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Proceedings of the 31st ACM SIGKDD Conference on knowledge Discovery and data Mining V.1

作者： Yuanchun Wang Jifan Yu Zijun Yao Jing Zhang Yuyang Xie Shangqing Tu Yiyang Fu Youhe Feng Jinkai Zhang Jingyao Zhang Bowen Huang Yuanyao Li Huihui Yuan Lei Hou Juanzi Li Jie Tang School of Information Renmin University of China Beijing China & Key Laboratory of Data Engineering and Knowledge Engineering MOE Beijing China Institute of Education Tsinghua University Beijing China Department of Computer Science and Technology Tsinghua University Beijing China School of Information Renmin University of China Beijing China & Engineering Research Center of Database and Business Intelligence MOE Beijing China School of Information Renmin University of China Beijing China Zhipu AI Beijing China

ISBN: (纸本)9798400712456

Applying large language models (LLMs) to academic API usage shows promise in reducing researchers' efforts to seek academic information. However, current LLM methods for using APIs struggle with the complex API coupling commonly encountered in academic queries. To address this, we introduce SoAy, a solution-based LLM methodology for academic information seeking. SoAy enables LLMs to generate code for invoking APIs, guided by a pre-constructed API calling sequence referred to as a solution. This solution simplifies the model's understanding of complex API relationships, while the generated code enhances reasoning efficiency. LLMs are aligned with this solution-oriented, code-based reasoning method by automatically enumerating valid API coupling sequences and transforming them into queries and executable *** evaluate SoAy, we introduce SoAyBench, an evaluation benchmark accompanied by SoAyEval, built upon a cloned environment of APIs from AMiner. Experimental results demonstrate a 34.58-75.99% performance improvement compared to state-of-the-art LLM API-based baselines. All datasets, codes, tuned models, and deployed online services are publicly accessible at https://***/RUCKBReasoning/SoAy.

关键词： academic information seeking

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Task Delay and Energy Consumption Minimization for Low-altitude MEC via Evolutionary Multi-objective Deep Reinforcement Learning

arXiv

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arXiv 2025年

作者： Sun, Geng Ma, Weilong Li, Jiahui Sun, Zemin Wang, Jiacheng Niyato, Dusit Mao, Shiwen College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore College of Software Jilin University Changchun130012 China Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States

The low-altitude economy (LAE), driven by unmanned aerial vehicles (UAVs) and other aircraft, has revolutionized fields such as transportation, agriculture, and environmental monitoring. In the upcoming six-generation (6G) era, UAV-assisted mobile edge computing (MEC) is particularly crucial in challenging environments such as mountainous or disaster-stricken areas. The computation task offloading problem is one of the key issues in UAV-assisted MEC, primarily addressing the trade-off between minimizing the task delay and the energy consumption of the UAV. In this paper, we consider a UAV-assisted MEC system where the UAV carries the edge servers to facilitate task offloading for ground devices (GDs), and formulate a calculation delay and energy consumption multi-objective optimization problem (CDECMOP) to simultaneously improve the performance and reduce the cost of the system. Then, by modeling the formulated problem as a multi-objective Markov decision process (MOMDP), we propose a multi-objective deep reinforcement learning (DRL) algorithm within an evolutionary framework to dynamically adjust the weights and obtain non-dominated policies. Moreover, to ensure stable convergence and improve performance, we incorporate a target distribution learning (TDL) algorithm. Simulation results demonstrate that the proposed algorithm can better balance multiple optimization objectives and obtain superior non-dominated solutions compared to other methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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Enhanced AI Model for Multimodal Influencer Marketing Analysis and Classification

Enhanced AI Model for Multimodal Influencer Marketing Analys...

