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Learning Semantic Textual Similarity via Topic-informed Discrete Latent Variables

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arXiv 2022年

作者： Yu, Erxin Du, Lan Jin, Yuan Wei, Zhepei Chang, Yi School of Artificial Intelligence Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering Jilin University China International Center of Future Science Jilin University China Faculty of Information Technology Monash University Australia

Recently, discrete latent variable models have received a surge of interest in both Natural Language Processing (NLP) and Computer Vision (CV), attributed to their comparable performance to the continuous counterparts in representation learning, while being more interpretable in their predictions. In this paper, we develop a topic-informed discrete latent variable model for semantic textual similarity, which learns a shared latent space for sentence-pair representation via vector quantization. Compared with previous models limited to local semantic contexts, our model can explore richer semantic information via topic modeling. We further boost the performance of semantic similarity by injecting the quantized representation into a transformer-based language model with a well-designed semantic-driven attention mechanism. We demonstrate, through extensive experiments across various English language datasets, that our model is able to surpass several strong neural baselines in semantic textual similarity tasks. © 2022, CC0.

关键词： Semantics

NSPG-Miner: Mining Repetitive Negative Sequential Patterns

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arXiv 2025年

作者： Li, Yan Wang, Zhulin Liu, Jing Guo, Lei Fournier-Viger, Philippe Wu, Youxi Wu, Xindong School of Economics and Management Hebei University of Technology Tianjin300401 China School of Artificial Intelligence Hebei University of Technology Tianjin300401 China State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin300401 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518061 China Hebei Key Laboratory of Big Data Computing 300401 China Key Laboratory of Knowledge Engineering with Big Data The Ministry of Education of China Hefei University of Technology Hefei230009 China

Sequential pattern mining (SPM) with gap constraints (or repetitive SPM or tandem repeat discovery in bioinformatics) can find frequent repetitive subsequences satisfying gap constraints, which are called positive sequential patterns with gap constraints (PSPGs). However, classical SPM with gap constraints cannot find the frequent missing items in the PSPGs. To tackle this issue, this paper explores negative sequential patterns with gap constraints (NSPGs). We propose an efficient NSPG-Miner algorithm that can mine both frequent PSPGs and NSPGs simultaneously. To effectively reduce candidate patterns, we propose a pattern join strategy with negative patterns which can generate both positive and negative candidate patterns at the same time. To calculate the support (frequency of occurrence) of a pattern in each sequence, we explore a NegPair algorithm that employs a key-value pair array structure to deal with the gap constraints and the negative items simultaneously and can avoid redundant rescanning of the original sequence, thus improving the efficiency of the algorithm. To report the performance of NSPG-Miner, 11 competitive algorithms and 11 datasets are employed. The experimental results not only validate the effectiveness of the strategies adopted by NSPG-Miner, but also verify that NSPG-Miner can discover more valuable information than the state-ofthe-art algorithms. Algorithms and datasets can be downloaded from https://***/wuc567/PatternMining/tree/master/NSPG-Miner. Copyright © 2025, The Authors. All rights reserved.

关键词： Miners

A Multi-UAV Trajectory Planning Algorithm for Time-Sensitive Data Collection

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A Multi-UAV Trajectory Planning Algorithm for Time-Sensitive...

Next Generation Data-driven Networks (NGDN), International Conference on

作者： Peng Zhang Baoyu Liu Sihua Gao Bingyang Zhao Weifeng Xu China's Aviation System Engineering Research Institute Beijing China Chinese Aeronautical Establishment Beijing China Civil Aviation University of China Tianjin China North China Electric Power University (Baoding) China Hebei Key Laboratory of Knowledge Computing for Energy & Power China

ISBN: (数字)9798350388374

ISBN: (纸本)9798350388381

Unmanned Aerial Vehicle has been employed to assist wireless sensor networks in collecting data that requires information freshness. However, due to the endurance of a single UAV, completing the data collection mission is difficult. In this paper, we firstly construct a mathematical model for the multi-UAV trajectory planning problem on the basis of time-sensitive data collection. Then, a deep reinforcement learning algorithm, named Independent Proximal Policy Optimization (I-PPO) algorithm, is used to schedule each UAVs access sequence to the sensor nodes or the base station, to minimize the UAV swarm's energy consumption under the condition of data freshness for each sensor node. Numerical results demonstrate that the I-PPO algorithm outperforms the Value-Decomposition Networks (VDN) algorithms in the comparison group in energy consumption and trajectory length of UAVs.

