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Статті в журналах з теми "Robust aggregation"

Автор: Grafiati

Опубліковано: 7 червня 2025

Оновлено: 2 серпня 2025

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1

Arieli, Itai, Yakov Babichenko, and Rann Smorodinsky. "Robust forecast aggregation." Proceedings of the National Academy of Sciences 115, no. 52 (2018): E12135—E12143. http://dx.doi.org/10.1073/pnas.1813934115.

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Bayesian experts who are exposed to different evidence often make contradictory probabilistic forecasts. An aggregator, ignorant of the underlying model, uses this to calculate his or her own forecast. We use the notions of scoring rules and regret to propose a natural way to evaluate an aggregation scheme. We focus on a binary state space and construct low regret aggregation schemes whenever there are only two experts that either are Blackwell-ordered or receive conditionally independent and identically distributed (i.i.d.) signals. In contrast, if there are many experts with conditionally i.

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2

Cross, Daniel, Jaime Ramos, Barbara Mellers, Philip E. Tetlock, and David W. Scott. "Robust forecast aggregation: FourierL2Eregression." Journal of Forecasting 37, no. 3 (2017): 259–68. http://dx.doi.org/10.1002/for.2489.

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3

Allison-Bunnell, Jodi. "Finding Aid Aggregation: Toward a Robust Future." American Archivist 85, no. 2 (2022): 556–86. http://dx.doi.org/10.17723/2327-9702-85.2.556.

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ABSTRACT Over the last twenty-five years, cultural heritage professionals have formed aggregations—of finding aids, digital object metadata, or related forms of description—in order to overcome barriers to creating and presenting structured, consistent, and interoperable description and to enable expanded access. Now most of these aggregators are struggling to update their infrastructure, meet user needs for access to archival collections, and engage with some of the most promising conceptual, technical, and structural advances in the field. In 2018–2019, the “Toward a National Archival Findin

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4

Pillutla, Krishna, Sham M. Kakade, and Zaid Harchaoui. "Robust Aggregation for Federated Learning." IEEE Transactions on Signal Processing 70 (2022): 1142–54. http://dx.doi.org/10.1109/tsp.2022.3153135.

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5

Liu, Fei, Shaokang Qi, Shibin Wang, Xu Tian, Liantao Liu, and Xue Zhao. "Robust Trading Decision-Making Model for Demand-Side Resource Aggregators Considering Multi-Objective Cluster Aggregation Optimization." Energies 18, no. 2 (2025): 236. https://doi.org/10.3390/en18020236.

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In the context of a high proportion of new energy grid connections, demand-side resources have become an inevitable choice for constructing new power systems due to their high flexibility and fast response speed. However, the response capability of demand-side resources is decentralized and fluctuating, which makes it difficult for them to effectively participate in power market trading. Therefore, this paper proposes a robust transaction decision model for demand-side resource aggregators considering multi-objective clustering aggregation optimization. First, a demand-side resource aggregatio

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6

Labraoui, Nabila, Mourad Gueroui, Makhlouf Aliouat, and Jonathan Petit. "RAHIM: Robust Adaptive Approach Based on Hierarchical Monitoring Providing Trust Aggregation for Wireless Sensor Networks." JUCS - Journal of Universal Computer Science 17, no. (11) (2011): 1550–71. https://doi.org/10.3217/jucs-017-11-1550.

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In-network data aggregation has a great impact on the energy consumption in large-scale wireless sensor networks. However, the resource constraints and vulnerable deployment environments challenge the application of this technique in terms of security and efficiency. A compromised node may forge arbitrary aggregation value and mislead the base station into trusting a false reading. In this paper, we present RAHIM, a reactive defense to secure data aggregation scheme in cluster-based wireless sensor networks. The proposed scheme is based on a novel application of adaptive hierarchical level of

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7

Yiming Chen, Yiming Chen, Hongjun Duan Yiming Chen, Dong Wang Hongjun Duan, Yining Liu Dong Wang, and Chin-Chen Chang Yining Liu. "SERDA: Secure Enhanced and Robust Data Aggregation Scheme for Smart Grid." 電腦學刊 35, no. 5 (2024): 073–90. http://dx.doi.org/10.53106/199115992024103505006.

