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Monte Carlo Simulation-Based Robust Workflow Scheduling for Spot Instances in Cloud Environments

Tsinghua science and Technology 2024年第1期29卷 112-126页

作者： Quanwang Wu Jianzhao Fang Jie Zeng Junhao Wen Fengji Luo College of Computer Science Chongqing UniversityChongqing 400044China National Experimental Teaching Demonstration Center Chongqing UniversityChongqing 400044China College of Big Data and Software Engineering Chongqing UniversityChongqing 400044China School of Civil Engineering The University of SydneySydney 2006Australia

When deploying workflows in cloud environments,the use of Spot Instances(SIs)is intriguing as they are much cheaper than on-demand ***,Sls are volatile and may be revoked at any time,which results in a more challenging scheduling problem involving execution interruption and hence hinders the successful handling of conventional cloud workflow scheduling *** some scheduling methods for Sls have been proposed,most of them are no more applicable to the latest Sls,as they have evolved by eliminating bidding and simplifying the pricing *** study focuses on how to minimize the execution cost with a deadline constraint when deploying a workflow on volatile Sls in cloud *** on Monte Carlo simulation and list scheduling,a stochastic scheduling method called MCLS is devised to optimize a utility function introduced for this *** the Monte Carlo simulation framework,MCLS employs sampled task execution time to build solutions via deadline distribution and list scheduling,and then returns the most robust solution from all the candidates with a specific evaluation mechanism and selection *** results show that the performance of MCLS is more competitive comparedwithtraditionalalgorithms.

关键词： constrained optimization Monte Carlo simulation robustness Spot Instances(Sls) workflow scheduling

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TreeVI: Reparameterizable Tree-structured Variational Inference for Instance-level Correlation Capturing 38

TreeVI: Reparameterizable Tree-structured Variational Infere...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Xiao, Junxi Su, Qinliang School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China

Mean-field variational inference (VI) is computationally scalable, but its highly-demanding independence requirement hinders it from being applied to wider scenarios. Although many VI methods that take correlation into account have been proposed, these methods generally are not scalable enough to capture the correlation among data instances, which often arises in applications involving graphs or explicit constraints among instances. In this paper, we developed the Tree-structured Variational Inference (TreeVI)2, which uses a tree to capture the correlation among latent variables in the posterior. We show that samples from the tree-structured posterior can be reparameterized efficiently and parallelly, making its training cost just 2 or 3 times that of VI under the mean-field assumption. To capture correlation with more complicated structure, the TreeVI is further extended to the multiple-tree case. Furthermore, we show that the underlying tree structure can be automatically learned from training data. With experiments on synthetic datasets, constrained clustering, user matching and link prediction, we demonstrate the efficacy of TreeVI in capturing instance-level correlation in posteriors and enhancing the performance of downstream applications. © 2024 Neural information processing systems foundation. All rights reserved.

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Research on Multi-Label Classification of Chinese Text Based on Word-Label Probability and DistilBERT 2

Research on Multi-Label Classification of Chinese Text Based...

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2nd IEEE International Conference on Image Processing and computer Applications, ICIPCA 2024

作者： Zhao, Hong Tian, Weiquan An, Ding Mi, Shan School of Computer Science and Communication Lanzhou University of Technology Lanzhou China Cccc Big Data Technology Co. Ltd. Lanzhou China

ISBN: (纸本)9798350360240

Multi-label text classification is a fundamental task in the field of natural language processing. Currently, there are issues in the Chinese multi-label text classification tasks, such as insufficient extraction of text label features and a lack of learning about the correlations between labels. To address these challenges, we proposed a Multi-label Text Classification Model based on DistilBERT and Word-Label Probabilities (DBWL). The model consists of a metric space module that integrates word-label probabilities and a deep neural network module based on DistilBERT. Firstly, the trained Labeled Latent Dirichlet Allocation (Labeled-LDA) model is utilized to obtain the probabilities of words associated with labels, which are then used to calculate the text mapping vectors and map them into the same metric space as the labels. Similarity measurement is employed to extract features between text-label pairs and label-label pairs. Then, in the deep neural network module, DistilBERT is employed to jointly embed texts and labels, capturing rich semantic information about text labels, followed by feature extraction using TextCNN. Finally, the features extracted from both modules are weighted and fused for label prediction. Experimental results on two benchmark datasets demonstrate that the proposed model outperforms current mainstream multi-label classification models in terms of key performance metrics. © 2024 IEEE.

关键词： Semantics

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Estimation of Facial Emotion Based on Landmark Points by Applying Artificial Intelligence and Machine Learning 7

Estimation of Facial Emotion Based on Landmark Points by App...

