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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Liang, Hanzhe Xie, Guoyang Hou, Chengbin Wang, Bingshu Gao, Can Wang, Jinbao College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen Audencia Financial Technology Institute Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Intelligent Manufacturing CATL Ningde China School of Computing and Artificial Intelligence Fuyao University of Science and Technology Fuzhou China School of Software Northwestern Polytechnical University Xi’an China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China

ISBN: (纸本)157735897X

3D anomaly detection has recently become a significant focus in computer vision. Several advanced methods have achieved satisfying anomaly detection performance. However, they typically concentrate on the external structure of 3D samples and struggle to leverage the internal information embedded within samples. Inspired by the basic intuition of why not look inside for more, we introduce a straightforward method named Internal Spatial Modality Perception (ISMP) to explore the feature representation from internal views fully. Specifically, our proposed ISMP consists of a critical perception module, Spatial Insight Engine (SIE), which abstracts complex internal information of point clouds into essential global features. Besides, to better align structural information with point data, we propose an enhanced key point feature extraction module for amplifying spatial structure feature representation. Simultaneously, a novel feature filtering module is incorporated to reduce noise and redundant features for further aligning precise spatial structure. Extensive experiments validate the effectiveness of our proposed method, achieving object-level and pixel-level AUROC improvements of 4.2% and 13.1%, respectively, on the Real3D-AD benchmarks. Note that the strong generalization ability of SIE has been theoretically proven and is verified in both classification and segmentation tasks. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Feature extraction

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Research on HIV/AIDS Epidemic Trend Prediction Model Based on ARIMA-LSTM

Research on HIV/AIDS Epidemic Trend Prediction Model Based o...

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IEEE International Conference on Orange Technologies (ICOT)

作者： Xin Guo Yichen Yang Shih-Pang Tseng Zilong Yin Cheng Wang School of Information Science and Technology Sanda University Shanghai China School of Software and Big Data Changzhou College of Information Technology Changzhou China Department of Electronic and Electrical Engineering Shanghai University of Engineering Science Shanghai China

ISBN: (纸本)9781665454209

In China, the number of persons who have infected Acquired Immune Deficiency Syndrome (AIDS) is rising quickly every year. Various public health and social issues have been brought on by the Human Immunodeficiency Virus/ Acquired Immune Deficiency Syndrome (HIV/AIDS) epidemic. Understanding the epidemic's development trend is crucial for controlling and preventing HIV/AIDS. Long Short-Term Memory (LSTM) is a nonlinear model, whereas Autoregressive Integrated Moving Average model (ARIMA) is a linear model. Both linear and nonlinear tendencies can be seen in China's HIV/AIDS prevalence rates. In order to forecast the HIV/AIDS pandemic trajectory, this research investigates the combined ARIMA-LSTM model. The increasing number of HIV/AIDS patients was predicted using the ARIMA model, the LSTM model, and the ARIMA-LSTM combination model, and their fitting effects were compared. The findings demonstrate that all four models can be used to predict the rising number of HIV/AIDS patients, however the parallel ARIMA-LSTM combination model has a better fitting prediction effect than the other three models. It works better as a forecasting model for the AIDS pandemic trend.

关键词： Pandemics Acquired immune deficiency syndrome Fitting Time series analysis Predictive models Market research Feature extraction

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An Unstructured data Representation Enhanced Model for Postoperative Risk Prediction 21

An Unstructured Data Representation Enhanced Model for Posto...

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21st Chinese National Conference on Computational Linguistic, CCL 2022

作者： Wang, Yaqiang Yang, Xiao Hao, Xuechao Shu, Hongping Chen, Guo Zhu, Tao College of Software Engineering Chengdu University of Information Technology China Institute for Data Science and Engineering Chengdu University of Information Technology China Sichuan Key Laboratory of Software Automatic Generation and Intelligent Service China Department of Anesthesiology Sichuan University China

Postoperative risk prediction has a positive effect on clinical resource planning, emergency plan preparation and reducing postoperative risk and mortality. Postoperative risk prediction is mainly based on patient's basic information, laboratory tests, vital signs and other structured data, while the value of unstructured preoperative diagnosis with rich semantic information remains to be verified. Aiming at attempting this problem, an unstructured data representation enhanced postoperative risk prediction model is proposed in this paper. The model utilizes self-attention to fuse structured data with preoperative diagnosis. Through comparing with the commonly used statistical machine learning models and the state-of-the-art deep neural networks, the proposed model has not only better prediction performance, but also better interpretability. © 2022 China National Conference on Computational Linguistics Published under Creative Commons Attribution 4.0 International License.

