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International Conference on data engineering

作者： Xu Xie Jin Niu Lifang Deng Dan Wang Jiandong Zhang Zhihua Wu Kaigui Bian Gang Cao Bin Cui School of CS & Key Laboratory of High Confidence Software Technologies (MOE) Peking University Alibaba Group China Lazada National Engineering Lab for Big Data Analysis and Applications Beijing Academy of Artificial Intelligence (BAAI) Institute of Computational Social Science Peking University Qingdao China

Click-through rate (CTR) prediction, whose purpose is to predict the probability of a user clicking on an item, plays a pivotal role in recommender systems. Capturing users’ accurate preferences from their historical interactions (e.g., clicks) is an essential step for handling this task and has aroused wide concern in both academia and industry. However, most of the previous methods focus on the users with abundant clicks and ill-serve the users who rarely click or purchase items. Though the ratio of these long-tailed users may be small on popular platforms, such as Amazon and Taobao, they are the majority on the newborn e-commerce company like Lazada. To extract the interests of long-tailed users, several works attempt to integrate the side information, such as demographic features. Nevertheless, these features are usually not available and may even lead to privacy concerns. Therefore, how to utilize the noisy and limited clicks becomes the key *** this paper, we propose a novel model called Hierarchical Interest Modeling (HIM). It hierarchically utilizes long-tailed users’ limited behaviors and captures their preferences from both personalized and group-wise perspectives. HIM consists of two main components, including User Behavior Pyramid (UBP) and User Behavior Clustering (UBC). The UBP module utilizes additional negative feedback to reduce the noises in positive feedback, thus obtaining reliable user personalized representations. Then, the UBC module automatically discovers latent user groups with self-supervised reconstruction loss and learns another interest representation for each user in a group-wise aspect. Extensive experiments on both public and industrial datasets verify the superiority of HIM compared with the state-of-the-art baselines. Moreover, HIM has already been deployed on Lazada recommendation scenario and gains 3.38% on CTR prediction on average on the online A/B test. Our codes are available in https://***/xiaojin-nj/HIM.

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Optimization of Extraction Process of Valeriana officinalis L. Root Essential Oil and Study on Its Anti-Free Radical Activity 2

Optimization of Extraction Process of Valeriana officinalis ...

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2020 2nd International Conference on Advances in Civil engineering, Energy Resources and Environment engineering, ACCESE 2020

作者： Miao, Tianlin Zheng, Hongjuan Wang, Xue Lan, Ying Zhang, Yuehua Song, Yujun Li, Lan School of Life and Science Jiamusi University Jiamusi City Heilongjiang Province154007 China School of Data Science and Software Engineering Wuzhou University Wuzhou City Guangxi Province543000 China

Taking Valerianrhizome as raw material and the yield of volatile oil of Valerian as an index, the extraction process of valerian volatile oil was analyzed by microwave assisted steam distillation, and optimized by Box-Behnken response surface test. The effects of scavenging capacity of Li, DPPH and hydroxyl radicals on their antioxidant capacity were studied. The results showed that the optimal process conditions of microwave extraction of volatile oil of Valerian were 350W microwave power, extraction time 25min, liquid-to-material ratio 8.8mL: 1.0g, and the yield of volatile oil of Valerian extracted under this condition was 5.88%. Microwave-extracted volatile oil of Valerian has strong scavenging ability and high reducing power to DPPH free radicals and hydroxyl free radicals. Compared with steam distillation, it has obvious differences, which can provide reference for the development of antioxidant products of Valerian oil. © Published under licence by IOP Publishing Ltd.

关键词： Essential oils

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Automatic Text Summarization Method Based on Improved Textrank Algorithm and K-Means Clustering

SSRN

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SSRN 2023年

作者： Liu, Wenjun Sun, Yuyan Yu, Bao Wang, Hailan Peng, Qingcheng Hou, Mengshu Guo, Huan Wang, Hai Liu, Cheng Chengdu611731 China School of Computer and Software Engineering XiHua University Chengdu610039 China School of Big Data and Artificial Intelligence Chengdu Technological University Chengdu611730 China Science and Technology on Communication Security Laboratory The 30th Research Institute of China Electronics Technology Group Corporation Chengdu610041 China

