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arXiv 2022年

作者： Wu, Youxi Hu, Qian Li, Yan Guo, Lei Zhu, Xingquan Wu, Xindong School of Artificial Intelligence Hebei University of Technology Tianjin300401 China Hebei Key Laboratory of Big Data Computing Tianjin300401 China School of Artificial Intelligence Hebei University of Technology Tianjin300401 China School of Economics and Management Hebei University of Technology Tianjin300401 China State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin300401 China Department of Computer Electrical Engineering and Computer Science Florida Atlantic University FL33431 United States Mininglamp Academy of Sciences Mininglamp Technology Beijing100084 China Ministry of Education Hefei230009 China

A time series is a collection of measurements in chronological order. Discovering patterns from time series is useful in many domains, such as stock analysis, disease detection, and weather forecast. To discover patterns, existing methods often convert time series data into another form, such as nominal/symbolic format, to reduce dimensionality, which inevitably deviates the data values. Moreover, existing methods mainly neglect the order relationships between time series values. To tackle these issues, inspired by order-preserving matching, this paper proposes an Order-Preserving sequential Pattern (OPP) mining method, which represents patterns based on the order relationships of the time series data. An inherent advantage of such representation is that the trend of a time series can be represented by the relative order of the values underneath the time series data. To obtain frequent trends in time series, we propose the OPP-Miner algorithm to mine patterns with the same trend (sub-sequences with the same relative order). OPP-Miner employs the filtration and verification strategies to calculate the support and uses pattern fusion strategy to generate candidate patterns. To compress the result set, we also study finding the maximal OPPs. Experiments validate that OPP-Miner is not only efficient and scalable but can also discover similar sub-sequences in time series. In addition, case studies show that our algorithms have high utility in analyzing the COVID-19 epidemic by identifying critical trends and improve the clustering performance. The algorithms and data can be downloaded from https://***/wuc567/Pattern-Mining/tree/master/OPP-Miner. Copyright © 2022, The Authors. All rights reserved.

关键词： Time series

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Hierarchical Learning for IRS-Assisted MEC Systems with Rate-Splitting Multiple Access

arXiv

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arXiv 2024年

作者： Wu, Yinyu Zhang, Xuhui Ren, Jinke Shen, Yanyan Yang, Bo Wang, Shuqiang Guan, Xinping Niyato, Dusit The Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong518055 China The University of Chinese Academy of Sciences Beijing100049 China The Shenzhen Future Network of Intelligence Institute The School of Science and Engineering The Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Guangdong Shenzhen518172 China Shenzhen University of Advanced Technology Guangdong518055 China The Department of Automation The Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University Shanghai200240 China The College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore

Intelligent reflecting surface (IRS)-assisted mobile edge computing (MEC) systems have shown notable improvements in efficiency, such as reduced latency, higher data rates, and better energy efficiency. However, the resource competition among users will lead to uneven allocation, increased latency, and lower throughput. Fortunately, the rate-splitting multiple access (RSMA) technique has emerged as a promising solution for managing interference and optimizing resource allocation in MEC systems. This paper studies an IRS-assisted MEC system with RSMA, aiming to jointly optimize the passive beamforming of the IRS, the active beamforming of the base station, the task offloading allocation, the transmit power of users, the ratios of public and private information allocation, and the decoding order of the RSMA to minimize the average delay from a novel uplink transmission perspective. Since the formulated problem is non-convex and the optimization variables are highly coupled, we propose a hierarchical deep reinforcement learning-based algorithm to optimize both continuous and discrete variables of the problem. Additionally, to better extract channel features, we design a novel network architecture within the policy and evaluation networks of the proposed algorithm, combining convolutional neural networks and densely connected convolutional network for feature extraction. Simulation results indicate that the proposed algorithm not only exhibits excellent convergence performance but also outperforms various benchmarks. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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A multi-perspective exploration of the salinization mechanisms of groundwater in the Guanzhong Basin, China

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Science of the Total Environment 2024年 957卷 177421页