关键词： Deep neural networks

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Context-Aware Collaborative Ranking 15th

Context-Aware Collaborative Ranking

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15th Asia Information Retrieval Societies Conference, AIRS 2019

作者： Dai, Wei Pan, Weike Ming, Zhong National Engineering Laboratory for Big Data System Computing Technology College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9783030428341

Recommender systems (RS) are being used in a broad range of applications, from online shopping websites to music streaming platforms, which aim to provide users high-quality personalized services. Collaborative filtering (CF) is a promising technique to ensure the accuracy of a recommender system, which can be divided into specific tasks such as rating prediction and item ranking. However, there is a larger volume of published works studying the problem of rating prediction, rather than item ranking though it is recognized to be more appropriate for the final recommendation in a real application. On the other hand, many studies on item ranking devoted to leveraging implicit feedback are limited in performance improvements due to the uniformity of implicit feedback. Hence, in this paper, we focus on item ranking with informative explicit feedback, which is also called collaborative ranking. In particular, we propose a novel recommendation model termed context-aware collaborative ranking (CCR), which adopts a logistic loss function to measure the predicted error of ranking and exploits the inherent preference context derived from the explicit feedback. Moreover, we design an elegant strategy to distinguish between positive and negative samples used in the process of model training. Empirical studies on four real-world datasets clearly demonstrate that our CCR outperforms the state-of-the-art methods in terms of various ranking-oriented evaluation metrics. © Springer Nature Switzerland AG 2020.

关键词： Collaborative filtering

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Random Forest and Sobol Method Based data Center PUE Prediction

Random Forest and Sobol Method Based Data Center PUE Predict...

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International Conference on Information, Communication and Networks (ICICN)

作者： Longchuan Yan Zhaofang Luan Ling He Congfeng Jiang Junming Liu Chuanqi Xu Liangbin Zhang Information & Telecommunication Branch State Grid Corporation of China Beijing China School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Information Technology Center Hangzhou Dianzi University Hangzhou China College of Big Data and Software Engineering Zhejiang Wanli University Ningbo China

ISBN: (数字)9798350355802

ISBN: (纸本)9798350355819

data centers are critical infrastructures that support a multitude of business loads such as big data, artificial intelligence, and cloud computing. While providing the necessary computing power, the problem of high energy consumption in data centers has become a major obstacle limiting their scale-up, reliability and quality of service. Improving the power usage effectiveness (PUE) of data centers is an important way to improve the energy efficiency of data centers. With the development of theories and methods of machine learning, modeling, analysis and prediction of data center energy consumption using machine learning techniques has become a feasible option. In this study, several accurate PUE prediction models are constructed using neural networks and machine learning techniques for the PUE of a small air-cooled data center in actual operation, among which the mean absolute error of the optimal model reaches 0.29%. In addition, this paper also uses Random Forest and Sobol methods to model and conduct sensitivity analyses of key factors affecting data center PUE, revealing the relationship between data center operating parameters and PUE, and finding that the cooling system exhibits different energy consumption patterns under different IT equipment power conditions, which has a decisive impact on PUE. Based on these sensitivity analyses, we argue that for small air-cooled data centers, improving internal insulation and humidity is an important strategy for optimizing PUE.

关键词： Temperature measurement data centers Energy consumption Sensitivity analysis Atmospheric modeling Predictive models data models Energy efficiency Random forests Optimization

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Optimizing Ethanol Production in Escherichia Coli Using a Hybrid of Particle Swarm Optimization and Artificial Bee Colony 22

Optimizing Ethanol Production in Escherichia Coli Using a Hy...

