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On Estimating the Average Response Time of High-Performance computing Environments 25th

On Estimating the Average Response Time of High-Performanc...

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25th international conference on Distributed and Computer and Communication Networks, DCCN 2022

作者： Gorbunova, A.V. Vishnevsky, V.M. V.A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences 65 Profsoyuznaya Street Moscow117997 Russia

ISBN: (纸本)9783031232060

Parallel and distributed computing have become widespread as one of the main ways to improve the performance of computing environments when processing big data. The issue of assessing the main performance indicators of such systems remains relevant to the present time due to the complexity of such analysis. A fork-join queuing system is well suited for modeling parallel data processing. The article explores the possibility of estimating the average response time of the system using an analytical expression for the upper bound of the maximum order statistics. Two options are described, depending on the amount of knowledge available about the operation of a high-performance environment. The results of the numerical experiment confirm the efficiency of the approach for a fairly wide range of parameters, one of the main advantages of which is the ease of use. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： data handling

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Enhancing Gender Prediction Model Performance through Automatic Individual Entity Extraction and Class Balance

Enhancing Gender Prediction Model Performance through Automa...

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2025 IEEE international conference on big data and Smart computing, bigComp 2025

作者： Kim, Chaeyun Kim, Eunseo Kim, Yeonhee Sim, Jaehyeong Kim, Jongkil Ewha Womans University Artificial Intelligence Convergence Seoul Korea Republic of Ewha Womans University Department of Computer Science and Engineering Seoul Korea Republic of Ewha Womans University Department of Cyber Security Seoul Korea Republic of

To build a model that predicts the gender of named entities in text, a high-quality labeled dataset is required, which requires considerable manual effort and time. This paper proposes two major contributions to address this issue. First, we develop a mechanism to automatically extract individual entities from sentences using Named Entity Recognition (NER) and Part-of-Speech (PoS) tagging techniques. This approach automates the data generation process and reduces costs. Second, we ensure class balance in the dataset to optimize model performance. Experimental results demonstrate that the automated data generation method and balanced dataset significantly enhance the performance of the gender prediction model. This work makes a substantial contribution to data generation and the improvement of model performance in Natural Language Processing (NLP) tasks. © 2025 IEEE.

关键词： Natural language processing systems

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ReMax: A Simple, Effective, and Efficient Reinforcement Learning Method for Aligning Large Language Models 41

ReMax: A Simple, Effective, and Efficient Reinforcement Lear...

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41st international conference on Machine Learning, ICML 2024

作者： Li, Ziniu Xu, Tian Zhang, Yushun Lin, Zhihang Yu, Yang Sun, Ruoyu Luo, Zhi-Quan School of Data Science The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data China National Key Laboratory for Novel Software Technology Nanjing University China Polixir.ai China China Shenzhen International Center For Industrial and Applied Mathematics China

Reinforcement Learning from Human Feedback (RLHF) is key to aligning Large Language Models (LLMs), typically paired with the Proximal Policy Optimization (PPO) algorithm. While PPO is a powerful method designed for general reinforcement learning tasks, it is overly sophisticated for LLMs, leading to laborious hyper-parameter tuning and significant computation burdens. To make RLHF efficient, we present ReMax, which leverages 3 properties of RLHF: fast simulation, deterministic transitions, and trajectory-level rewards. These properties are not exploited in PPO, making it less suitable for RLHF. Building on the renowned REINFORCE algorithm, ReMax does not require training an additional value model as in PPO and is further enhanced with a new variance reduction technique. ReMax offers several benefits over PPO: it is simpler to implement, eliminates more than 4 hyper-parameters in PPO, reduces GPU memory usage, and shortens training time. ReMax can save about 46% GPU memory than PPO when training a 7B model and enables training on A800-80GB GPUs without the memory-saving offloading technique needed by PPO. Applying ReMax to a Mistral-7B model resulted in a 94.78% win rate on the AlpacaEval leaderboard and a 7.739 score on MT-bench, setting a new SOTA for open-source 7B models. These results show the effectiveness of ReMax while addressing the limitations of PPO in LLMs. Copyright 2024 by the author(s)

关键词： Reinforcement learning

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Vision Transformers as Probabilistic Expansion from Learngene 41

Vision Transformers as Probabilistic Expansion from Learngen...

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41st international conference on Machine Learning, ICML 2024

作者： Wang, Qiufeng Yang, Xu Chen, Haokun Geng, Xin School of Computer Science and Engineering Southeast University Nanjing210096 China Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Ministry of Education China

Deep learning has advanced through the combination of large datasets and computational power, leading to the development of extensive pretrained models like Vision Transformers (ViTs). However, these models often assume a one-size-fits-all utility, lacking the ability to initialize models with elastic scales tailored to the resource constraints of specific downstream tasks. To address these issues, we propose Probabilistic Expansion from LearnGene (PEG) for mixture sampling and elastic initialization of Vision Transformers. Specifically, PEG utilizes a probabilistic mixture approach to sample Multi-Head Self-Attention layers and Feed-Forward Networks from a large ancestry model into a more compact part termed as learngene. Theoretically, we demonstrate that these learngene can approximate the parameter distribution of the original ancestry model, thereby preserving its significant knowledge. Next, PEG expands the sampled learngene through non-linear mapping, enabling the initialization of descendant models with elastic scales to suit various resource constraints. Our extensive experiments demonstrate the effectiveness of PEG and outperforming traditional initialization strategies. Copyright 2024 by the author(s)

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Impacts of Darwinian Evolution on Pre-Trained Deep Neural Networks

Impacts of Darwinian Evolution on Pre-Trained Deep Neural Ne...