关键词： Energy consumption Wireless sensor networks Schedules Trajectory planning Data integrity Data collection Autonomous aerial vehicles Trajectory Optimization Next generation networking

Bayesian Network Structure Learning and Application

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Mobile Information Systems 2022年 2022卷

作者： Zhang, Liwei Liu, Dayou Wang, Zheng Wang, Xinlu College of Computer Science and Technology Jilin University Jilin130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Jilin130012 China Department of Computer Aviation University of Air Force Jilin130022 China Engineering Training Center Changchun Institute of Engineering Jilin130012 China

With the continuous development of artificial intelligence technology and information technology, a large number of background data are constantly generated. How to obtain effective and useful data in a large and complex data group becomes important and meaningful. The traditional Bayesian network can represent the probability distribution of data variables from a large number of data based on graphical models. It has relatively clear and reliable reasoning ability and decision-making mechanism. However, the traditional Bayesian network structure has serious shortcomings in the recognition accuracy of corresponding key data, so the efficiency of the corresponding algorithm is seriously low. Based on this, this study adds an adaptive genetic algorithm with causality to the original Bayesian structure, so as to optimize the strategy of its structure operation, quantitatively describe the order of the corresponding data nodes, creatively arrange the corresponding data nodes in order by using the node priority, and initialize the initial architecture of Bayesian network based on this. Finally, the network is initialized through information exchange and data score correction, so as to get the final learning results. In this study, the convolution neural network algorithm in a database is verified in the experiment. The experimental results show that the accuracy of the experimental results given by the Bayesian network structure proposed in this study is about 10% higher than the traditional accuracy, and its corresponding learning results basically cover the important algorithms, hypotheses, and verification of convolution neural network, from this level;the algorithm proposed in this study has obvious advantages in bibliometrics. © 2022 Liwei Zhang et al.

关键词： Bayesian networks

BARGAIN-MATCH: A Game Theoretical Approach for Resource Allocation and Task Offloading in Vehicular Edge computing Networks

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arXiv 2022年

作者： Sun, Zemin Sun, Geng Liu, Yanheng Wang, Jian Cao, Dongpu The 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 The School of Vehicle and Mobility Tsinghua University Beijing China

Vehicular edge computing (VEC) is emerging as a promising architecture of vehicular networks (VNs) by deploying the cloud computing resources at the edge of the VNs. However, efficient resource management and task offloading in the VEC network is challenging. In this work, we first present a hierarchical framework that coordinates the heterogeneity among tasks and servers to improve the resource utilization for servers and service satisfaction for vehicles. Moreover, we formulate a joint resource allocation and task offloading problem (JRATOP), aiming to jointly optimize the intra-VEC server resource allocation and inter-VEC server load-balanced offloading by stimulating the horizontal and vertical collaboration among vehicles, VEC servers, and cloud server. Since the formulated JRATOP is NP-hard, we propose a cooperative resource allocation and task offloading algorithm named BARGAIN-MATCH, which consists of a bargaining-based incentive approach for intra-server resource allocation and a matching method-based horizontal-vertical collaboration approach for inter-server task offloading. Besides, BARGAIN-MATCH is proved to be stable, weak Pareto optimal, and polynomial complex. Simulation results demonstrate that the proposed approach achieves superior system utility and efficiency compared to the other methods, especially when the system workload is heavy. Copyright © 2022, The Authors. All rights reserved.

关键词： Game theory

Weakly supervised prototype topic model with discriminative seed words: Modifying the category prior by self-exploring supervised signals

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arXiv 2021年

作者： Wang, Bing Wang, Yue Li, Ximing Ouyang, Jihong College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation Knowledge Engineering of Ministry of Education Jilin University China

Dataless text classification, i.e., a new paradigm of weakly supervised learning, refers to the task of learning with unlabeled documents and a few predefined representative words of categories, known as seed words. The recent generative dataless methods construct document-specific category priors by using seed word occurrences only, however, such category priors often contain very limited and even noisy supervised signals. To remedy this problem, in this paper we propose a novel formulation of category prior. First, for each document, we consider its label membership degree by not only counting seed word occurrences, but also using a novel prototype scheme, which captures pseudo-nearest neighboring categories. Second, for each label, we consider its frequency prior knowledge of the corpus, which is also a discriminative knowledge for classification. By incorporating the proposed category prior into the previous generative dataless method, we suggest a novel generative dataless method, namely Weakly Supervised Prototype Topic Model (Wsptm). The experimental results on real-world datasets demonstrate that Wsptm outperforms the existing baseline methods. © 2021, CC0.