作者： Mu, Dawei Li, Peiyue De Baets, Bernard Li, Dongfeng Li, Zhaoyu He, Song School of Water and Environment Chang'an University No.126 Yanta Road Shaanxi Xi'an710054 China Key Laboratory of Subsurface Hydrology and Ecological Effects in Arid Region of the Ministry of Education Chang'an University No.126 Yanta Road Shaanxi Xi'an710054 China Key Laboratory of Eco-hydrology and Water Security in Arid and Semi-arid Regions of the Ministry of Water Resources Chang'an University No.126 Yanta Road Shaanxi Xi'an710054 China KERMIT Department of Data Analysis and Mathematical Modelling Ghent University Coupure links 653 Ghent9000 Belgium PowerChina Sinohydro Bureau 3 Co. LTD. No. 4069 Expo Avenue Chanba Ecological District Shaanxi Xi'an710024 China PowerChina Northwest Engineering Corporation Limited No. 18 Zhangbadong Road Shaanxi Xi'an710065 China

A comprehensive understanding of the salinization of groundwater in the Guanzhong Basin, China, is crucial for ensuring sustainable groundwater development. However, the mechanism driving salinization in different regions of the basin remains unclear. Therefore, this study employed multivariate statistical methods, hydrochemical analysis, isotope studies, and hydrochemical modeling to uncover the factors and processes influencing groundwater salinization. The results indicate significant regional variations in total dissolved solids (TDS), with concentrations exceeding 1000 mg/L predominantly occurring to the north of the Weihe River and the east of the Jinghe River. The correlations of groundwater chloride (Cl−) with Cl/Br molar ratio and stable isotopes show that groundwater salinity in the Guanzhong Basin is mainly controlled by mineral dissolution, and evaporation. In addition, human activities, such as vertical irrigation recharge and excessive fertilizer use, exacerbate local salinity levels. Irrigation activities worsen the shallow groundwater salt enrichment in the runoff zone of the central basin, revealed by the high salinity (TDS>3000 mg/L), high Cl/Br ratios (>2000), moderate δ2H (−57.5 to −67.5 ‰) and moderate δ18O (−8.1 to −8.9 ‰). High salinity (TDS>1000 mg/L), high nitrate concentration (>100 mg/L), and moderate Cl− (100 to 500 mg/L) indicate the impact of excessive fertilizer use. It is worth noting that intensive groundwater withdrawal disrupts the dynamic balance within the aquifer, causing shallow high-saline groundwater to percolate downward, thereby increasing the risk of deep groundwater pollution. The research enhances the understanding of groundwater salinity transport and provides insights into the effects of groundwater salinization in the irrigation area. © 2024 Elsevier B.V.

关键词： Molar ratio

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Publisher Correction: Two-stage salient object detection based on prior distribution learning and saliency consistency optimization

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The Visual Computer 2022年第12期39卷 6771-6771页

作者： Wu, Yunhe Chang, Xingya Chen, Dongyue Chen, Lei Jia, Tong College of Information Science and Engineering Northeastern University Shenyang China Midea Group Shanghai China Key Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Ministry of Education Shenyang China

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Deceiving LLM through Compositional Instruction with Hidden Attacks

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ACM Transactions on Autonomous and Adaptive Systems 1000年

作者： Shuyu Jiang Xingshu Chen Rui Tang School of Cyber Science and Engineering Sichuan University China School of Cyber Science and Engineering Sichuan University Key Laboratory of Data Protection and Intelligent Management (Sichuan University) Ministry of Education Cyber Science Research Institute Sichuan University China

Recently, large language models (LLMs) have demonstrated promising applications in the autonomous driving (AD) domain, including language-based interactions and decision-making. Ensuring they safely handle harmful inputs is crucial before formal deployment. However, research reveals emerging hand-crafted jailbreak attacks, which pack harmful prompts into harmless instructions, can bypass LLMs’ security mechanisms and elicit harmful responses. To deeply understand such jailbreaks, this paper introduces a Compositional Instruction Attack (CIA) framework to generalize them, and develop two CIA jailbreaking methods to automatically generate tailored jailbreak prompts for each harmful prompt. Then, this paper builds the first CIA question-answering (CIAQA) dataset with 2.7K multiple-choice questions of 900 successful jailbreaks, for assessing LLMs’ ability to identify underlying harmful intents, harmfulness, and task priority in CIA jailbreaks. Combined with experimental analysis on CIAQA and other datasets, this paper concludes three possible reasons for the failure of LLM defenses against CIAs. Finally, we propose an intent-based defense paradigm (IBD), enabling LLMs to defend against CIA by leveraging its capability to identify intents. Experimental results show CIA can achieve attack success rates (ASR) of 95%+ and 85%+ in AD and common harmful scenarios for three well-known LLMs (GPT-4, GPT-3.5, and Llama2-70b-chat), and IBD reduces ASR by 74%+.