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2023 7th International Conference On Computing, Communication, Control And Automation, ICCUBEA 2023

作者： Osman Abdalraheem, Mohammed Hassan Hossain, Mohammad Alamgir Ahmed Hamdan, Alfadil Kechadi, M. Tahar Limker, Suresh Jazan University Department of Information Technology Saudi Arabia Jazan University Department of Computer Science Saudi Arabia Sudan University of Science and Technology Department of Computer Science Sudan School of Computer Science University College Dublin Dublin Ireland Aissms Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India

ISBN: (纸本)9798350304268

In the science of emotion processing, it is hard to figure out how to predict an emotion from naturalistic facial expressions in a multidimensional space. While many studies and works have been done to develop methods for making accurate predictions, relatively little has been done to put the uniqueness of the human face to use in reading and responding to people's expressions of emotion. This work proposes an emotional image retrieval strategy for multimodal emotion detection. This strategy is based on the fact that face features are always there and that the human face has regional redundancy that is independent of face features. Our method uses landmark points to get rid of unnecessary information about facial expressions and focus on the most attractive ones in each frame. It's possible that by making use of the landmarks, we'll be able to predict facial emotions more precisely. This is especially helpful for long-term emotion prediction since it allows us to recover time and space representations by introducing an emotional face. The preliminary results based on a benchmark dataset show that the proposed method exceeds its competitors in terms of prediction accuracy. © 2023 IEEE.

关键词： emotion prediction facial expression landmark point LBP method

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LMDTA: Molecular Pre-trained and Interaction Fine-tuned Attention Neural Network for Drug-Target Affinity Prediction

LMDTA: Molecular Pre-trained and Interaction Fine-tuned Atte...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Hu, Minjie Yang, Kuo Xu, Kuan Zhou, Xuezhong Beijing Jiaotong University Institute of Medical Intelligence Beijing Key Lab of Traffic Data Analysis and Mining School of Computer Science & Technology Beijing China

ISBN: (纸本)9798350386226

Accurately predicting drug-target binding affinity is crucial for advancing drug discovery. Recent molecular pre-training models and biological large models provide general molecular representations, but effectively leveraging these features for specific tasks like Drug-Target Affinity (DTA) prediction remains challenging. To address this, we propose LMDTA, a novel attention-based neural network combining molecular pre-training with interaction fine-tuning for affinity prediction. LMDTA learns complex drug and protein representations through pre-trained models, while fine-tuning an interaction-specific model for the DTA task. The two-side attention mechanism integrates pre-trained and fine-tuned features, capturing key drug-target interactions. Experiments on benchmark datasets show LMDTA achieves state-of-the-art performance, with ablation studies validating the model's design. © 2024 IEEE.

关键词： Molecular docking

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Hierarchical Context Interaction and Reasoning with Transformer for Emotion Recognition

Hierarchical Context Interaction and Reasoning with Transfor...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Wang, Wenxuan Wang, Chenglei Wang, Xiaomei Shen, Xuli Xu, Qing Qian, Xuelin School of Computer Science Northwestern Polytechnical University Xi'an China UniDT Co. Ltd. Shanghai China School of Data Science Fudan University Shanghai China School of Automation Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9798350368741

Emotion recognition is an important task in computer vision. However, current approaches using hard associations (e.g., element-wise addition or concatenation) suffer from information pollution. To overcome these challenges and utilize information at different scales, we present a novel Transformer-based emotion recognition (TransEmo) framework utilizing only the RGB modality as input. TransEmo is a hierarchical feature fusion framework with soft-association to effectively realize contextual interaction and emotion reasoning. It comprises a top-down path and a bottom-up path with cross-attention layers. The top-down path enriches the features of the current hierarchy gradually by using information from a larger receptive field and interacts effectively with contexts to suppress redundant information and noise. The bottom-up path introduces a set of learnable prototypes to adaptively capture clues for various emotions by reasoning with hierarchical *** proposed approach achieves state-of-the-art performance on two benchmarks and provides an analysis to discuss the advantages of our modules. © 2025 IEEE.

关键词： context reasoning emotion recognition hierarchical interaction transformer

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An accurate cell segmentation method for high-resolution complex brain image 9

An accurate cell segmentation method for high-resolution com...

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9th International Conference on Image, Vision and Computing, ICIVC 2024

作者： Xu, Xiaofeng Li, Shuai Zheng, Wei Henan Engineering Research Center of Intelligent Processing for Big Data of Digital Image School of Computer Science and Technology Nanyang Normal University Nanyang China

ISBN: (纸本)9798350385991

Cell segmentation is a key step in medical image analysis and cell biology research, and is important in areas such as understanding cell behaviour, cancer detection and cell cycle analysis. This task is challenging due to the presence of overlap between cells and image noise. With the development of imaging techniques, the amount and quality of acquired cellular image data have increased significantly and even contain diverse information such as neuronal structures and blood vessels, which puts higher demands on segmentation techniques. Real rat brain cell images obtained using micro-optical slice tomography (MOST) contain complex and diverse information such as neuronal structure, cell morphology, and the distribution of blood vessels, demonstrating the characteristics of real biological samples, for which segmentation of the target region is more difficult. In order to solve these challenges, this paper proposes a new cell image segmentation method that combines watershed order differential feature extraction and fuzzy entropy spatial theory. Due to the complexity of image information, we first use traditional image enhancement techniques. Then feature extraction is performed and fuzzy entropy processing is added to achieve accurate identification of cell boundaries. Then distance transform and curvature analysis are added to further segment the cell clusters into individual cells while avoiding over-segmentation and missed detection. Experiments demonstrate that the method not only shows high performance on our dataset, but also validates our method on several publicly available cellular datasets respectively, proving that the segmentation algorithm shows great potential for segmentation on different types of cells. Future research will further optimise the model to improve segmentation performance on complex and irregularly shaped cells and explore the application of these methods to other types of medical image segmentation tasks. © 2024 IEEE.

