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A frequency and two-hop configuration checking-driven local search algorithm for the minimum weakly connected dominating set problem

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Neural Computing and Applications 2024年第22期36卷 13833-13852页

作者： Li, Ruizhi He, Jintao Lin, Cuisong Liu, Ying Hu, Shuli Yin, Minghao Jilin University of Finance and Economics Changchun130117 China Northeast Normal University Changchun130117 China Business Big Data Research Center of Jilin Province Changchun130117 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China

The minimum weakly connected dominating set problem is a typical NP-hard problem with a wide range of applications. To solve this problem, we propose a frequency property and two-hop configuration checking strategy-driven local search algorithm (FCC2LS). In this algorithm, we first propose a lock-vertex-based initial solution construction procedure. This procedure guarantees that certain vertices, which must be included in the optimal solution, are added to the solution. Second, we propose a two-hop configuration checking strategy and a frequency property. The two-hop configuration checking strategy is a new variant of the original configuration checking strategy, which prohibits vertices with unchanged configuration from being added to the candidate solution. The frequency property is used to record the number of times for each vertex is added to the solution, which can increase the diversity of selected vertices. Third, we combine two scoring functions, Dscore and Nscore, with the above strategies and propose effective vertex selection methods to help the algorithm select suitable vertices to add into or remove from the candidate solutions. Finally, we evaluate the proposed algorithm FCC2LS with four state-of-the-art algorithms on four groups of benchmark instances. Experimental results show that our algorithm performs better on four classical benchmark instances. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Combinatorial optimization

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Few-Shot Partial Multi-View Learning

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2023年第10期45卷 11824-11841页

作者： Zhou, Yuan Guo, Yanrong Hao, Shijie Hong, Richang Luo, Jiebo Ministry of Education Hefei University of Technology The Key Laboratory of Knowledge Engineering with Big Data Hefei230009 China Hefei University of Technology The School of Computer Science and Information Engineering Hefei230009 China University of Rochester The Department of Computer Science RochesterNY14627 United States

It is often the case that data are with multiple views in real-world applications. Fully exploring the information of each view is significant for making data more representative. However, due to various limitations and failures in data collection and pre-processing, it is inevitable for real data to suffer from view missing and data scarcity. The coexistence of these two issues makes it more challenging to achieve the pattern classification task. Currently, to our best knowledge, few appropriate methods can well-handle these two issues simultaneously. Aiming to draw more attention from the community to this challenge, we propose a new task in this paper, called few-shot partial multi-view learning, which focuses on overcoming the negative impact of the view-missing issue in the low-data regime. The challenges of this task are twofold: (i) it is difficult to overcome the impact of data scarcity under the interference of missing views;(ii) the limited number of data exacerbates information scarcity, thus making it harder to address the view-missing issue in turn. To address these challenges, we propose a new unified Gaussian dense-anchoring method. The unified dense anchors are learned for the limited partial multi-view data, thereby anchoring them into a unified dense representation space where the influence of data scarcity and view missing can be alleviated. We conduct extensive experiments to evaluate our method. The results on Cub-googlenet-doc2vec, Handwritten, Caltech102, Scene15, Animal, ORL, tieredImagenet, and Birds-200-2011 datasets validate its effectiveness. The codes will be released at https://***/zhouyuan888888/UGDA. © 1979-2012 IEEE.

关键词： Neural networks

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DSM: Question Generation over knowledge Base via Modeling Diverse Subgraphs with Meta-learner

DSM: Question Generation over Knowledge Base via Modeling Di...