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<p>Data aggregation is considered a viable security and privacy solution for smart grid as it allows to obtain the total electricity consumption within a region without disclosing individual data. However, existing data aggregation schemes give little consideration in their threat models to use cases where devices operate in untrustworthy environments and adversaries have physical system access, which is common in the smart grid. They cannot support authentication and resist physical attacks while maintaining data privacy and supporting fault tolerance for smart meter (SM) failures. Moti

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8

WU, Zhong-Bo, Chong-Sheng ZHANG, Hong CHEN, and Hang QIN. "Robust Aggregation Algorithm in Sensor Networks." Journal of Software 20, no. 7 (2010): 1885–94. http://dx.doi.org/10.3724/sp.j.1001.2009.03272.

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9

Du, Lei, Yan Pan, and Xiaonan Luo. "Robust Spectral Clustering via Matrix Aggregation." IEEE Access 6 (2018): 53661–70. http://dx.doi.org/10.1109/access.2018.2871030.

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10

Guan, Yuyao, Tingting Sun, Jun Ding, and Zhigang Xie. "Robust organic nanoparticles for noninvasive long-term fluorescence imaging." Journal of Materials Chemistry B 7, no. 44 (2019): 6879–89. http://dx.doi.org/10.1039/c9tb01905g.

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11

Zhang, Changlun, Chao Li, and Jian Zhang. "A Secure Privacy-Preserving Data Aggregation Model in Wearable Wireless Sensor Networks." Journal of Electrical and Computer Engineering 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/104286.

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With the rapid development and widespread use of wearable wireless sensors, data aggregation technique becomes one of the most important research areas. However, the sensitive data collected by sensor nodes may be leaked at the intermediate aggregator nodes. So, privacy preservation is becoming an increasingly important issue in security data aggregation. In this paper, we propose a security privacy-preserving data aggregation model, which adopts a mixed data aggregation structure. Data integrity is verified both at cluster head and at base station. Some nodes adopt slicing technology to avoid

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12

S., Divya* Mr. D. Duraikumar Prof VP. Manikandan. "DETECTION OF CLONE NODE USING CHORD ALGORITHM FOR SECURE DATA TRANSMISSION AND PREVENTION OF COLLUSION ATTACKS IN WSN." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 6 (2016): 244–47. https://doi.org/10.5281/zenodo.54786.

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At present due to limitation and computing power and energy resources of sensor node the data is aggregated by extremely simple algorithm such as averaging data from multiple sensors is aggregated at aggregation node which is done at base station only the aggregate values. Such algorithm is very vulnerable to false and more important malicious attack since WSNR usually un attend without tamper resistance network they are highly susceptible to such attacks. This cannot be remedied by cryptographic methods, because the attackers generally gain complete access to information stored in the comprom

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13

Cheng, Shih-Fen, John Tajan, and Hoong Chuin Lau. "Robust distributed scheduling via time-period aggregation." Web Intelligence and Agent Systems: An International Journal 10, no. 3 (2012): 305–18. http://dx.doi.org/10.3233/wia-2012-0248.

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14

Sarkar, Chandrima, Sarah Cooley, and Jaideep Srivastava. "Robust Feature Selection Technique Using Rank Aggregation." Applied Artificial Intelligence 28, no. 3 (2014): 243–57. http://dx.doi.org/10.1080/08839514.2014.883903.

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15

Pasdar, Abbas, Changxin Liu, and Nicola Bastianello. "Robust Federated Learning with Multi-Step Aggregation." IFAC-PapersOnLine 59, no. 4 (2025): 181–86. https://doi.org/10.1016/j.ifacol.2025.07.065.

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16

Rasouli, Samira, Kerstin Dautenhahn, and Chrystopher L. Nehaniv. "Simulation of a Bio-Inspired Flocking-Based Aggregation Behaviour in Swarm Robotics." Biomimetics 9, no. 11 (2024): 668. http://dx.doi.org/10.3390/biomimetics9110668.

