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Optimal Transport with a Diversified Memory Bank for Cross-Domain Speaker Verification 48

Optimal Transport with a Diversified Memory Bank for Cross-D...

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48th IEEE International conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Zhang, Ruiteng Wei, Jianguo Lu, Xugang Lu, Wenhuan Jin, Di Zhang, Lin Xu, Junhai Tianjin University College of Intelligence and Computing Tianjin China Qinghai Nationalities University Computer College Xining China National Institute of Information and Communications Technology Kyoto Japan National Institute of Informatics Tokyo Japan

ISBN: (纸本)9781728163277

Optimal transport (OT) can be applied to cross-domain adaptation in speaker verification (SV) by converting speakers' probability distributions from source to target domains. However, in scenarios involving over-massive categories (speakers) or difficult samples in discrimination, OT often has difficulty computing effective transports. To address this challenge, we propose an OT-based unsupervised domain adaptation (UDA) framework for SV, OT with a diversified memory bank, called DMB-OT, which ensures the accuracy of transfers by two strategies: (1) It regularizes the solution space of OT, which attempts to plan transformations between audio samples from the same speaker with high confidence;(2) it integrates a dynamic curriculum learning algorithm, preventing OT from calculating transport couplings based on hard-discriminative samples in the early stage of UDA. Experiments under different target domains showed that our unsupervised DMB-OT could significantly improve the performance of OT-based UDA and could even match the performance of the supervised PLDA-based adaptation. © 2023 IEEE.

关键词： diversified memory bank domain adaptation optimal transport self-paced learning Speaker verification

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performance Measurement on Heterogeneous Processors with PAPI

Performance Measurement on Heterogeneous Processors with PAP...

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high performance Computing, Networking, Storage and Analysis, SC-W: Workshops of the International conference for

作者： Willow E. Cunningham Vincent M. Weaver Electrical and Computer Engineering University of Maine Orono Maine USA

ISBN: (数字)9798350355543

ISBN: (纸本)9798350355550

Modern computer processors improve their computing power by having multiple cores. Traditionally these cores were homogeneous: many identical cores with the same capabilities. Instead it is possible to create processors that have heterogeneous (or hybrid) cores, where the various cores have differing capabilities. This can lead to energy savings and other efficiencies, but complicates performance analysis. Heterogeneous cores have been common for years in embedded ARM processors; recently support has appeared in x86 desktop processors as well. It is likely that before long server and high-performance systems will also gain hybrid *** look at current Linux support for heterogeneous processors and detail the various problems encountered when adding support for them to the PAPI performance measurement library.

关键词： Measurement program processors Linux high performance computing conferences Energy conservation Libraries performance analysis Servers

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A Multi-Level Platform-Independent GPU API for high-Level programming Models 37th

A Multi-Level Platform-Independent GPU API for High-Level Pr...

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37th International Supercomputing conference on high performance Computing (ISC high performance Computing)

作者： Hayashi, Akihiro Paul, Sri Raj Sarkar, Vivek Georgia Inst Technol Atlanta GA 30332 USA Intel Corp Austin TX USA

ISBN: (纸本)9783031232190;9783031232206

While there has been a growing interest in supporting accelerators, especially GPU accelerators, in large-scale systems, the user typically has to work with low-level GPU programming models such as CUDA along with the low-level message passing interface (MPI). We believe higher-level programming models such as Partitioned Global Address Space (PGAS) programming models enable productive parallel programming at both the intra-node and inter-node levels in homogeneous and heterogeneous nodes. However, GPU programming with PGAS languages in practice is still limited since there is still a big performance gap between compiler-generated GPU code and hand-tuned GPU code;hand-optimization of CPU-GPU data transfers is also an important contributor to this performance gap. Thus, it is not rare that the user eventually writes a fully external GPU program that includes the host part -i.e., GPU memory (de)allocation, host-device/device-host data transfer, and the device part - i.e., GPU kernels, and calls it from their primary language, which is not very productive. Our key observation is that the complexity of writing the external GPU program comes not only from writing GPU kernels in the device part, but also from writing the host part. In particular, interfacing objects in the primary language to raw C/C++ pointers is tedious and error-prone, especially because high-level languages usually have a well-defined type system with type inference. In this paper, we introduce the GPUAPI module, which offers multiple abstraction levels of low-level GPU API routines for high-level programming models with a special focus on PGAS languages, which allows the user to choose an appropriate abstraction level depending on their tuning scenarios. The module is also designed to work with multiple standard low-level GPU programming models: CUDA, HIP, DPC++, and SYCL, thereby significantly improving productivity and portability. We use Chapel as the primary example and our preliminary performanc