Automatic text summarization is to obtain a summary by compressing the text while retaining its important information, and then users can obtain the important content of the text by reading the summary. In the research literatures, the extraction method is widely used and is also one of the main research methods of most abstracts. However, this summary method still has some problems. The selection of the initial cluster center has not been carefully determined, and in articles with complex sentences, the sentence redundancy summarized is high. In order to solve the above problems, this paper proposes an automatic text summarization method based on improved TextRank algorithm and K-Means clustering. This method uses the improved BM25 model to calculate the BM25 similarity between sentences, and then uses the TextRank model to obtain the TR score of sentences. Then, the TR score is used to select the initial center of K-Means clustering. Finally, the summary is obtained through the class score and sentence score. The experimental results show that the proposed BM25Rank algorithm has a better effect on DUC2004 dataset. In conclusion, the improved TextRank and K-Means method proposed in this paper improve the accuracy of automatic text summarization and reduce the redundancy. © 2023, The Authors. All rights reserved.

关键词： K-means clustering

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Effects of Gradient Concentration of Colchicine on Callus Induction and Differentiation ofRose rosea 2

Effects of Gradient Concentration of Colchicine on Callus In...

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2020 2nd International Conference on Advances in Civil engineering, Energy Resources and Environment engineering, ACCESE 2020

作者： Dai, Jianan Zhang, Yaosheng Gu, Ye Wen, Bin Guan, Jinqiu Li, Lan School of Science and Life Jiamusi University Jiamusi City Heilongjiang Province154007 China School of Data Science and Software Engineering Wuzhou University Wuzhou City Guangxi Province543000 China

To induce polypoid plants ofRosa rugosa tunb, the effect of the interaction between the concentration of colchicine and the treatment time on the callus induction and differentiation of R. rugosa tunb was studied using colchicine as the inducer. The colchicine immersion method was selected, and the callus of red Rosa rugosa tunb was immersed in colchicine of different concentrations for 2h, 4h, and 8h. The best culture medium for inducing callus was as follows: MS+1.0mg/L6-BA+0.1mg/L NAA+9.0 g agar+30.0g sucrose. After cultivation, the callus was dense in texture, light green and obviously granular. After the callus was immersed in colchicine, the combination of different colchicine concentrations and treatment time had an interaction with the callus. Therefore, the best treatment method for polyploid-induced Rosa rugosa tunb polyploid should be higher colchicine combined with shorter processing time. This experiment can provide experimental basis and theoretical basis for cultivating Rosa rugosa tunbpolyploid plant with excellent traits, thereby increasing the content of essential oil in the plant itself. © Published under licence by IOP Publishing Ltd.

关键词： Essential oils

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Authentic Learning of Machine Learning to Ransomware Detection and Prevention

Authentic Learning of Machine Learning to Ransomware Detecti...

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IEEE Annual International Computer software and Applications Conference (COMPSAC)

作者： Md Jobair Hossain Faruk Mohammad Masum Hossain Shahriar Kai Qian Dan Lo Department of Software Engineering and Game Development Kennesaw State University USA School of Data Science Kennesaw State University USA Department of Information Technology Kennesaw State University USA Department Computer Science Kennesaw State University USA

ISBN: (数字)9781665488105

ISBN: (纸本)9781665488112

The primary goal of the authentic learning provides students with an engaging and motivating learning environment for students with hands-on experiences in solving real-world security problems. Each learning topic consists of pre-lab, lab, and post-lab (Pre/Lab/Post) activities. With an authentic learning approach, we design and develop portable labware on Google CoLab for ML for ransomware detection and prevention so that students can access and practice these hands-on labs anywhere and anytime without time tedious installation and configuration which will help students more focus on learning of concepts and getting more experience for hands-on problem-solving skills.

