evidence of the remarkable performance exhibited by the proposed INLS in comparison to various other well-known moeAs. These results serve to underscore the competitiveness of the proposed method. © 2023 IEEE.

关键词： Evolutionary algorithms

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research on Collaborative Filtering Recommendation Algorithm Based on Improved User Portraits

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印刷与数字媒体技术研究 2024年第6期 117-123,134页

作者： HOU Meng WANG Guo-peng SONG Li-zhe WANG Hao-yue SI Zhan-jun College of Artificial Intelligence Tianjin University of Science and TechnologyTianjin 300457China The Open University of China Beijing 100039China Engineering Research Center of Integration and Application of Digital Learning Technology Ministry of EducationBeijing 100039China

With the arrival of the big data era,the phenomenon of information overload is becoming increasingly *** response to the common issue of sparse user rating matrices in recommendation systems,a collaborative filtering recommendation algorithm was proposed based on improved user profiles in this ***,a profile labeling system was constructed based on user *** study proposed that user profile labels should be created using basic user information and basic item information,in order to construct multidimensional user ***-IDF algorithm was used to determine the weights of user-item feature ***,user similarity was calculated by weighting both profile-based collaborative filtering and user-based collaborative filtering algorithms,and the final user similarity was obtained by harmonizing these ***,personalized recommendations were generated using Top-N *** with the MovieLens-1M dataset revealed that this algorithm enhances both recommendation Precision and Recall compared to single-method approaches(recommendation algorithm based on user portrait and user-based collaborative filtering algorithm).

关键词： Collaborative filtering User profiling Recommender system Similarity

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Detecting Mixing Services in Bitcoin Transactions Using Embedding Feature and Machine learning 2nd

Detecting Mixing Services in Bitcoin Transactions Using Embe...

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2nd International Conference on Network Simulation and Evaluation, NSE 2023

作者： Ye, Chuhan Li, Qilin Gao, Ruijie Fu, Yi Wang, Peng Bao, Xuanyu Wang, Guopeng Liu, Yuan Tian, Zhihong GuangZhou University GuangZhou510000 China Northeastern University ShenYang110000 China Engineering Research Center of Integration and Application of Digital Learning Technology Ministry of Education Beijing100039 China Pazhou Lab Guangzhou510330 China

ISBN: (纸本)9789819745180

Due to the intrinsic anonymity of the Bitcoin network, it is frequently subject to money laundering activities, many of which involve coin mixing services. Coin mixing serves as a technique to obscure the trajectory of funds and is inherently dynamic and complex. Such dynamism results in inconsistent transaction behavior across numerous addresses. Moreover, the attributes and patterns associated with coin mixing fluctuate over diverse temporal spans, complicating its detection. To address this challenge, our research introduces a novel classification technique. Initially, we classify coin mixing addresses by their unique transaction behaviors, capturing a variety of patterns. We then determine the influence scope of each address category within its respective subnetwork, leading to the development of dynamic embedding features. These features adapt in real-time, reflecting the behavioral patterns of the current category. In our final step, we use a multi-classifier ensemble strategy, integrating both embedding features and topological structure insights, thereby bolstering classification precision and resilience. Our experimental findings highlight significant improvements across diverse evaluation benchmarks. Of special note is the efficacy resulting from merging dynamic embedding features with the multi-classifier ensemble methodology, achieving an impressive precision rate of up to 0.9747. This study provides fresh perspectives and approaches to mitigate the challenges of money laundering within the Bitcoin network. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Embeddings

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A Hybrid Remora Optimization Algorithm with Variable Neighborhood Search for the Flexible Job Shop Scheduling Problem

A Hybrid Remora Optimization Algorithm with Variable Neighbo...

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Advanced Algorithms and Control engineering (ICAACE), International Conference on

作者： Zheng Yang Xiaodan Liang Yajing Li Haoyue Wang School of Computer Science and Technology Tiangong University Tianjin China Engineering Research Center of Integration and Application of Digital Learning Technology Ministry of Education The Open University of China Beijing China College of Artificial Intelligence Tianjin University of Science and Technology Beijing China