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This paper presents a biologically inspired flocking-based aggregation behaviour of a swarm of mobile robots. Aggregation behaviour is essential to many swarm systems, such as swarm robotics systems, in order to accomplish complex tasks that are impossible for a single agent. In this work, we developed a robot controller using Reynolds’ flocking rules to coordinate the movements of multiple e-puck robots during the aggregation process. To improve aggregation behaviour among these robots and address the scalability issues in current flocking-based aggregation approaches, we proposed using a K-m

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17

Zhou, Antong, Ning Jiang, and Tong Tang. "Asynchronous Robust Aggregation Method with Privacy Protection for IoV Federated Learning." World Electric Vehicle Journal 15, no. 1 (2024): 18. http://dx.doi.org/10.3390/wevj15010018.

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Due to the wide connection range and open communication environment of internet of vehicle (IoV) devices, they are susceptible to Byzantine attacks and privacy inference attacks, resulting in security and privacy issues in IoV federated learning. Therefore, there is an urgent need to study IoV federated learning methods with privacy protection. However, the heterogeneity and resource limitations of IoV devices pose significant challenges to the aggregation of federated learning model parameters. Therefore, this paper proposes an asynchronous robust aggregation method with privacy protection fo

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18

Nath, Suman, Phillip B. Gibbons, Srinivasan Seshan, and Zachary Anderson. "Synopsis diffusion for robust aggregation in sensor networks." ACM Transactions on Sensor Networks 4, no. 2 (2008): 1–40. http://dx.doi.org/10.1145/1340771.1340773.

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19

Zhu, Tengteng, Zehua Guo, Chao Yao, et al. "Byzantine-robust Federated Learning via Cosine Similarity Aggregation." Computer Networks 254 (December 2024): 110730. http://dx.doi.org/10.1016/j.comnet.2024.110730.

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20

Considine, Jeffrey, Marios Hadjieleftheriou, Feifei Li, John Byers, and George Kollios. "Robust approximate aggregation in sensor data management systems." ACM Transactions on Database Systems 34, no. 1 (2009): 1–35. http://dx.doi.org/10.1145/1508857.1508863.

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21

Kim, Kwang In. "Robust Distributed Gradient Aggregation Using Projections onto Gradient Manifolds." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 12 (2024): 13151–59. http://dx.doi.org/10.1609/aaai.v38i12.29214.

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We study the distributed gradient aggregation problem where individual clients contribute to learning a central model by sharing parameter gradients constructed from local losses. However, errors in some gradients, caused by low-quality data or adversaries, can degrade the learning process when naively combined. Existing robust gradient aggregation approaches assume that local data represent the global data-generating distribution, which may not always apply to heterogeneous (non-i.i.d.) client data. We propose a new algorithm that can robustly aggregate gradients from potentially heterogeneou

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22

Imran, Muhammad, Muhammad Salah Ud Din, Muhammad Atif Ur Rehman, and Byung-Seo Kim. "MIA-NDN: Microservice-Centric Interest Aggregation in Named Data Networking." Sensors 23, no. 3 (2023): 1411. http://dx.doi.org/10.3390/s23031411.

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The named data networking (NDN)-based microservice-centric in-network computation poses various challenges in terms of interest aggregation and pending interest table (PIT) lifetime management. A same-named microservice-centric interest packet may have a different number of input parameters with nonidentical input values. In addition, the same-named interest packet with the same number of parameters may have different corresponding parameter values. The vanilla NDN request aggregation (based on the interest name, while ignoring the input parameters count and/or their corresponding values) may

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23

Hipp, John R. "Block, Tract, and Levels of Aggregation: Neighborhood Structure and Crime and Disorder as a Case in Point." American Sociological Review 72, no. 5 (2007): 659–80. http://dx.doi.org/10.1177/000312240707200501.