关键词： GPUs Chapel PGAS languages Distributed programming model GPU API library

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Towards Optimized Hydrological Forecast Prediction of WRF-Hydro on GPU 25

Towards Optimized Hydrological Forecast Prediction of WRF-Hy...

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25th IEEE International conferences on high performance Computing and Communications, 9th International conference on Data Science and Systems, 21st IEEE International conference on Smart City and 9th IEEE International conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Liu, Hongrui Lei, Kelun Yang, Hailong Luan, Zhongzhi Qian, Depei School of Computer Science and Engineering Beihang University Beijing China

ISBN: (纸本)9798350330014

Hydrological models are designed to simulate and predict the dynamic cycling of surface and groundwater. These models represent simplifications of complex hydrological pro-cesses and play an integral role in hydrological research and real-world applications. The WRF Hydro model is widely used due to its multi-scale capabilities for land-atmosphere modeling. Recent studies have focused on enhancing the performance of different modules in WRF through GPU parallel optimization techniques. However, WRF - Hydro has not been able to utilize the GPU for computations which leads to lower computational efficiency when simulating hydrological calculations over large regions for extended periods. In this paper, we first analyze the computational and communication bottlenecks in WRF Hydro. We then propose several methods for performance optimization accordingly. To address the computational bottleneck, we develop efficient GPU-based overland routing and channel routing, eliminating redundant calculations. Additionally, we adopt an improved block partitioning strategy to enhance memory access efficiency on the GPU. To tackle the communicational bottleneck, we adopt a packetized communication strategy to reduce the number of communication operation calls in channel routing and simplify the preprocessing operations for communication. Meanwhile, for overland routing, we employ multi-stream to minimize redundant synchronization operations and overlap communication with computation to reduce communication overhead. The evaluation results demonstrate that WRF -Hydro can achieve significant speedups in several simulation cases after applying our proposed optimizations, resulting in a maximum speedup of 6.67 ×. © 2023 IEEE.

关键词： Graphics processing unit

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Enhancing Rumor Detection Methods with Propagation Structure Infused Language Model 31

Enhancing Rumor Detection Methods with Propagation Structure...

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31st International conference on Computational Linguistics, COLING 2025

作者： Cui, Chaoqun Li, Siyuan Ma, Kunkun Jia, Caiyan School of Computer Science and Technology & Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing100044 China

ISBN: (纸本)9798891761964

Pretrained Language Models (PLMs) have excelled in various Natural Language Processing tasks, benefiting from large-scale pretraining and self-attention mechanism's ability to capture long-range dependencies. However, their performance on social media application tasks like rumor detection remains suboptimal. We attribute this to mismatches between pretraining corpora and social texts, inadequate handling of unique social symbols, and pretraining tasks ill-suited for modeling user engagements implicit in propagation structures. To address these issues, we propose a continue pretraining strategy called Post Engagement Prediction (PEP) to infuse information from propagation structures into PLMs. PEP makes models to predict root, branch, and parent relations between posts, capturing interactions of stance and sentiment crucial for rumor detection. We also curate and release large-scale Twitter corpus: TwitterCorpus (269GB text), and two unlabeled claim conversation datasets with propagation structures (UTwitter and UWeibo). Utilizing these resources and PEP strategy, we train a Twitter-tailored PLM called SoLM. Extensive experiments demonstrate PEP significantly boosts rumor detection performance across universal and social media PLMs, even in few-shot scenarios. On benchmark datasets, PEP enhances baseline models by 1.0-3.7% accuracy, even enabling it to outperform current state-of-the-art methods on multiple datasets. SoLM alone, without high-level modules, also achieves competitive results, highlighting the strategy's effectiveness in learning discriminative post interaction features. © 2025 Association for Computational Linguistics.

关键词： Tweets

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Method and system of power load classification and decomposition based on deep learning 4

Method and system of power load classification and decomposi...