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2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Guo, Shasha Zhang, Jing Wang, Yanling Zhang, Qianyi Li, Cuiping Chen, Hong School of Information Renmin University of China Beijing China Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education China

Existing methods on knowledge base question generation (KBQG) learn a one-size-fits-all model by training together all subgraphs without distinguishing the diverse semantics of subgraphs. In this work, we show that making use of the past experience on semantically similar subgraphs can reduce the learning difficulty and promote the performance of KBQG models. To achieve this, we propose a novel approach to model diverse subgraphs with meta-learner (DSM). Specifically, we devise a graph contrastive learning-based retriever to identify semantically similar subgraphs, so that we can construct the semantics-aware learning tasks for the meta-learner to learn semantics-specific and semantics-agnostic knowledge on and across these tasks. Extensive experiments on two widely-adopted benchmarks for KBQG show that DSM derives new state-of-the-art performance and benefits the question answering tasks as a means of data augmentation. Codes and datasets are available online. © 2022 Association for Computational Linguistics.

关键词： Semantics

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ATPRINPM: A single-source domain generalization method for the remaining useful life prediction of unknown bearings 3

ATPRINPM: A single-source domain generalization method for t...

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3rd International Conference on Sensing, Measurement and data Analytics in the Era of Artificial Intelligence, ICSMD 2022

作者： Xu, Juan Ma, Bin Fan, Yuqi Ding, Xu Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology School of Computer and Information Hefei China

ISBN: (纸本)9781665492812

The remaining useful life (RUL) of bearings is critical to the proper operation of mechanical equipment, maintenance of equipment costs and availability. The existing domain adaptation methods have had great success in RUL prediction. However, when the target bearing data are unavailable or unknown to be involved in model training, the domain adaptation approaches also incapable. To solve the problem, we propose a parallel reversible instance normalization method based on adaptive threshold stage division for remaining useful life prediction of unknown bearings. First, we design an adaptive threshold method to find degradation points to divide the healthy and degradation stages. Then according to time series, we merge the original vibration data and its instance normalized data to increase the data distribution diversity. Finally, we combine instance normalization and parallel reversible normalization of the source bearing data into unified RUL learning framework to solve the uncertainty of counterfactual data and improve RUL prediction performance. The results show that the method is superior to the state-of-the-art methods for RUL prediction of unknown bearings. © 2022 IEEE.

关键词： Adaptive Threshold Reversible Instance Normalization RUL Prediction Stage Division

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IDIDNG: A Domain Generalization Remaining Useful Life Prediction Method of Unknown Bearings 3

IDIDNG: A Domain Generalization Remaining Useful Life Predic...

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3rd International Conference on Sensing, Measurement and data Analytics in the Era of Artificial Intelligence, ICSMD 2022

作者： Xu, Juan Xu, Zhen Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data School of Computer and Information Hefei China

ISBN: (纸本)9781665492812

Domain adaptation (DA) -based RUL prediction methods have achieved great success for the adaptation ability of the distribution discrepancy between the source and target domains. However, DA methods are powerless when the target domain data are not available for training. To solve this problem, we propose an inter-domain intra-domain normalized generalization (IDIDNG) network, which consists of three modules, respectively, the pre-processing module, the feature transformation module, and the RUL prediction module. First, we design the pre-processing module to process the bearing vibration data with peak-to-peak and Z-score. Finally, it is connected into a four-dimensional array. In the feature transformation module, via intra-domain and inter-domain normalization as well as mean-variance cross-swap, we transform the data distribution expressions of invariant features of bearings from the perspective of different bearings and different degradation stage of one bearing, such that the model enables to learn the intra-domain and inter-domain discrepancy. Further we design four adaptable weighting parameters into the intra-domain normalization to learn the appropriate normalized mean and variance via the model training. Finally, we design the GRU-based RUL prediction module to predict the unknown bearings. We conducted experiments under the PHM2012 dataset, experimental results show that our method achieves satisfactory prediction accuracy in the unknown bearings. © 2022 IEEE.