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This article highlights the importance of considering the proper level of aggregation when estimating neighborhood effects. Using a unique nonrural subsample from a large national survey (the American Housing Survey) at three time points that allows placing respondents in blocks and census tracts, this study tests the appropriate level of aggregation of the structural characteristics hypothesized to affect block-level perceptions of crime and disorder. I find that structural characteristics differ in their effects based on the level of aggregation employed. While the effects of racial/ethnic h

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24

Vijay Kumar, K., S. Sravanthi, Syed Shujauddin Sameer, and K. Anil Kumar. "Effective Data Aggregation Moel for the Healthcare Data Transmission and Security in Wireless Sensor Network Environment." Journal of Sensors, IoT & Health Sciences 1, no. 1 (2023): 40–50. http://dx.doi.org/10.69996/jsihs.2023004.

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Data aggregation is the process of collecting and combining information from multiple sources to provide a unified view or summary. In various fields such as statistics, economics, and data analysis, aggregating data helps reveal patterns, trends, or general insights that may not be apparent when examining individual data points. Aggregated data can provide a more comprehensive perspective, facilitating decision-making and strategic planning. This paper explores the application of Sugeno Fuzzy Model Monkey Swarm Optimization (SFMMsO) in healthcare, specifically focusing on data aggregation and

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25

Leski, Jacek M. "Robust nonlinear aggregation operator for ECG powerline interference reduction." Biomedical Signal Processing and Control 69 (August 2021): 102675. http://dx.doi.org/10.1016/j.bspc.2021.102675.

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26

Kulathumani, Vinod, Anish Arora, Mukundan Sridharan, Kenneth Parker, and Masahiro Nakagawa. "Census: fast, scalable and robust data aggregation in MANETs." Wireless Networks 24, no. 6 (2017): 2017–34. http://dx.doi.org/10.1007/s11276-017-1452-y.

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27

Cormode, Graham, Srikanta Tirthapura, and Bojian Xu. "Time-decaying Sketches for Robust Aggregation of Sensor Data." SIAM Journal on Computing 39, no. 4 (2010): 1309–39. http://dx.doi.org/10.1137/08071795x.

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28

Zhou, Xin, Yohei Murakami, Toru Ishida, Xuanzhe Liu, and Gang Huang. "ARM: Toward Adaptive and Robust Model for Reputation Aggregation." IEEE Transactions on Automation Science and Engineering 17, no. 1 (2020): 88–99. http://dx.doi.org/10.1109/tase.2019.2902407.

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29

Chassein, André, and Marc Goerigk. "On scenario aggregation to approximate robust combinatorial optimization problems." Optimization Letters 12, no. 7 (2017): 1523–33. http://dx.doi.org/10.1007/s11590-017-1206-x.

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30

Conti, Mauro, Lei Zhang, Sankardas Roy, Roberto Di Pietro, Sushil Jajodia, and Luigi Vincenzo Mancini. "Privacy-preserving robust data aggregation in wireless sensor networks." Security and Communication Networks 2, no. 2 (2009): 195–213. http://dx.doi.org/10.1002/sec.95.

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31

Yang, Caiyi, and Javad Ghaderi. "Byzantine-Robust Decentralized Learning via Remove-then-Clip Aggregation." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 19 (2024): 21735–43. http://dx.doi.org/10.1609/aaai.v38i19.30173.

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We consider decentralized learning over a network of workers with heterogeneous datasets, in the presence of Byzantine workers. Byzantine workers may transmit arbitrary or malicious values to neighboring workers, leading to degradation in overall performance. The heterogeneous nature of the training data across various workers complicates the identification and mitigation of Byzantine workers. To address this complex problem, we introduce a resilient decentralized learning approach that combines the gradient descent algorithm with a novel robust aggregator. Specifically, we propose a remove-th

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32

Liu, Yuting, Hongyu Yang, and Qijun Zhao. "Hierarchical Feature Aggregation from Body Parts for Misalignment Robust Person Re-Identification." Applied Sciences 9, no. 11 (2019): 2255. http://dx.doi.org/10.3390/app9112255.