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Proceedings of the 6th International Conference on Advances in Artificial Intelligence

作者： Mohamad Faiz Dzulkalnine Mohd Saberi Mohamad Yee Wen Choon Muhammad Akmal Remli Hany Alashwal Artificial Intelligence and Bioinformatics Research Group Faculty of Computing Universiti Teknologi Malaysia Malaysia Health Data Science Lab Department of Genetics and Genomics College of Medical and Health Sciences United Arab Emirates University UAE and Big Data Analytics Center United Arab Emirates University UAE Institute for Artificial Intelligence and Big Data Universiti Malaysia Kelantan Malaysia and Department of Data Science Universiti Malaysia Kelantan Malaysia Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University UAE and Big Data Analytics Center United Arab Emirates University UAE

ISBN: (纸本)9781450396943

Metabolic engineering for biomass production using microorganisms’ cell has received considerable attention in recent years. This is due to the biomass products being extensively used in the field of food additives, supplements, pharmaceuticals, and polymer materials. In this paper, ethanol production in Escherichia coli (E. coli) is the desired product. Sugarcane and corn are often used to produce ethanol. However, one of the problems to produce adequate amounts of ethanol is that large areas are needed to plant sugarcane and corn. Furthermore, the amount of time for the process of dry milling and wet milling is high, which are 40 to 50 hours and 24 to 48 hours, respectively. The wet laboratory is also having limitation on the production of ethanol in microorganisms because the amount of the ethanol produced is not satisfying. Hence, a lot of metabolic engineering techniques is introduced to enhance the production of ethanol in E. coli, such as gene knockout strategy, but the production is yet to meet the demand. Therefore, this paper proposes a hybrid algorithm of Particle Swarm Optimization with the Artificial Bee Colony algorithm (PSOABC) to identify the optimal set of gene knockout strategy to improve the ethanol production in E. coli. A list of genes to knockout, production of the desired product, and growth rate are presented in this paper. PSOABC has shown better performance in terms of production, growth rate and accuracy.

关键词： Gene Knockouts Particle Swarm Optimization Artificial Bee Colony Ethanol Flux Balance Analysis

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An Application of Localized Model Explainability: Identifying Key Disparities in Social Determinants of Health in Food Deserts

An Application of Localized Model Explainability: Identifyin...

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IEEE International Conference on Big data

作者： Md Shafiul Alam Namazbai Ishmakhametov Ying Xie Sumit Chakravarty Analytics and Data Science Kennesaw State University Kennesaw USA College of Computing & Software Engineering Kennesaw State University Kennesaw USA Department of Electronic Engineering Kennesaw State University Kennesaw USA

ISBN: (纸本)9781665480468

Food deserts are geographic areas where people have limited access to healthy and affordable food. Millions of people in the US are experiencing adverse effects stemming from living in such areas and it becomes an important challenge for federal and local authorities to improve the quality of life of people living in food deserts. The majority of studies in this field explicitly consider income level when analyzing problems of food deserts, thus shadowing other attributes that might provide deeper insights into the problems of the food desert’s population. The current paper assesses the significance of subtler, non-income related characteristics of census tracts by using a new variable attribution technique Appley which approximates Shapley values in linear time. The results show that attributes of census tracts that fall into the set of the so called "social determinants of health" have similar classification power compared to models that explicitly use income variables. The results suggest that each food desert has its own unique set of problems with social determinants of health with varying levels of intensities of each problem and this allows to clearly distinguish not only food deserts from non-food desert areas but also distinguish food deserts from each other. Appley attributes localized importance scores to food deserts’ social determinants of health, thus showing which issues are more severe in a given food desert compared to another one. This allows for devising food desert-specific solutions to the health issues of desert residents thus letting authorities improve the quality of life of people in a targeted and budget-efficient manner.