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2024 IEEE international conference on Systems, Man, and Cybernetics, SMC 2024

作者： Du, Guodong Jiang, Runhua Yang, Senqiao Li, Haoyang Chen, Wei Li, Keren Goh, Sim Kuan Tang, Ho-Kin China School of Electrical Engineering and Artificial Intelligence Xiamen University Malaysia Malaysia Center for Quantum Computing Peng Cheng Laboratory China

ISBN: (纸本)9781665410205

Darwinian evolution of the biological brain is documented through multiple lines of evidence, although the modes of evolutionary changes remain unclear. Drawing inspiration from the evolved neural systems (e.g., visual cortex), deep learning models have demonstrated superior performance in visual tasks, among others. While the success of training deep neural networks has been relying on back-propagation (BP) and its variants to learn representations from data, BP does not incorporate the evolutionary processes that govern biological neural systems. This work proposes a neural network optimization framework based on evolutionary theory. Specifically, BP-trained deep neural networks for visual recognition tasks obtained from the ending epochs are considered the primordial ancestors (initial population). Subsequently, the population evolved with differential evolution. Extensive experiments are carried out to examine the relationships between Darwinian evolution and neural network optimization, including the correspondence between datasets, environment, models, and living species. The empirical results show that the proposed framework has positive impacts on the network, with reduced over-fitting and an order of magnitude lower time complexity compared to BP. Moreover, the experiments show that the proposed framework performs well on deep neural networks and big datasets. © 2024 IEEE.

关键词： Biotic

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Sentiment Analysis of Chinese Comments on OTA Website using BERT and LSTM

Sentiment Analysis of Chinese Comments on OTA Website using ...

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2022 international conference on Cloud computing, Internet of Things, and Computer Applications, CICA 2022

作者： Shen, Juan Xu, Mei School of Big Data and Artificial Intelligence AnHui XinHua University HeFei230088 China

ISBN: (纸本)9781510656673

Under the background of "Internet + tourism", many OTA sites have large numbers of users and have accumulated huge amounts of user review information. In this paper, web crawler is used to grab Chinese comment data on OTA website for pre-processing. BERT pre-training model is used to represent text feature vectors at sentence level. The results are then input into LSTM model to obtain high-level semantic features. Thus, the emotional tendency of the comments can be classified. The comparison of experimental data shows that BERT-LSTM model has higher classification accuracy, recall rate and F1 value. It shows that the effectiveness of text sentiment analysis has been improved. An emotional analysis of these emotional commentary texts is conducive to the monitoring of management departments, providing reference for the government and enterprises to make decisions. And this research has very high application value for promoting the development of tourism. © 2022 SPIE.

关键词： Sentiment analysis

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data Exploration Optimization for Medical big data 11th

Data Exploration Optimization for Medical Big Data

引用

11th international conference on Health Information Science, HIS 2022

作者： Ding, Shuang Mao, Chenyu Zheng, Wenkui Xiao, Qi Wu, Yitao School of Software Henan University Kaifeng475004 China Henan International Joint Laboratory of Intelligent Network Theory and Key Technology Kaifeng475004 China Henan University Kaifeng475004 China International College Beijing University of Posts and Telecommunications Beijing100876 China

ISBN: (纸本)9783031206269

In medical domain, huge amounts of data are generated at all times. data exploration is very important to help physicians or medical researchers to find the required datasets. However, the storage and computation of large-scale data surpass the performance limits of traditional relational databases, which are prone to performance bottlenecks, and it is difficult to expand the storage capacity and computational power. To solve the above problems, this paper studies the distributed migration storage and computing methods of medical data, and proposes a distributed computing and storage strategy of medical data to achieve efficient medical data exploration. We use the open source tools to migrate the MIMIC-IV medical database and optimize the sepsis data exploration. The experimental results show that compared with the traditional single node method, our optimization method based on Hive-ORC and its indexes, partition table reduces the storage space by 85% and the query time by 86%. This mechanism has higher efficiency in data management in the medical field. In addition, with the increase of data volume and cluster nodes, the strategy can achieve better optimization results. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： big data

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Artificial intelligence Based Rural E-Commerce Boosting Using big data 5

Artificial Intelligence Based Rural E-Commerce Boosting Usin...