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In this work, we focus on the misalignment problem in person re-identification. Human body parts commonly contain discriminative local representations relevant with identity recognition. However, the representations are easily affected by misalignment that is due to varying poses or poorly detected bounding boxes. We thus present a two-branch Deep Joint Learning (DJL) network, where the local branch generates misalignment robust representations by pooling the features around the body parts, while the global branch generates representations from a holistic view. A Hierarchical Feature Aggregati

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33

Franses, Philip Hans. "Marketing response and temporal aggregation." Journal of Marketing Analytics 9, no. 2 (2021): 111–17. http://dx.doi.org/10.1057/s41270-020-00102-7.

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AbstractThis paper deals with inferring key parameters on marketing response at a true high frequency while data are partly or fully available only at a lower frequency aggregate levels. The familiar Koyck model turns out to be very useful for this purpose. Assuming this model for the high-frequency data makes it possible to infer the high-frequency parameters from modified Koyck type models when lower frequency data are available. This means that inference using the Koyck model is robust to temporal aggregation.

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34

Baghdadi, Majid, Farzaneh Shemirani, and Hamid Reza Lotfi Zadeh Zhad. "Determination of cobalt in high-salinity reverse osmosis concentrates using flame atomic absorption spectrometry after cold-induced aggregation microextraction." Analytical Methods 8, no. 8 (2016): 1908–13. http://dx.doi.org/10.1039/c5ay02989a.

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35

Liu, Chong, and Yu-Xiang Wang. "Doubly Robust Crowdsourcing." Journal of Artificial Intelligence Research 73 (January 12, 2022): 209–29. http://dx.doi.org/10.1613/jair.1.13304.

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Large-scale labeled dataset is the indispensable fuel that ignites the AI revolution as we see today. Most such datasets are constructed using crowdsourcing services such as Amazon Mechanical Turk which provides noisy labels from non-experts at a fair price. The sheer size of such datasets mandates that it is only feasible to collect a few labels per data point. We formulate the problem of test-time label aggregation as a statistical estimation problem of inferring the expected voting score. By imitating workers with supervised learners and using them in a doubly robust estimation framework, w

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36

Jana, Chiranjibe, and Madhumangal Pal. "A Robust Single-Valued Neutrosophic Soft Aggregation Operators in Multi-Criteria Decision Making." Symmetry 11, no. 1 (2019): 110. http://dx.doi.org/10.3390/sym11010110.

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Molodtsov originated soft set theory that was provided a general mathematical framework for handling with uncertainties in which we meet the data by affix parameterized factor during the information analysis as differentiated to fuzzy as well as neutrosophic set theory. The main object of this paper is to lay a foundation for providing a new approach of single-valued neutrosophic soft tool which is considering many problems that contain uncertainties. In present study, a new aggregation operators of single-valued neutrosophic soft numbers have so far not yet been applied for ranking of the alt

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37

Brogan, Alex P. S. "Preparation and application of solvent-free liquid proteins with enhanced thermal and anhydrous stabilities." New Journal of Chemistry 45, no. 15 (2021): 6577–85. http://dx.doi.org/10.1039/d1nj00467k.

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38

Chen, Xin, and Na Li. "Leveraging Two-Stage Adaptive Robust Optimization for Power Flexibility Aggregation." IEEE Transactions on Smart Grid 12, no. 5 (2021): 3954–65. http://dx.doi.org/10.1109/tsg.2021.3068341.

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39

Li, Yawei, Hong Zhang, Yujie Wu, and Ding Yuan. "Dynamic Scene Video Deblurring Using Robust Incremental Weighted Fourier Aggregation." IEEE Signal Processing Letters 28 (2021): 1565–69. http://dx.doi.org/10.1109/lsp.2021.3097239.

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40

Andrieu, Pierre, Bryan Brancotte, Laurent Bulteau, et al. "Efficient, robust and effective rank aggregation for massive biological datasets." Future Generation Computer Systems 124 (November 2021): 406–21. http://dx.doi.org/10.1016/j.future.2021.06.013.

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41

Lupitskyy, Robert, and Sergiy Minko. "Robust synthesis of nanogel particles by an aggregation-crosslinking method." Soft Matter 6, no. 18 (2010): 4396. http://dx.doi.org/10.1039/c0sm00306a.