ISBN: (数字)9798350361445

ISBN: (纸本)9798350361452

Reducing the production makespan of modern small-batch, multi-variety jobs can be abstracted into a flexible job shop scheduling problem (FJSP). The organization can greatly improve production efficiency by scheduling according to the optimal solution of FJSP. However, due to the high computational complexity of FJSP, it is often difficult to achieve higher performance in actual production. To optimize the makespan of the FJSP, a hybrid remora optimization algorithm with variable neighborhood search (HROA-VNS) is proposed in this work. A hybrid initialization method based on machine load balancing is utilized to improve the quality of the initial population. To solve discrete FJSP, a conversion method for individual positions and scheduling solutions is proposed, and a host switching mechanism (HSM) is designed to enhance the development and exploration capabilities of the algorithm. Furthermore, for enhancing the accuracy and effectiveness of the optimal solution, a variable neighborhood search (VNS) strategy which contains multiple structures is designed for local search. According to the experimental data, compared with the basic algorithm, the HROA-VNS reduced the makespan by an average of 33.02% and 14.40% on the two benchmark test sets respectively, and it shows better performance than related algorithms.

关键词： Job shop scheduling Processor scheduling Heuristic algorithms Sociology Production Switches Search problems

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Chain of Condition: Construct, Verify and Solve Conditions for Conditional Question Answering

arXiv

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arXiv 2024年

作者： Lin, Jiuheng Lai, Yuxuan Feng, Yansong Wangxuan Institute of Computer Technology Peking University China State Key Laboratory of General Artificial Intelligence Peking University China Department of Computer Science The Open University of China China Engineering Research Center of Integration and Application of Digital Learning Technology Ministry of Education China

Conditional question answering (CQA) is an important task that aims to find probable answers and identify missing conditions. Existing approaches struggle with CQA due to two challenges: (1) precisely identifying necessary conditions and the logical relationship, and (2) verifying conditions to detect any that are missing. In this paper, we propose a novel prompting approach, Chain of condition, by first identifying all conditions and constructing their logical relationships explicitly according to the document, then verifying whether these conditions are satisfied, finally solving the logical expression to indicate any missing conditions and generating the answer accordingly. Experiments on two CQA benchmark datasets show our chain of condition outperforms existing prompting baselines, establishing a new state of the art. Furthermore, with only a few examples, our method can facilitate GPT-3.5-Turbo or GPT-4 to outperform all existing supervised models. © 2024, CC BY.

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Teaching Text-to-Image Models to Communicate in Dialog

arXiv

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arXiv 2023年

作者： Sun, Xiaowen Feng, Jiazhan Wang, Yuxuan Lai, Yuxuan Shen, Xingyu Zhao, Dongyan Wangxuan Institute of Computer Technology Peking University China National Key Laboratory of General Artificial Intelligence BIGAI China Engineering Research Center of Integration and Application of Digital Learning Technology Ministry of Education China Department of Computer Science The Open University of China China

A picture is worth a thousand words, thus, it is crucial for conversational agents to understand, perceive, and effectively respond with pictures. However, we find that directly employing conventional image generation techniques is inadequate for conversational agents to produce image responses effectively. In this paper, we focus on the innovative dialog-to-image generation task, where the model synthesizes a high-resolution image aligned with the given dialog context as a response. To tackle this problem, we design a tailored fine-tuning approach on the top of state-of-the-art text-to-image generation models to fully exploit the structural and semantic features in dialog context during image generation. Concretely, we linearize the dialog context with specific indicators to maintain the dialog structure, and employ in-domain data to alleviate the style mismatch between dialog-to-image and conventional image generation tasks. Empirical results on PhotoChat and MMDialog Corpus show that our approach brings consistent and remarkable improvement with 3 state-of-the-art pre-trained text-to-image generation backbones. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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SFedXL: Semi-Synchronous Federated learning With Cross-Sharpness and Layer-Freezing

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IEEE Internet of Things Journal 2025年第12期12卷 18692-18707页

作者： Zhao, Mingxiong Zhao, Shihao Feng, Chenyuan Yang, Howard H. Niyato, Dusit Quek, Tony Q. S. Yunnan University National Pilot School of Software Kunming650500 China Ministry of Education Engineering Research Center of Integration and Application of Digital Learning Technology Beijing100039 China Ministry of Education Engineering Research Center of Cyberspace Kunming650504 China Yunnan University of Finance and Economics Yunnan Key Laboratory of Service Computing Kunming650221 China EURECOM Sophia Antipolis06410 France Zhejiang University University of Illinois Urbana-Champaign Institute Zhejiang University Haining314400 China Nanyang Technological University School of Computer Science and Engineering 639798 Singapore Singapore University of Technology and Design Information Systems Technology and Design Pillar 487372 Singapore