关键词： Large language model autonomous driving adversarial attack harmful prompt

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Unraveling the Oxidation Behaviors of MXenes in Aqueous Systems by Active-Learning-Potential Molecular-Dynamics Simulation

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Angewandte Chemie 2023年第32期135卷

作者： Pengfei Hou Yumiao Tian Prof. Yu Xie Prof. Fei Du Prof. Gang Chen Dr. Aleksandra Vojvodic Prof. Jianzhong Wu Dr. Xing Meng Key Laboratory of Physics and Technology for Advanced Batteries (Ministry of Education) College of Physics Jilin University 130012 Changchun P. R. China Department of Chemical and Biomolecular Engineering University of Pennsylvania PA 19104 Philadelphia USA Department of Chemical and Environmental Engineering University of California CA 92521 Riverside USA Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University 130012 Changchun P. R. China

MXenes are 2D materials with great potential in various applications. However, the degradation of MXenes in humid environments has become a main obstacle in their practical use. Here we combine deep neural networks and an active learning scheme to develop a neural network potential (NNP) for aqueous MXene systems with ab initio precision but low cost. The oxidation behaviors of super large aqueous MXene systems are investigated systematically at nanosecond timescales for the first time. The oxidation process of MXenes is clearly displayed at the atomic level. Free protons and oxides greatly inhibit subsequent oxidation reactions, leading to the degree of oxidation of MXenes to exponentially decay with time, which is consistent with the oxidation rate of MXenes measured experimentally. Importantly, this computational study represents the first exploration of the kinetic process of oxidation of super-sized aqueous MXene systems. It opens a promising avenue for the future development of effective protection strategies aimed at controlling the stability of MXenes.

关键词： MXenes Degradation Neural NetworksActive Learning Oxidation Protection

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A Fuzzy Clustering Based Collaborative Filtering Algorithm for Time-aware POI Recommendation

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Journal of Physics: Conference Series 2021年第1期1746卷

作者： Minghao Yin Yanheng Liu Xu Zhou Geng Sun College of Computer Science and Technology Jilin University Changchun Jilin China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun Jilin China Center for Computer Fundamental Education Jilin University Changchun Jilin China

Location based social network develops and gets widely concern along with the population and widespread use of mobile. Point of interest(POI) recommendation become one of the most widely application among location-based service. To get better POI recommendation performance, a fuzzy clustering based collaborative filtering algorithm (FCCF) for time-aware POI recommendation is proposed in this paper. It first constructs the user feature vector from users' check-in behaviours. Individual's check-in behaviour can be under the influence of location region and time slots, so user's feature consists of two parts. One is the vising frequency of each user in different location regions, and the other is the vising frequency of each user in different time slots. Next fuzzy c-means is adopted due to its simplicity to group users according to user feature vector. Then the user similarity computation can be limited in the similar small user groups. In the end, a collaborative filtering algorithm is applied to recommend a number of top-N POIs at a given time for the target user. Some experiments are conducted and the comparative results on Foursquare and Gowalla show that FCCF has higher precision and recall value than the comparative algorithms.

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Ppdrm: Privacy-Preserving Drm Training and Classification on the Cloud

SSRN

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SSRN 2023年

作者： Xu, Lida Tian, Chengliang Zhang, Guoyan Li, Leibo Tian, Weizhong Zhang, Yan College of Computer Science and Technology Qingdao University Qingdao266071 China School of Cyber Science and Technology Shandong University Shandong Ji’nan250101 China Key Laboratory of Cryptologic Technology and Information Security Ministry of Education Shandong Ji’nan250101 China Shandong Institute of Blockchain Shandong Ji’nan250101 China College of Big Data and Internet Shenzhen Technology University Guangdong Shenzhen518118 China College of Electromechanical Engineering Qingdao University of Science and Technology Qingdao266061 China