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5th international conference on Contemporary computing and Informatics, IC3I 2022

作者： Roy, Saumi Salve, Archana Ravindra Shah, Jaymin Arvind Kadam, Sanjeev Muda, Iskandar Dash, Manoranjan Horizon College of Engineering Department of Management Studies New Bengaluru India MBA Faculty Indira College of Engineering and Management Maharashtra Pune India Amity University Amity Business School Mumbai India Pune India Universitas Sumatera Utara Faculty Economic and Business Department of Doctoral Program Jl. Prof TM Hanafiah 12 Medan20222 Indonesia USU Campus Padang bulan Medan20155 Indonesia Siksha O Anusandhan Deemed to Be University Ghatikia Kalinga Nagar Odisha Bhubaneswar India

ISBN: (纸本)9798350398267

Following 40 years of reform and opening up, India's economy has achieved a new peak, with rural ecommerce emerging as a key driver of this expansion. As the state's policy continues to develop to encourage rural ecommerce, the sector has opened up a big historical opportunity, with the size of the market growing and the sector's social awareness rising rapidly. With the widespread use of big data networks, big data-driven, AI-powered e-commerce has also grown rapidly in recent years. The use of electronic commerce is more common in urban regions than in rural ones. As part of the plan for 'revitalizing the countryside,' widespread use of ecommerce in rural regions will be a major step toward bolstering the rural economy. The growth of this route has the potential to strongly encourage the modernization of antiquated production techniques in rural regions while also improving the accessibility and efficiency of the flow of agricultural goods inside India and so facilitating their eventual export. The third-party distribution model was used as the logistics approach, and agricultural items from Mysore City, such as paddy and vegetables, served as the primary research objects to illustrate the utility of the Novel Colony Algorithm in the context of rural e-commerce. The issue of the third-party distribution model is examined through the process of model building. In light of this, PyCharm is used to determine the shortest path and total cost using a novel colony algorithm. In this research, we evaluate the models and conduct analyses of the efficiency and cost issues that arise when changing the parameters. © 2022 IEEE.

关键词： big data

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Organs-At-Risk Segmentation in Medical Imaging Based on the U-Net with Residual and Attention Mechanisms 23

Organs-At-Risk Segmentation in Medical Imaging Based on the ...

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23rd IEEE international conference on High Performance computing and Communications, 7th IEEE international conference on data Science and Systems, 19th IEEE international conference on Smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and big data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Lin, Xiao-Wei Li, Ni Qi, Qi School of Computer Science and Technology Hainan University Haikou China School of Mathematics and Statistics Hainan Normal University Hainan Normal University Key Laboratory of Data Science and Intelligence Education of Ministry of Education Haikou China

ISBN: (纸本)9781665494571

In this paper, we proposed a new organs-at-risk segmentation method for Medical images of heads and necks based on the U-Net with residuals and attention mechanism. A Project & Excite (PE) block combined with a residual method is proposed as Residual-PE Block in the U-Net encoding stage in order to enhance the feature expression ability. Attention Gates (AGs) are introduced in the decoding stage to increase the relevance of feature weighting in accord with segmentation tasks. The proposed method has been evaluated through a set of real world medical images including 22 organs at risk in the head and neck. Experimental results showed that our proposed method improved the average segmentation accuracy by 3-4% compared with current existing methods. The average Dice Similarity Coefficient (DSC) score on the segmentation of the 22 types of head-and-neck related organs at risk is 79.5% and the average 95% Hausdorff Distance (HD) is 6.99mm. It demonstrates that this approach is promising for clinical doctors to improve their accuracy and efficiency in radiotherapy. © 2021 IEEE.

关键词： Image segmentation Head Three-dimensional displays Smart cities Medical services Logic gates Encoding

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Revisiting Pseudo-Label for Single-Positive Multi-Label Learning 40

Revisiting Pseudo-Label for Single-Positive Multi-Label Lear...

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40th international conference on Machine Learning

作者： Liu, Biao Xu, Ning Lv, Jiaqi Geng, Xin Southeast Univ Sch Comp Sci & Engn Nanjing 210096 Peoples R China RIKEN Ctr Adv Intelligence Project Tokyo 1030027 Japan

To deal with the challenge of high cost of annotating all relevant labels for each example in multilabel learning, single-positive multi-label learning (SPMLL) has been studied in recent years, where each example is annotated with only one positive label. By adopting pseudo-label generation, i.e., assigning pseudo-label to each example by various strategies, existing methods have empirically validated that SPMLL would significantly reduce the amount of supervision with a tolerable damage in classification performance. However, there is no existing method that can provide a theoretical guarantee for learning from pseudo-label on SPMLL. In this paper, the conditions of the effectiveness of learning from pseudo-label for SPMLL are shown and the learnability of pseudo-label-based methods is proven. Furthermore, based on the theoretical guarantee of pseudo-label for SPMLL, we propose a novel SPMLL method named MIME, i.e., Mutual label enhancement for sIngle-positive Multi-label lEarning and prove that the generated pseudo-label by MIME approximately converges to the fully-supervised case. Experiments on four image datasets and five MLL datasets show the effectiveness of our methods over several existing SPMLL approaches.

关键词： Machine learning

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