关键词： Text processing

Joint Power and 3D Trajectory Optimization for UAV-enabled Wireless Powered Communication Networks with Obstacles

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arXiv 2023年

作者： Pan, Hongyang Liu, Yanheng Sun, Geng Fan, Junsong Liang, Shuang Yuen, Chau The College of Computer Science and Technology Jilin University Changchun130012 China Pillar Singapore University of Technology and Design 487372 Singapore The Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China The School of Information Science and Technology Northeast Normal University Changchun130024 China

Unmanned aerial vehicle (UAV)-enabled wireless powered communication networks (WPCNs) are promising technologies in 5G/6G wireless communications, while there are several challenges about UAV power allocation and scheduling to enhance the energy utilization efficiency, considering the existence of obstacles. In this work, we consider a UAV-enabled WPCN scenario that a UAV needs to cover the ground wireless devices (WDs). During the coverage process, the UAV needs to collect data from the WDs and charge them simultaneously. To this end, we formulate a joint-UAV power and three-dimensional (3D) trajectory optimization problem (JUPTTOP) to simultaneously increase the total number of the covered WDs, increase the time efficiency, and reduce the total flying distance of UAV so as to improve the energy utilization efficiency in the network. Due to the difficulties and complexities, we decompose it into two sub optimization problems, which are the UAV power allocation optimization problem (UPAOP) and UAV 3D trajectory optimization problem (UTTOP), respectively. Then, we propose an improved non-dominated sorting genetic algorithm-II with K-means initialization operator and Variable dimension mechanism (NSGA-II-KV) for solving the UPAOP. For UTTOP, we first introduce a pretreatment method, and then use an improved particle swarm optimization with Normal distribution initialization, Genetic mechanism, Differential mechanism and Pursuit operator (PSO-NGDP) to deal with this sub optimization problem. Simulation results verify the effectiveness of the proposed strategies under different scales and settings of the networks. Copyright © 2023, The Authors. All rights reserved.

关键词： Energy utilization

Suppress content shift: better diffusion features via off-the-shelf generation techniques 24

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Suppress content shift: better diffusion features via off-th...

Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Benyuan Meng Qianqian Xu Zitai Wang Zhiyong Yang Xiaochun Cao Qingming Huang Institute of Information Engineering CAS and School of Cyber Security University of Chinese Academy of Sciences Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS and Peng Cheng Laboratory Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS School of Computer Science and Tech. University of Chinese Academy of Sciences School of Cyber Science and Tech. Shenzhen Campus of Sun Yat-sen University School of Computer Science and Tech. University of Chinese Academy of Sciences and Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS and Key Laboratory of Big Data Mining and Knowledge Management CAS

ISBN: (纸本)9798331314385

Diffusion models are powerful generative models, and this capability can also be applied to discrimination. The inner activations of a pre-trained diffusion model can serve as features for discriminative tasks, namely, diffusion feature. We discover that diffusion feature has been hindered by a hidden yet universal phenomenon that we call content shift. To be specific, there are content differences between features and the input image, such as the exact shape of a certain object. We locate the cause of content shift as one inherent characteristic of diffusion models, which suggests the broad existence of this phenomenon in diffusion feature. Further empirical study also indicates that its negative impact is not negligible even when content shift is not visually perceivable. Hence, we propose to suppress content shift to enhance the overall quality of diffusion features. Specifically, content shift is related to the information drift during the process of recovering an image from the noisy input, pointing out the possibility of turning off-the-shelf generation techniques into tools for content shift suppression. We further propose a practical guideline named GATE to efficiently evaluate the potential benefit of a technique and provide an implementation of our methodology. Despite the simplicity, the proposed approach has achieved superior results on various tasks and datasets, validating its potential as a generic booster for diffusion features. Our code is available at https://***/Darkbblue/diffusion-content-shift.

关键词：

A Multi-Agent Deep Reinforcement Learning Method for Fully Noisy Observations

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SSRN

SSRN 2023年

作者： Wang, Kaiyu Zhang, Menglin Qu, Bohao Wang, Xianchang College of Computer Science and Technology Jilin University Jilin Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin Changchun130012 China School of Artificial Intelligence Jilin University Jilin Changchun130012 China Chengdu Kestrel Artificial Intelligence Institute Chengdu611730 China

Multi-agent reinforcement learning (MARL) algorithms have achieved great breakthroughs in many aspects. The MARL algorithms can learn effective policies in ideal simulation environments. But different from the ideal simulation environments, noise is unavoidable in the real world. MARL algorithms need to learn effective policies in unavoidable fully noisy environments. In this paper, we consider a challenging multi-agent reinforcement learning problem: All agents cannot observe any noiseless observations from environments during the whole training process and MARL algorithms cannot learn effective policies in these fully noisy observation environments. To solve this problem, we propose a method called Robust Policy Learning under Fully Noisy Observation viA DeNoising REpresentation NeTwork (PLANET), which enables MARL algorithms learning effective policies in fully noisy observation environments. The PLANET method learns the effective policy through two steps. (1) Extracting the noise characteristics and motion laws information from fully noisy observation histories. (2) Making MARL algorithms extract information from the noise characteristics and motion laws information and learn effective policies. The results of a series of exhaustive experiments show that our method can eliminate the effects of noise and learn effective policies in fully noisy observation environments. © 2023, The Authors. All rights reserved.

关键词： Learning algorithms