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Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： Charu Bisaria K.R. Praneeth Divya Nimma T. Naresh Babu Manidipa Roy M. Karthik Amity University Uttar Pradesh Lucknow Campus India School of Management The Apollo University The Apollo Knowledge City Campus Chittoor Andhra Pradesh India University of Southern Mississippi Data Analyst in UMMC USA KSRM College of Management Studies Kadapa Andhra Pradesh India Department of ECE ABES Engineering College Ghaziabad Uttar Pradesh India Department of AI & DS Ramakrishnan College of Engineering Trichy India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

Social media platforms have become a hub for influencer marketing, where understanding influencer categories and post content is crucial. This study presents an Enhanced Influencer Profiler model that leverages both text and image data for classifying influencers and posts with exceptional accuracy. The study utilizes an enhanced multimodal encoder, combining Efficient Net for image feature extraction with BERT, RoBERTa, and GPT-3 for textual features, and apply a cross-attention mechanism to fuse these features into a unified representation. In order to extend the definitional aspect of the influencer representation, the study considers a multi-level attention framework that can analyses internal and external interactions between influencers as well as their temporal and engagement patterns. In the context of influencer classification, the authors obtain a remarkable accuracy measurement of 99.62 % and this model maintains high scores of F1 in each category to quantify the effectiveness of this classification model. In post classification, the low-level model attains a level of 98.45% this is much higher than the general baseline method like SVC (71.60%) and Random Forest (76.25%). The findings described here demonstrate the effectiveness and flexibility of the model in multimodal classification tasks and outperform traditional models. Thus, the Extended Influencer Profiler, which has been developed using the Python software for data processing and training, shows the feasibility of the method. Therefore, the research findings propose that this model can be applied for influencer marketing and content classification in order to work more accurately and efficiently for marketers and analysts. In general, the concept of the Enhanced Influencer Profiler contributes to the progress of social media analysis practices, especially for dealing with multiple and heterogeneous data. Possible research directions for future work may involve imposing other modalities and evalua

关键词： Training Analytical models Accuracy Social networking (online) Computational modeling Static VAr compensators Feature extraction Software Real-time systems Context modeling

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Ontology-based representation and semantic querying of digital notices to airmen

Ontology-based representation and semantic querying of digit...

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14th Integrated Communications, Navigation and Surveillance Conference, ICNS 2014

作者： Gringinger, Eduard Data and Knowledge Engineering Germany

This article consists of a collection of slides from the author's conference presentation.

ISBN: (纸本)9781479948918

This article consists of a collection of slides from the author's conference presentation.

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A Depth-first Algorithm of Finding All Association Rules Generated by a Frequent Itemset

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Journal of Donghua University(English Edition) 2006年第6期23卷 1-4,9页

作者：武坤姜保庆魏庆 Institute of Data and Knowledge Engineering Henan University Institute of Data and Knowledge Engineering Henan University The Computer Science Department Henan University of Finance and Economics

The classical algorithm of finding association rules generated by a frequent itemset has to generate all non-empty subsets of the frequent itemset as candidate set of consequents. Xiongfei Li aimed at this and proposed an improved algorithm. The algorithm finds all consequents layer by layer, so it is breadth-first. In this paper, we propose a new algorithm Generate Rules by using Set-Enumeration Tree (GRSET) which uses the structure of Set-Enumeration Tree and depth-first method to find all consequents of the association rules one by one and get all association rules correspond to the consequents. Experiments show GRSET algorithm to be practicable and efficient.

关键词： association rule frequent itemset breath-first,depth-first consequent.

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database Research： Achievements and Challenges

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Journal of Computer Science & Technology 2006年第5期21卷 823-837页

作者：王珊杜小勇孟小峰陈红 School of Information Renmin University of China MOE Key Lab of Data Engineering and Knowledge Engineering Beijing 100872 P.R. China

database system is the infrastructure of the modern information system. The R＆D in the database system and its technologies is one of the important research topics in the field. The database R＆D in China took off later but it moves along by giant steps. This report presents the achievements Renmin University of China （RUC） has made in the past 25 years and at the same time addresses some of the research projects we, RUC, are currently working on. The National Natural Science Foundation of China supports and initiates most of our research projects and these successfully conducted projects have produced fruitful results.