The prevalence of cloud computing greatly promotes the development of artificial intelligence(AI). Accurately and efficiently classify data on the cloud is a classic AI task. The recent Discriminative Ridge Machine(DRM) is a new type of supervised classifier which outperforms many other classic classifiers in accuracy and efficiency, especially for high-dimensional data or unbalanced ***, the DRM training and classification task is tedious for resource-constrained data owners. Motivated by this, in this paper, we study the outsourcing of DRM training and classification within a cloud environment and propose a quantum secure and efficient solution for this problem. Specifically, based on the two non-colluding servers model, we adopt the quantum-immune CKKS cryptosystem to design a series of homomorphic security protocols for matrix and argmin operations and realize the correct model training and data classification without leaking the privacy of training dataset, the DRM model, the query data and the classification results. We argue the $\mathcal{CPA}$ secure of our design and evaluate the accuracy and efficiency from theory and experiment perspectives. Our theoretical and experimental analysis demonstrates that our design can achieve the same classification accuracy as the origin plaintext-based DRM classifier and the query-response time is reasonable and acceptable. © 2023, The Authors. All rights reserved.

关键词： Classification (of information)

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Research on the Design and Construction of Sanya Smart Tourism Information Platform Based on Federated Migration Algorithm

Research on the Design and Construction of Sanya Smart Touri...

引用

Digital Society and Intelligent Systems (DSInS), International Conference on

作者： Kun Zhang Jia Zhu Xuebin Huang Haifeng Wang Vaniushkina Dina Kaparova Kumushai Zhe Zhao Penghui Zeng Key Laboratory of Island Tourism Resource Data Mining and Monitoring Ministry of Culture and Tourism Hainan Tropical Ocean University Sanya China College of Art Hainan Tropical Ocean University Sanya China College of Computer Science and Technology Hainan Tropical Ocean University Sanya China Education Center of MTA College of Tourism Hainan Tropical Ocean University Sanya China Hainan Tropical Ocean University Sanya China College of Information and Communication Engineering Hainan University Haikou China

ISBN: (纸本)9781665406314

The article is a series of research results of the smart tourism construction research project in Asia. Through the preliminary research, it analyzes and studies the problems existing in the smart tourism construction of Sanya City, and gives the countermeasures and measures for the smart tourism construction of Sanya City from multiple angles. Suggestions and based on the federal migration algorithm, the Sanya smart tourism information platform was designed, and the comprehensive Sanya smart tourism construction was analyzed and researched.

关键词： Trade agreements Urban areas Asia Task analysis Intelligent systems Information technology

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FABind: fast and accurate protein-ligand binding 23

FABind: fast and accurate protein-ligand binding

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Qizhi Pei Kaiyuan Gao Lijun Wu Jinhua Zhu Yingce Xia Shufang Xie Tao Qin Kun He Tie-Yan Liu Rui Yan Gaoling School of Artificial Intelligence Renmin University of China and Engineering Research Center of Next-Generation Intelligent Search and Recommendation Ministry of Education School of Computer Science and Technology Huazhong University of Science and Technology Microsoft Research AI4Science School of Information Science and Technology University of Science and Technology of China Gaoling School of Artificial Intelligence Renmin University of China Gaoling School of Artificial Intelligence Renmin University of China and Beijing Key Laboratory of Big Data Management and Analysis Methods

Modeling the interaction between proteins and ligands and accurately predicting their binding structures is a critical yet challenging task in drug discovery. Recent advancements in deep learning have shown promise in addressing this challenge, with sampling-based and regression-based methods emerging as two prominent approaches. However, these methods have notable limitations. Sampling-based methods often suffer from low efficiency due to the need for generating multiple candidate structures for selection. On the other hand, regression-based methods offer fast predictions but may experience decreased accuracy. Additionally, the variation in protein sizes often requires external modules for selecting suitable binding pockets, further impacting efficiency. In this work, we propose FABind, an end-to-end model that combines pocket prediction and docking to achieve accurate and fast protein-ligand binding. FABind incorporates a unique ligand-informed pocket prediction module, which is also leveraged for docking pose estimation. The model further enhances the docking process by incrementally integrating the predicted pocket to optimize protein-ligand binding, reducing discrepancies between training and inference. Through extensive experiments on benchmark datasets, our proposed FABind demonstrates strong advantages in terms of effectiveness and efficiency compared to existing methods. Our code is available at Github https://***/QizhiPei/FABind.

关键词：

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