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4th International conference on Smart Grid and Energy Engineering, SGEE 2023

作者： Sun, Hao Yuan, Hongtai Geng, Yudong Chen, Jing Zhang, Chuanfu Li, Wen Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350359619

This paper introduces a system for the classification and decomposition of electrical loads, addressing the shortcomings of current non-intrusive load decomposition methods that are prone to errors. We propose a method that first classifies electrical loads and then performs decomposition, along with the corresponding system workflow. Initially, we collect household electricity consumption data using a proprietary load collection device developed by the company, constructing an electrical load dataset. Subsequently, we train classification and decomposition methods using this dataset, conducting comparative experiments with various methods to select the most effective one. Based on the trained electrical load classification and decomposition models, we develop an electrical load inference program. The key implementation lies in applying the electrical load classification and decomposition inference program to edge devices. Leveraging the cloud server hosting a backend web service for the mobile app, we interact with users through the app, achieving precise decomposition and monitoring of electrical loads. Integrating edge computing and cloud computing, coupled with a mobile app serving as the interaction platform, this paper presents a high-precision, simplified, and highly scalable solution for electrical load analysis and management. In experimental comparisons, we find that using the Gated Recurrent Unit (GRU) method from deep learning and the Sequence-to-Point Neural Network (Seq2point) method outperform other methods in classification and decomposition, significantly improving the accuracy of electrical load classification. Therefore, we select the GRU method and Seq2Point method as the core techniques for the electrical load classification and decomposition system. This system not only demonstrates significant advantages in performance but also possesses high accuracy, model simplification, and scalability, providing an efficient and reliable solution for elec

关键词： Edge computing

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Accelerating Big Data Application by Eliminating Redundancy on Hadoop Cluster 29

Accelerating Big Data Application by Eliminating Redundancy ...

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29th IEEE International conference on Parallel and Distributed Systems, ICPADS 2023

作者： Lei, Kelun Du, Shaokang You, Xin Xuan, Zhibo Kong, Haoran Yang, Hailong Shang, Jing Xiao, Zhiwen Wu, Zhihui Luan, Zhongzhi Qian, Depei Beihang University School of Computer Science and Engineering Beijing China China Mobile Information Technology Center China

ISBN: (纸本)9798350330717

Big data applications are widely adopted to mine valuable information from a tremendous amount of industry data, which is commonly represented as a series of map-reduce operations. Among various map-reduce frameworks, Hadoop is most commonly adopted for data processing at large scale. Although Hadoop eases the development of highly scalable distributed big data applications, inefficient implementation due to poor coding practice and deep software abstractions can cause severe performance issues such as unreasonable slowdown, high response latency, and waste of computing resources, which can lead to unsatisfactory serving delay or significant maintenance cost. In this paper, we first categorize three common types of redundant patterns in big data applications. Then we propose a tool-assisted optimization workflow to detect the redundant patterns automatically, which profiles the application by sampling hardware performance monitoring units. Moreover, we present a profiling visualization method that can help to pinpoint the redundant codes. Based on these approaches, we optimize several big data applications by eliminating redundancies, yielding up to 14.8% performance improvement. © 2023 IEEE.

关键词： Hadoop performance Optimization Redundant Patterns

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Aegis: Post-Training Attribute Unlearning in Federated Recommender Systems against Attribute Inference Attacks 25

Aegis: Post-Training Attribute Unlearning in Federated Recom...

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34th ACM Web conference, WWW 2025

作者： Wu, Wenhan Jiang, Jiawei Hu, Chuang School of Computer Science Wuhan University Wuhan China State Key Laboratory of Internet of Things for Smart City University of Macau China

ISBN: (纸本)9798400712746

As privacy concerns in recommender systems become increasingly prominent, federated recommender systems (FedRecs) have emerged as a promising distributed training paradigm. FedRecs enable the collaborative training of a shared global recommendation model without requiring the exchange of raw client interaction data. However, models trained using standard FedRec methods remain vulnerable to personal information leakage, particularly through attribute inference attacks, which can expose sensitive user attributes such as gender and race. In this paper, we address these user-sensitive attributes as targets for federated unlearning. To protect users’ sensitive information, attribute unlearning aims to eliminate sensitive attributes from user embeddings, thereby preventing inference attacks while preserving recommendation performance. We introduce a novel post-training federated unlearning framework, Aegis, which performs unlearning based on private attribute requests after the model has been trained, minimizing the degradation in recommendation accuracy. Aegis employs an information-theoretic multi-component loss function to balance privacy protection and recommendation performance. Additionally, Aegis adapts to scenarios where training interaction data may be unavailable, reflecting real-world centralized protection scenarios. Comprehensive evaluations of various benchmark datasets demonstrate that our proposed method effectively safeguards user privacy while maintaining high-quality recommendations. © 2025 Copyright held by the owner/author(s).