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42

Chen, Ju, Jun Feng, Shenyu Zhang, Xiaodong Li, and Hamza Djigal. "Robust annotation aggregation in crowdsourcing via enhanced worker ability modeling." Information Processing & Management 62, no. 1 (2025): 103914. http://dx.doi.org/10.1016/j.ipm.2024.103914.

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43

Sun, Wenmei, and Yuan Zheng. "CGCANet: Context-Guided Cost Aggregation Network for Robust Stereo Matching." Computing and Informatics 43, no. 2 (2024): 505–28. http://dx.doi.org/10.31577/cai_2024_2_505.

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44

Kolde, Raivo, Sven Laur, Priit Adler, and Jaak Vilo. "Robust rank aggregation for gene list integration and meta-analysis." Bioinformatics 28, no. 4 (2012): 573–80. http://dx.doi.org/10.1093/bioinformatics/btr709.

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45

Albardan, Mahmoud, John Klein, and Olivier Colot. "SPOCC: Scalable POssibilistic Classifier Combination - toward robust aggregation of classifiers." Expert Systems with Applications 150 (July 2020): 113332. http://dx.doi.org/10.1016/j.eswa.2020.113332.

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46

Li, Yanli, Weiping Ding, Huaming Chen, Wei Bao, and Dong Yuan. "Contribution-wise Byzantine-robust aggregation for Class-Balanced Federated Learning." Information Sciences 667 (May 2024): 120475. http://dx.doi.org/10.1016/j.ins.2024.120475.

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47

Geng, Gangchao, Tianyang Cai, and Zheng Yang. "Better Safe Than Sorry: Constructing Byzantine-Robust Federated Learning with Synthesized Trust." Electronics 12, no. 13 (2023): 2926. http://dx.doi.org/10.3390/electronics12132926.

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Byzantine-robust federated learning empowers the central server to acquire high-end global models amidst a restrictive set of malicious clients. The general idea of existing learning methods requires the central server to statistically analyze all local parameter (gradient or weight) updates, and to delete suspicious ones. The drawback of these approaches is that they lack a root of trust that would allow us to identify which local parameter updates are suspicious, which means that malicious clients can still disrupt the global model. The machine learning community has recently proposed a new

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48

Siddhanta, Soumik, Ishan Barman, and Chandrabhas Narayana. "Revealing the trehalose mediated inhibition of protein aggregation through lysozyme–silver nanoparticle interaction." Soft Matter 11, no. 37 (2015): 7241–49. http://dx.doi.org/10.1039/c5sm01896j.

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49

Fatès, Nazim, and Nikolaos Vlassopoulos. "A Robust Scheme for Aggregating Quasi-Blind Robots in an Active Environment." International Journal of Swarm Intelligence Research 3, no. 3 (2012): 66–80. http://dx.doi.org/10.4018/jsir.2012070105.

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Анотація:

The question of how to aggregate autonomous agents with limited abilities in the absence of centralized coordination is known as the Decentralized Gathering Problem. The authors present a bio-inspired aggregation scheme that solves this problem and study a first application of this scheme to a small team of robots. The robots (Alice and Khepera III) obey simple rules and have only a rudimentary perception of their environment. The collective behavior is based on stigmergic principles and uses an active environment to relay the communications between robots. This results in an aggregation proce

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50

Kaur, Manpreet, and Shweta Mishra. "A smart grid data privacy-preserving aggregation approach with authentication." Scientific Temper 15, no. 04 (2024): 3214–24. https://doi.org/10.58414/scientifictemper.2024.15.4.31.

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Анотація:

Authentication of smart grid privacy-preserving aggregation addresses two of the key privacy and security issues of the smart grids: user data confidentiality and grid node communication safety. The proposed study elaborates on a new approach to data aggregation with authentication in smart grid systems for the safe and efficient exchange of information. The proposed solution would apply techniques, such as homomorphic encryption along with advanced cryptographic techniques, to calculate encrypted data without leaking sensitive information. Data and device integrity are more likely to be maint

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