关键词： Conferences Machine learning software Internet Ransomware Computer security

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Frame Structure and Protocol Design for Sensing-Assisted NR-V2X Communications

arXiv

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arXiv 2023年

作者： Li, Yunxin Liu, Fan Du, Zhen Yuan, Weijie Shi, Qingjiang Masouros, Christos Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen518055 China Peng Cheng Laboratory Shenzhen518066 China School of Electronic and Information Engineering Nanjing University of Information Science and Technology Nanjing210044 China School of Software Engineering Tongji University Shanghai201804 China Shenzhen Research Institute of Big Data Shenzhen518172 China Department of Electronic and Electrical Engineering University College London LondonWC1E 7JE United Kingdom

The emergence of the fifth-generation (5G) New Radio (NR) technology has provided unprecedented opportunities for vehicle-to-everything (V2X) networks, enabling enhanced quality of services. However, high-mobility V2X networks require frequent handovers and acquiring accurate channel state information (CSI) necessitates the utilization of pilot signals, leading to increased overhead and reduced communication throughput. To address this challenge, integrated sensing and communications (ISAC) techniques have been employed at the base station (gNB) within vehicle-to-infrastructure (V2I) networks, aiming to minimize overhead and improve spectral efficiency. In this study, we propose novel frame structures that incorporate ISAC signals for three crucial stages in the NR-V2X system: initial access, connected mode, and beam failure and recovery. These new frame structures employ 75% fewer pilots and reduce reference signals by 43.24%, capitalizing on the sensing capability of ISAC signals. Through extensive link-level simulations, we demonstrate that our proposed approach enables faster beam establishment during initial access, higher throughput and more precise beam tracking in connected mode with reduced overhead, and expedited detection and recovery from beam failures. Furthermore, the numerical results obtained from our simulations showcase enhanced spectrum efficiency, improved communication performance and minimal overhead, validating the effectiveness of the proposed ISAC-based techniques in NR V2I networks. Copyright © 2023, The Authors. All rights reserved.

关键词： Spectrum efficiency

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An overview of domain-specific foundation model: key technologies, applications and challenges

arXiv

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arXiv 2024年

作者： Chen, Haolong Chen, Hanzhi Zhao, Zijian Han, Kaifeng Zhu, Guangxu Zhao, Yichen Du, Ying Xu, Wei Shi, Qingjiang Shenzhen Research Institute of Big Data Shenzhen518172 China School of Science and Engineering The Chinese University of Hong Kong Shenzhen518172 China China Academy of Information and Communications Technology Beijing100191 China China Mobile Group Device Co. Ltd. Beijing100033 China School of Computer Science and Engineering Sun Yat-sen University Guangzhou510275 China National Mobile Communications Research Laboratory Southeast University Nanjing210096 China Purple Mountain Laboratories Nanjing211111 China School of Software Engineering Tongji University Shanghai201804 China

The impressive performance of ChatGPT and other foundation-model-based products in human language understanding has prompted both academia and industry to explore how these models can be tailored for specific industries and application scenarios. This process, known as the customization of domain-specific foundation models, addresses the limitations of general-purpose models, which may not fully capture the unique patterns and requirements of domain-specific data. Despite its importance, there is a notable lack of comprehensive overview papers on building domain-specific foundation models, while numerous resources exist for general-purpose models. To bridge this gap, this article provides a timely and thorough overview of the methodology for customizing domain-specific foundation models. It introduces basic concepts, outlines the general architecture, and surveys key methods for constructing domain-specific models. Furthermore, the article discusses various domains that can benefit from these specialized models and highlights the challenges ahead. Through this overview, we aim to offer valuable guidance and reference for researchers and practitioners from diverse fields to develop their own customized foundation models. Copyright © 2024, The Authors. All rights reserved.

关键词： Building Information Model

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Adaptive Convolutional Neural Network for Image Super-resolution

arXiv

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arXiv 2024年

作者： Tian, Chunwei Zhang, Xuanyu Wang, Tao Zhang, Yongjun Zhu, Qi Lin, Chia-Wen The School of Software Northwestern Polytechnical University Xi’an710129 China The National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi’an710129 China The Yangtze River Delta Research Institute Northwestern Polytechnical University Taicang215400 China The School of Computer Science Northwestern Polytechnical University Xi’an710129 China The College of Computer Science and Technology Guizhou University Guiyang550025 China The School of Artificial Intelligence Nanjing University of Aeronautics and Astronautics Nanjing210016 China The Department of Electrical Engineering The Institute of Communications Engineering National Tsing Hua University Taiwan