关键词： adaptive weights cross-swap generalization inter-domain intra-domain normalization RUL

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Polynomial-Time Assignment-Based Cell Association with Generic Utility Functions

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China Communications 2022年第9期19卷 214-228页

作者： Lusheng Wang Chao Fang Hai Lin Min Peng Caihong Kai Key Laboratory of Knowledge Engineering with Big Data Ministry of Education.School of Computer Science and Information EngineeringHefei University of TechnologyHefei 230601China Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education.School of Cyber Science and EngineeringWuhan UniversityWuhan 430072China

Cell association is a significant research issue in future mobile communication systems due to the unacceptably large computational time of traditional *** article proposes a polynomial-time cell association scheme which not only completes the association in polynomial time but also fits for a generic optimization objective *** the one hand,traditional cell association as a non-deterministic polynomial(NP)hard problem with a generic utility function is heuristically transformed into a 2-dimensional assignment optimization and solved by a certain polynomial-time algorithm,which significantly saves computational *** the other hand,the scheme jointly considers utility maximization and load balancing among multiple base stations(BSs)by maintaining an experience pool storing a set of weighting factor values and their corresponding *** an association optimization is required,a suitable weighting factor value is taken from the pool to calculate a long square utility matrix and a certain polynomial-time algorithm will be applied for the *** with several representative schemes,the proposed scheme achieves large system capacity and high fairness within a relatively short computational time.

关键词： mobile communication system 2-dimensional assignment problem hungarian algorithm fairness cell association

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Using Machine Learning with Pyspark for Solving a Big data Problem: Searching for Particles in High Energy Physics with Big Mass (HEPMASS) 7th

Using Machine Learning with Pyspark for Solving a Big Dat...

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7th International Conference on Smart City Applications, SCA 2022

作者： Azhari, Mourad Ahaji, Khalid Abarda, Abdallah Ettaki, Badia Zerouaoui, Jamal Center of Guidance and Planning Rabat Morocco Laboratoire de Modélisation Mathématiques et de Calculs Economiques FSJES Université Hassan 1er Settat Morocco Laboratory of Research in Computer Science Data Sciences and Knowledge Engineering Department of Data Content and Knowledge Engineering School of Information Sciences Rabat Morocco Laboratory of Engineering Sciences and Modeling Faculty of Sciences Ibn Tofail University Kenitra Morocco

ISBN: (纸本)9783031268519

The search for exotic particles is one of the most challenging topics for physicists. This work aims to resolve the Big data problem in the exotic particle area using the Apache Spark environment with the MLlib library. For this purpose, we analyze and discuss the performance of the following methods: Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), and Gradient Boosted Tree (GBT). The results are very encouraging and prove that these proposed methods run efficiently with an Accuracy exceeding (83%). While the Gradient Boosted Tree (GBT) reaches a high AUC score 85%, better than Decision Tree (DT) and Random Forest. In this manuscript, we attempt to solve a big data problem using the "HEPMASS" dataset. Our data includes 10.5 million examples collected from the UCI website for the experimental phase. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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Partial Label Learning via Conditional-Label-Aware Disambiguation

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Journal of Computer Science & Technology 2021年第3期36卷 590-605页

作者： Peng Ni Su-Yun Zhao Zhi-Gang Dai Hong Chen Cui-Ping Li Key Laboratory of Data Engineering and Knowledge Engineering(Renmin University of China) Ministry of Education Beijing 100087China School of Information Renmin University of ChinaBeijing 100087China

Partial label learning is a weakly supervised learning framework in which each instance is associated with multiple candidate labels,among which only one is the ground-truth *** paper proposes a unified formulation that employs proper label constraints for training models while simultaneously performing *** existing partial label learning approaches that only leverage similarities in the feature space without utilizing label constraints,our pseudo-labeling process leverages similarities and differences in the feature space using the same candidate label constraints and then disambiguates noise *** experiments on artificial and real-world partial label datasets show that our approach significantly outperforms state-of-the-art counterparts on classification prediction.

关键词： disambiguation partial label learning similarity and dissimilarity weak supervision

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Interpreting the configuration of micro-urban environments: Line-based Analyses vs. Visibility Graph Analyses for estimating pedestrian flows 14

Interpreting the configuration of micro-urban environments: ...