关键词： Correlation Local government Sociology Stability criteria Neural networks Buildings Big data

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Multi-objective Aerial IRS-assisted ISAC Optimization via Generative AI-enhanced Deep Reinforcement Learning

arXiv

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arXiv 2025年

作者： Xie, Wenwen Sun, Geng Wang, Jiacheng Du, Hongyang Kang, Jiawen Huang, Kaibin Leung, Victor C.M. College of Computer Science and Technology Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Department of Electrical and Electronic Engineering The University of Hong Kong 999077 Hong Kong School of Automation Guangdong University of Technology Guangzhou510006 China Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen518115 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering The University of British Columbia VancouverV6T 1Z4 Canada

Integrated sensing and communication (ISAC) has garnered substantial research interest owing to its pivotal role in advancing the development of next-generation (6G) wireless networks. However, achieving a performance balance between communication and sensing in the dual-function radar communication (DFRC)-based ISAC system remains a significant challenge. In this paper, an aerial intelligent reflecting surface (IRS)-assisted ISAC system is explored, where a base station (BS) supports dual-functional operations, enabling both data transmission for multiple users and sensing for a blocked target, with the channel quality enhanced by an IRS mounted on the unmanned aerial vehicle (UAV). Moreover, we formulate an integrated communication, sensing, and energy efficiency multi-objective optimization problem (CSEMOP), which aims to maximize the communication rate of the users and the echo rate of the target, while minimizing UAV propulsion energy consumption by jointly optimizing the BS beamforming matrix, IRS phase shifts, the flight velocity and angle of the UAV. Considering the non-convexity, trade-off, and dynamic nature of the formulated CSEMOP, we propose a generative diffusion model-based deep deterministic policy gradient (GDMDDPG) method to solve the problem. Specifically, the diffusion model is incorporated into the actor network of DDPG to improve the action quality, with noise perturbation mechanism for better exploration and recent prioritized experience replay (RPER) sampling mechanism for enhanced training efficiency. Simulation results indicate that the GDMDDPG method delivers superior performance compared to the existing methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Multiobjective optimization

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Correction: Agile meets quantum: a novel genetic algorithm model for predicting the success of quantum software development project

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Automated software engineering 2024年第1期32卷 1-11页

作者： Khan, Arif Ali Akbar, Muhammad Azeem Lahtinen, Valtteri Paavola, Marko Niazi, Mahmood Alatawi, Mohammed Naif Alotaibi, Shoayee Dlaim M3S Empirical Software Engineering Research Unit University of Oulu Oulu Finland Software Engineering Department Lappeenranta-Lahti University of Technology Lappeenranta Finland Quanscient Oy Tampere Finland VTT Technical Research Ctr of Finland Ltd Oulu Finland Department of Information and Computer Science King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia Interdisciplinary Research Centre for Intelligent Secure Systems King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia Information Technology Department Faculty of Computers and Information Technology University of Tabuk Tabuk Saudi Arabia Department of Artificial Intelligence and Data Science College of Computer Science and Engineering University of Hail Hail Saudi Arabia

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A Gait Recognition Method Based on the Combination of Human Body Posture and Human Body Contour 2

A Gait Recognition Method Based on the Combination of Human ...

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2nd International Conference on Artificial Intelligence and Computer science, AICS 2020

作者： Ma, Yue Wei, Chenghao Long, Hao Ministry of Public Security China Big Data Institute College of Computer Science and Software Engineering Shenzhen University China

Aiming at the issues that affect the gait recognition, such as outfit changes and carry-on-objects during gait recognition, this paper proposes a gait recognition method based on the improved GaitSet network, which relies on the fusion of human posture and human contour. This method introduces key points of human posture, and improves the precision of human contour extraction by introducing the key points of human posture. At the same time, the human posture map composed of the key points of human posture is used as the synchronous attribute of the contour map to extract gait features. Because key points focus on the inherent walking characteristics of human body, they are not affected by the external information such as clothing and carrying objects. Combined with the rich gait change attributes of human contour, it can effectively improve the accuracy and robustness of the gait recognition model. In scenes of human body wearing coats, our experimental results show that the accuracy of the proposed method in CASIA-B gait data set and self-built database can reach over 73.5% accuracy. © Published under licence by IOP Publishing Ltd.

关键词： Gait analysis

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