Convolutional neural networks can automatically learn features via deep network architectures and given input samples. However, the robustness of obtained models may face challenges in varying scenes. Bigger differences in network architecture are beneficial to extract more diversified structural information to strengthen the robustness of an obtained superresolution model. In this paper, we proposed a adaptive convolutional neural network for image super-resolution (ADSRNet). To capture more information, ADSRNet is implemented by a heterogeneous parallel network. The upper network can enhance relation of context information, salient information relation of a kernel mapping and relations of shallow and deep layers to improve performance of image super-resolution. That can strengthen adaptability of an obtained super-resolution model for different scenes. The lower network utilizes a symmetric architecture to enhance relations of different layers to mine more structural information, which is complementary with a upper network for image super-resolution. The relevant experimental results show that the proposed ADSRNet is effective to deal with image resolving. Codes are obtained at https://***/hellloxiaotian/ADSRNet. Copyright © 2024, The Authors. All rights reserved.

关键词： Convolutional neural networks

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A Dynamic Adaptive Search Algorithm for CoSpace Rescue Robot 6

A Dynamic Adaptive Search Algorithm for CoSpace Rescue Robot

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6th International Conference on Robotics and Automation engineering, ICRAE 2021

作者： Chengbo, Li Xiaoming, Wang Zixi, Huang Guangyao, Pang Xinyu, Han Yaguang, Lin Dalian Free Trade Zone No. 1 High School Dalian China Key Laboratory of Modern Teaching Technology Ministry of Education Xi'an China School of Computer Science Shaanxi Normal University Xi'an710119 China Shenzhen Middle School Shenzhen China Wuzhou University School of Data Science and Software Engineering Wuzhou China Singapore Polytechnic Advanced Robotics and Intelligent Control Centre Singapore

ISBN: (纸本)9781665406970

The application of robots to assist humans in earthquake rescue has always been an important topic that researchers are constantly studying. CoSpace Rescue is an abstract modeling and simulation of earthquake rescue and a simulation platform for studying robot rescue search algorithms. Its mathematical model is a multi-objective optimization problem. In this paper, we propose a search algorithm design framework for the CoSpace Rescue robot. Based On this framework and the idea of the heuristic search algorithm, we design and implement the 'Multi-objective Optimal Dynamic Adaptive On-Demand Search Algorithm'. The results of the experiment show that the algorithm can dynamically adapt to the changes in the environment and significantly improve the search efficiency of the rescue robot. © 2021 IEEE.

关键词： Earthquakes

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Laplacian2Mesh: Laplacian-Based Mesh Understanding

arXiv

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arXiv 2022年

作者： Dong, Qiujie Wang, Zixiong Li, Manyi Gao, Junjie Chen, Shuangmin Shu, Zhenyu Xin, Shiqing Tu, Changhe Wang, Wenping The School of Computer Science and Technology Shandong University Shandong Qingdao China The School of Software Shandong University Shandong Jinan China The School of Information and Technology Qingdao University of Science and Technology Shandong Qingdao China The School of Computer and Data Engineering Ningbo Institute of Technology Zhejiang University Zhejiang Ningbo China The Department of Computer Science & Engineering Texas A&M University United States

Geometric deep learning has sparked a rising interest in computer graphics to perform shape understanding tasks, such as shape classification and semantic segmentation. When the input is a polygonal surface, one has to suffer from the irregular mesh structure. Motivated by the geometric spectral theory, we introduce Laplacian2Mesh, a novel and flexible convolutional neural network (CNN) framework for coping with irregular triangle meshes (vertices may have any valence). By mapping the input mesh surface to the multi-dimensional Laplacian-Beltrami space, Laplacian2Mesh enables one to perform shape analysis tasks directly using the mature CNNs, without the need to deal with the irregular connectivity of the mesh structure. We further define a mesh pooling operation such that the receptive field of the network can be expanded while retaining the original vertex set as well as the connections between them. Besides, we introduce a channel-wise self-attention block to learn the individual importance of feature ingredients. Laplacian2Mesh not only decouples the geometry from the irregular connectivity of the mesh structure but also better captures the global features that are central to shape classification and segmentation. Extensive tests on various datasets demonstrate the effectiveness and efficiency of Laplacian2Mesh, particularly in terms of the capability of being vulnerable to noise to fulfill various learning tasks. Copyright © 2022, The Authors. All rights reserved.

关键词： Mesh generation

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