关键词： database management system parallel database systems Chinese database systems self-managing databases P2P computing and databases data warehousing OLAP Web data management XML mobile data management semantic web contextual information retrieval

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MRST-- An Efficient Monitoring Technology of Summarization on Stream data

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Journal of Computer Science & Technology 2007年第2期22卷 190-196页

作者：樊小泊解婷婷李翠平陈红 School of Information Renmin University of China Beijing 100872 China Key Laboratory of Data Engineering and Knowledge Engineering MOE Beijing 100872 China

Monitoring on data streams is an efficient method of acquiring the characters of data stream. However the available resources for each data stream are limited, so the problem of how to use the limited resources to process infinite data stream is an open challenging problem. In this paper, we adopt the wavelet and sliding window methods to design a multi-resolution summarization data structure, the Multi-Resolution Summarization Tree （MRST） which can be updated incrementally with the incoming data and can support point queries, range queries, multi-point queries and keep the precision of queries. We use both synthetic data and real-world data to evaluate our algorithm. The results of experiment indicate that the efficiency of query and the adaptability of MRST have exceeded the current algorithm, at the same time the realization of it is simpler than others.

关键词： Haar wavelet sliding window stream data

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Using Depth-Enhanced Spatial Transformation for Student Gaze Target Estimation in Dual-View Classroom Images

Using Depth-Enhanced Spatial Transformation for Student Gaze...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Haonan Miao Peizheng Zhao Yuqi Sun Fang Nan Xiaolong Zhang Yaqiang Wu Feng Tian School of Computer Science and Technology Xi’an Jiaotong University Xi’an China Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an China School of Advanced Technology Xi’an Jiaotong-Liverpool University Suzhou China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University Xi’an China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Dual-view gaze target estimation in classroom environments has not been thoroughly explored. Existing methods lack consideration of depth information, primarily focusing on 2D image information and neglecting the latent 3D spatial context, which could lead to suboptimal transformation and cause the gaze cone to intersect with an incorrect object. This paper introduces a novel dual-view gaze target estimation method tailored for classroom settings, leveraging depth-enhanced spatial transformations. By formulating a depth-enhanced 2D space, our method uses depth-enhanced spatial transformation to accurately project students’ gaze cones to the teacher-oriented image. Additionally, we collected a dataset named DVSGE, specifically for student gaze target estimation in dual-view classroom images. Experimental results demonstrate significant performance improvements of 9.8% in AUC and 19.9% in L2-Distance for our method, surpassing existing methods.

关键词： Three-dimensional displays Estimation Focusing Signal processing Acoustics Speech processing

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An effective scheme for top-k frequent itemset mining under differential privacy conditions

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Science China(Information Sciences) 2020年第5期63卷 200-202页

作者： Wenjuan LIANG Hong CHEN Jing ZHANG Dan ZHAO Cuiping LI Key Lab of Data Engineering and Knowledge Engineering of MOE Renmin University of China College of Computer and Information Engineering Henan University

Dear editor,Frequent itemset mining (FIM) is important in many data mining applications [1], such as web log mining and trend analysis. However, if the data are sensitive (e.g., web browsing history), directly releasing frequent itemsets and their support may breach user privacy. The protection of user privacy while obtaining statistical information is im-

关键词： An effective scheme for top-k frequent itemset mining under differential privacy conditions

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A distribution model with pattern structure in formal concept analysis for meteorological data minging

International Journal of Database Theory and Application

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International Journal of database Theory and Application 2015年第4期8卷 31-40页

作者： Shen, Xiajiong Zhang, Lei Han, Daojun Jia, Peiyan Institute of Data and Knowledge Engineering Henan University Kaifeng Henan China

Using data mining technology to analyze the huge amounts of meteorological data plays an important role in improving the accuracy of weather forecasts. After analyzed the features of meteorological data, a distributed meteorological data mining models using the pattern structure in formal concept analysis is proposed in this paper. Since there exists large numerical, boolean, and geographic concepts in meteorological data, using classic methods of formal concept analysis needs to build single-valued formal context. This paper adopts concept lattice pattern structure to avoid such conversions and the results of rules mining have higher readability and efficiency. This pattern structure of concept lattice is extended to the distributed model to improve data processing capability. © 2015 SERSC.

关键词： Numerical methods

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