关键词： Image segmentation

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Multi-Objective Mixed Bus Fleet Charging Schedule Problem with Time-of-Use for Real-world data-sets 32

Multi-Objective Mixed Bus Fleet Charging Schedule Problem wi...

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32nd Irish Conference on Artificial Intelligence and Cognitive science, AICS 2024

作者： Jarvis, Padraigh Climent, Laura Arbelaez, Alejandro Insight Centre for Data Analytics School of Computer Science & IT University College Cork Ireland Universitat Politècnica de València Valencia Spain

As the effects of climate change are increasingly felt worldwide, the transition to Electric Buses (EB) presents an opportunity to decarbonize the transportation sector. Several issues exist that hinder the adoption of a green bus fleet. Such as the increased cost, reduced travel distances, and required recharge times, which may negatively impact service quality. This work proposes a Mixed Integer Programming model to solve a multi-objective mixed fleet charging schedule problem. The mixed fleet considers EBs and Internal Combustion Engine Buses (ICEBs) and minimizes daily costs, such as fuel price, the Social Cost of Carbon (SCC) produced by the bus fleet, and the Value of Time (VoT) of public transport users. Non-linear charging is considered as well as alternative approaches along with Time of Use (TOU) constraints for electricity price and SCC. Empirical evaluation shows that significant savings can be made, with reductions of over 20000 in fuel costs, and reductions of over 100 tCO2eq per day. Consideration of VoT minimizes negative customer impact, limiting late arrivals to an average of 8.78 seconds per EB. The inclusion of non-linear charging makes minimal positive impact compared to limiting the total capacity of the battery, and while the inclusion of TOU constraints correlates to more savings, the amount saved is minute. © 2024 Copyright for this paper by its authors.

关键词： Integer programming

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Permeability Model for Fractal Porous Media with Rough Capillary Walls

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International Journal of High Speed Electronics and Systems 2025年

作者： Zhao, Yibo School of Data and Computer Science Shandong Women's University Jinan China

The pore space of a porous medium is typically considered to consist of a collection of curved capillaries with rough walls. A model describing roughness is established by applying fractal theory. As a result, a modified permeability model is derived theoretically, which is closely related to roughness. To validate this model, a new form of Kozeny-Carman (KC) equation is also developed for estimating permeability on a set of benchmark rock samples, and a good agreement was achieved in terms of small relative error between measured and predicted permeabilities. Therefore, we can conclude that pore roughness should not be ignored in predicting permeability. Although the improved KC equation can predict permeability effectively too, it is not practical as the KC constant is difficult to determine. © 2025 The Author(s).

关键词： Fractal dimension

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Hot or Cold? Adaptive Temperature Sampling for Code Generation with Large Language Models 38

Hot or Cold? Adaptive Temperature Sampling for Code Generati...

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38th AAAI Conference on Artificial Intelligence, AAAI 2024

作者： Zhu, Yuqi Li, Jia Li, Ge Zhao, YunFei Jin, Zhi Mei, Hong Academy of Military Sciences China Ministry of Education School of Computer Science Peking University Beijing China Advanced Institute of Big Data Beijing China

ISBN: (纸本)1577358872

Recently, Large Language Models (LLMs) have shown impressive abilities in code generation. However, existing LLMs' decoding strategies are designed for Natural Language (NL) generation, overlooking the differences between NL and programming languages (PL). Due to this oversight, a better decoding strategy for code generation remains an open question. In this paper, we conduct the first systematic study to explore a decoding strategy specialized in code generation. With an analysis of loss distributions of code tokens, we find that code tokens can be divided into two categories: challenging tokens that are difficult to predict and confident tokens that can be easily inferred. Among them, the challenging tokens mainly appear at the beginning of a code block. Inspired by the above findings, we propose a simple yet effective method: Adaptive Temperature (AdapT) sampling, which dynamically adjusts the temperature coefficient when decoding different tokens. We apply a larger temperature when sampling for challenging tokens, allowing LLMs to explore diverse choices. We employ a smaller temperature for confident tokens avoiding the influence of tail randomness noises. We apply AdapT sampling to LLMs with different sizes and conduct evaluations on two popular datasets. Results show that AdapT sampling significantly outperforms state-of-the-art decoding strategy. Copyright © 2024, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Temperature

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