关键词： Recommender systems

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A Smote-Based Churn Prediction System Using Machine Learning Techniques

A Smote-Based Churn Prediction System Using Machine Learning...

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2023 International conference on Science, Engineering and Business for Sustainable Development Goals, SEB-SDG 2023

作者： Akinrotimi, Akinyemi Omololu Ogundokun, Roseline Oluwaseun Mabayoje, Modinat Abolore Oyekunle, Rafiat Ajibade Adebiyi, Marion Olubunmi Augustine University Department of Mathematics Sciences Computer Science Program Ilara-Epe Epe Nigeria Kaunas University of Technology Department of Multimedia Engineering Lithuania Landmark University Omu Aran Sdg 4. Quality Education Nigeria University of Ilorin Department of Computer Science Ilorin Nigeria University of Ilorin Department of Information and Communication Ilorin Nigeria Landmark University Omu-Aran Department of Computer Science Omu Aran Nigeria

ISBN: (纸本)9798350324785

The financial backbone of every telecommunications company is strictly made up of the number of customers patronizing the organization. Due to the high level of competition amongst existing telecommunication companies, customers sometimes leave, dropping the services of a particular one for the other. Predicting which subscribers may want to leave a telecommunications company and providing solutions to keep them from doing so, are the main objectives of customer churn prediction. Churn prediction helps classify consumers who are likely to move from one business to another. The ability to foresee churners before they leave has been increasingly useful in recent years, especially in light of increased competition among communications carriers. To compute churners for telecommunications providers, the study described in this paper used oversampling techniques to balance churn data and applied the dimensionality reduction technique to discover optimal features with a strong predictive ability for detecting would-be churners and non-churners. The model used Logistic Regression and the Naïve Bayes Classification Algorithm to implement comparative classification strategies. The performance levels of these strategies were compared using a Telecommunications Customer Churn data set, as well as performance metrics such as accuracy, sensitivity, and specificity. The Naïve Bayes Algorithm proves more efficient than its counterpart giving higher positive rate detection and a lower negative rate detection. © 2023 IEEE.

关键词： Forecasting

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Characterize and Optimize Dense Linear Solver on Multi-core CPUs 29

Characterize and Optimize Dense Linear Solver on Multi-core ...

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29th IEEE International conference on Parallel and Distributed Systems, ICPADS 2023

作者： Fu, Xiao Su, Xing Dong, Dezun Yang, Weiling National University of Defense Technology College of Computer Changsha China

ISBN: (纸本)9798350330717

The dense linear solver is an essential subroutine in high-performance computing. Typical parallel implementations either adopt the fork-join or task parallel programming models. Blocked algorithms built upon the fork-join paradigm focus on optimizing cache locality, leaving significant synchronization overhead. Following the data-driven execution model, tile-based algorithms formed on the task parallel paradigm effectively relieve the pain and exhibit superior load balancing. Nevertheless, they introduce redundant memory access expenses, plaguing the CPU execution. In this paper, we first characterize and quantify the impact of the performance bottlenecks in-depth and then propose a series of optimizations. Specifically, we reduce the idle time of threads by merging LU factorization with the subsequent lower triangular solver to improve parallelism. Moreover, we eliminate tile-based matrix format transformation and diminish duplicated data packing operations to lower memory access overhead. performance evaluation is conducted on two modern multi-core systems, Intel Xeon Gold(R) 6252N and HiSilicon Kunpeng 920. The evaluation results demonstrate the superiority of our proposed solver over state-of-the-art open-source implementations, achieving performance gains of up to 11.5% and 12.2% on the respective platforms. © 2023 IEEE.

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