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14th International Space Syntax Symposium, SSS 2024

作者： Mara, Federico Hacar, Özge Öztürk Gülgen, Fatih Altafini, Diego Department of Energy Systems Territory and Construction Engineering University of Pisa Italy Department of Geomatic Engineering Yildiz Technical University Turkey Knowledge Discovery and Data Mining Laboratory ISTI-CNR Italy Welsh School of Architecture Cardiff University United Kingdom

ISBN: (纸本)9791256690329

The existence of strong correlations between pedestrian flows and Space Syntax metrics, whether in form of Axial Analysis (AA), Angular Segment Analysis (ASA), or Visibility Graph Analysis (VGA), has been extensively discussed. While AA and ASA tend to perform these estimations well, at both architectural and urban scales, critical aspects emerge in the VGA, especially when applied to micro-urban scales. Those can be summarized in the grids’ non-randomness and their spacing, the impact of variations in altitude and the heavy computational workloads required to model visual fields’ relationships in larger areas. Still, VGA is a valuable resource to micro-urban analyses, as it depicts details within areas characterized by wide open spaces or higher morphological complexity that are beyond line-based models’ capabilities, potentially enabling more precise insights into local movement logics. Considering this, the paper proposes an initial methodological discussion on the methods for estimating pedestrian flows at micro-urban scales, comparing different environment extensions, techniques, metrics, and extraction methods. Tests are performed within a 10.000m2 historical district at Pisa, Italy, which aligns with the spatial and morphological characteristics deemed suitable to VGA. Using spatial statistics, measures associated to pedestrian flows are correlated to observed pedestrian traffic passing through 10 gates in a span of three days to highlight what measure gives a better response. This research encourages the exploration of VGA within micro-urban contexts and can be useful to enable a finer-grained comprehension of social logics in space, as many VGA measures seem to be suitable to comprehend pedestrian flows hierarchies. © 14th International Space Syntax Symposium, SSS 2024.

关键词： Syntactics

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Exploring Spatial Crime Impedance to highlight risky places: A Space Syntax-based environmental approach to urban security 14

Exploring Spatial Crime Impedance to highlight risky places:...

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14th International Space Syntax Symposium, SSS 2024

作者： Mara, Federico Hacar, Müslüm Hacar, Özge Öztürk Altafini, Diego Cutini, Valerio Department of Energy Systems Territory and Construction Engineering University of Pisa Italy Department of Geomatic Engineering Yildiz Technical University Turkey Knowledge Discovery and Data Mining Laboratory ISTI-CNR Italy Welsh School of Architecture Cardiff University United Kingdom

ISBN: (纸本)9791256690329

The environmental approach to urban security acknowledges the importance that spatial and social dimensions of urban environments have for both real and perceived security, emphasizing the need to understand how urban spaces influence human behaviour. Space Syntax offers insights into human behavioural patterns in space by examining the spatial configuration of places and identifying individuals' movement patterns, representing an asset to uncovering underlying spatial dynamics in criminal behaviour, both at the urban and micro-urban scale. The association between Space Syntax and crime patterns is not new, however additional steps should be taken to associate it to environmental criminological theories, which deconstruct the components that contribute to crime occurrence. Considering this, the research delves into the existing correlations between crime distributions and intrinsic spatial properties derived from Space Syntax, using the historic centre of Pisa, Italy, as a case study. Two objectives are set: (1) identifying a comprehensive set of significant measures to explore crime phenomena through a discussion of previous research and an examination of the logic of criminology theories;and (2) to develop and discuss a methodology for linking Space Syntax metrics with crime distribution patterns, and assess correlations between space and crimes, thus identifying risky areas within the urban context. The direction and the strength of correlations between certain type of crime and specific attributes of the environment suggests the existence of a "Spatial Crime Impedance" property: a measure of natural resistance that the urban environment, considering only its spatial configuration and physical layout, opposes to criminal activities. © 14th International Space Syntax Symposium, SSS 2024.

关键词： Syntactics

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