Federated learning (FL) emerges as a potential solution for enabling multiple terminal devices to collaboratively accomplish computational tasks within an Unmanned Aerial Vehicle (UAV) swarm. However, traditional FL approaches, predicated on synchronous data aggregation, are not feasible for a UAV swarm owing to the inherently variable and dynamic nature of their communication networks compared with terrestrial systems. Furthermore, the data procured by UAVs is often highly heterogeneous, attributable to disparities in deployment environments and device attributes. Considering the distinct flight paths and unique operational conditions encountered by different UAVs, a considerable amount of data remains unlabeled. To tackle the challenges associated with asynchronous operations and the prevalence of unlabeled data, we introduce a novel framework termed Semi-synchronous FL with Cross-Sharpness and Layer-Freezing (SFedXL), tailored for a UAV swarm. In particular, we devise a cross-sharpness model training strategy aimed at optimizing the utilization of both labeled and unlabeled datasets. Additionally, we propose an innovative semi-synchronous model aggregation protocol, complemented by client-specific layer-freezing and client cluster scheduling, designed to expedite the training process. Our simulation results indicate that the proposed algorithm surpasses current FL methods in terms of object recognition accuracy and communication efficiency, albeit with a trade-off of increased local computation latency. © 2014 IEEE.

关键词： Federated learning

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A Multi-Objective Evolutionary Algorithm Based on Indicator and Performing Local Searches

A Multi-Objective Evolutionary Algorithm Based on Indicator ...

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Information Science and Education (ICISE-IE), International Conference on

作者： Hanning Chen Xiangrui Zeng Kun Jia Yabao Hu Guopeng Wang School of Computer Science and technology Tiangong University Tianjin China Engineering Research Center of Integration and Application of Digital Learning Technology Ministry of Education Beijing China School of Artificial Intelligence Tiangong University Tianjin China Jingwei Textile Machinery Co. Ltd Beijing China College of Artificial Intelligence University of Science and Technology Tianjin China The Open University of China

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research on Group User Portrait of Online Education Platform Based on Big Data

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印刷与数字媒体技术研究 2024年第6期 124-134页

作者： TONG Wen-jing WANG Guo-peng SONG Li-zhe HU Ya-bao SI Zhan-jun College of Artificial Intelligence Tianjin University of Science and TechnologyTianjin 300457China The Open University of China Beijing 100039China Engineering Research Center of Integration and Application of Digital Learning Technology Ministry of EducationBeijing 100039China

With the rapid development of big data,online education can use big data technology to achieve personalized and intelligent education as well as improve learning effect and user *** this study,the users of The Open University of China online education platform were taken as the research object,their user behavior data was collected,cleaned,and analyzed with text *** RFM model and the improved K-Means algorithm were used to construct the user portrait of the platform group and the needs and preferences of different types of the users were *** word segmentation was used to show the key words of different types of users and the word cloud of their using *** focus of different user groups was determined to facilitate for the follow-up course recommendation and precision *** results showed that the improved K-Means algorithm can well depict the behavior of group *** index of silhouette score was improved to 0.811 by the improved K-Means algorithm,from random uncertainty to a fixed value,which can effectively solve the problem of inconsistent results caused by outlier sample points.

关键词： User portrait Online education platform RFM model Clustering

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research on User Profile Construction Method Based on Improved TF-IDF Algorithm

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印刷与数字媒体技术研究 2024年第6期 110-116页

作者： SHAO Ze-ming LI Yu-ang YANG Ke WANG Guo-peng LIU Xing-guo CHEN Han-ning SI Zhan-jun College of Artificial Intelligence Tianjin University of Science and TechnologyTianjin 300457China The Open University of China Beijing 100039China Engineering Research Center of Integration and Application of Digital Learning Technology Ministry of EducationBeijing 100039China

In the data-driven era of the internet and business environments,constructing accurate user profiles is paramount for personalized user understanding and *** traditional TF-IDF algorithm has some limitations when evaluating the impact of words on classification ***,an improved TF-IDF-K algorithm was introduced in this study,which included an equalization factor,aimed at constructing user profiles by processing and analyzing user search *** the training and prediction capabilities of a Support Vector Machine(SVM),it enabled the prediction of user demographic *** experimental results demonstrated that the TF-IDF-K algorithm has achieved a significant improvement in classification accuracy and reliability.

关键词： TF-IDF-K algorithm User profiling Equalization factor SVM

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