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8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Dominic, Nicholas Cenggoro, Tjeng Wawan Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Predicting the best-quality of rice phenotypes is the priority among agricultural researchers to fulfill worldwide food security. Trend development of predictive models from statistics to machine learning is the subject of this review. Gathered from the Google Scholar database, 14 appropriate papers (2016-2020) related to the rice phenotypes prediction were selected through title and abstract content filtering. The outputs show that Support Vector Machine, Multi-layer Perceptron, and regression are the most used models, while yield is the priority prediction point besides tiller, panicle, and 1000-grain weight of rice. However, finding the accurate predictor is invariably challenging due to distinct rice varieties in the world and high confounding factors. Thus, developing an advanced deep learning model that accommodates these needs is worth considering further. © 2023 The Authors. Published by Elsevier B.V.

关键词： machine learning rice phenotypes prediction statistics

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On Error-Based Step Size Control for Discontinuous Galerkin Methods for Compressible Fluid Dynamics

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应用数学与计算数学学报 2025年第1期7卷 3-39页

作者： Hendrik Ranocha Andrew R.Winters Hugo Guillermo Castro Lisandro Dalcin Michael Schlottke-Lakemper Gregor J.Gassner Matteo Parsani Applied Mathematics University of HamburgBundesstr.5520146 HamburgGermany Department of Mathematics Applied Mathematics Linköping UniversityLinköpingSweden Extreme Computing Research Center(ECRC) Computer Electrical and Mathematical Science and Engineering Division(CEMSE)King Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia Applied and Computational Mathematics RWTH Aachen UniversityAachenGermany High-Performance Computing Center Stuttgart University of StuttgartStuttgartGermany Department of Mathematics and Computer Science University of CologneCologneGermany Center for Data and Simulation Science University of CologneCologneGermany Extreme Computing Research Center(ECRC) Computer Electrical and Mathematical Science and Engineering Division(CEMSE)King Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia Physical Science and Engineering Division(PSE) King Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia

We study a temporal step size control of explicit Runge-Kutta(RK)methods for com-pressible computational fluid dynamics(CFD),including the Navier-Stokes equations and hyperbolic systems of conservation laws such as the Euler *** demonstrate that error-based approaches are convenient in a wide range of applications and compare them to more classical step size control based on a Courant-Friedrichs-Lewy(CFL)*** numerical examples show that the error-based step size control is easy to use,robust,and efficient,e.g.,for(initial)transient periods,complex geometries,nonlinear shock captur-ing approaches,and schemes that use nonlinear entropy *** demonstrate these properties for problems ranging from well-understood academic test cases to industrially relevant large-scale computations with two disjoint code bases,the open source Julia pack-ages *** with *** and the C/Fortran code SSDC based on PETSc.

关键词： Explicit Runge-Kutta(RK)methods Step size control Compressible fluid dynamics Adaptivity in space and time Shock capturing

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Ghost Module Based Residual Mixture of Self-Attention and Convolution for Online Signature Verification

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computers, Materials & Continua 2024年第4期79卷 695-712页

作者： Fangjun Luan Xuewen Mu Shuai Yuan Department of Computer Technology School of Computer Science and EngineeringShenyang Jianzhu UniversityShenyang110168China Liaoning Province Big Data Management and Analysis Laboratory of Urban Construction Shenyang Jianzhu UniversityShenyang110168China Shenyang Branch of National Special Computer Engineering Technology Research Center Shenyang Jianzhu UniversityShenyang110168China

Online Signature Verification (OSV), as a personal identification technology, is widely used in various ***, it faces challenges, such as incomplete feature extraction, low accuracy, and computational heaviness. Toaddress these issues, we propose a novel approach for online signature verification, using a one-dimensionalGhost-ACmix Residual Network (1D-ACGRNet), which is a Ghost-ACmix Residual Network that combines convolutionwith a self-attention mechanism and performs improvement by using Ghost method. The Ghost-ACmix Residualstructure is introduced to leverage both self-attention and convolution mechanisms for capturing global featureinformation and extracting local information, effectively complementing whole and local signature features andmitigating the problem of insufficient feature extraction. Then, the Ghost-based Convolution and Self-Attention(ACG) block is proposed to simplify the common parts between convolution and self-attention using the Ghostmodule and employ feature transformation to obtain intermediate features, thus reducing computational ***, feature selection is performed using the random forestmethod, and the data is dimensionally reducedusing Principal Component Analysis (PCA). Finally, tests are implemented on the MCYT-100 datasets and theSVC-2004 Task2 datasets, and the equal error rates (EERs) for small-sample training using five genuine andforged signatures are 3.07% and 4.17%, respectively. The EERs for training with ten genuine and forged signaturesare 0.91% and 2.12% on the respective datasets. The experimental results illustrate that the proposed approacheffectively enhances the accuracy of online signature verification.

关键词： Online signature verification feature selection ACG block ghost-ACmix residual structure

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Exploring Recurrent Neural Network Models for Depression Detection Through Facial Expressions: A Systematic Literature Review 6

Exploring Recurrent Neural Network Models for Depression Det...

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6th International Conference on computer and Informatics Engineering, IC2IE 2023

作者： Rumahorbo, Brilyan Nathanael Pardamean, Bens Elwirehardja, Gregorius Natanael Bina Nusantara University Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia

ISBN: (纸本)9798350345162

Major Depressive Disorder (MDD) is a prevalent mental disorder, affecting a significant number of individuals, with estimates reaching 300 million cases worldwide. Currently, the diagnosis of this condition relies heavily on subjective assessments based on the experience of medical professionals. Therefore, researchers have turned to deep learning models to explore the detection of depression. The objective of this review is to gather information on detecting depression based on facial expressions in videos using deep learning techniques. Overall, this research found that RNN models achieved 7.22 MAE for AVEC2014. LSTM models produced 4.83 MAE for DAIC-WOZ, while GRU models achieved an accuracy of 89.77% for DAIC-WOZ. Features like Facial Action Units (FAU), eye gaze, and landmarks show great potential and need to be further analyzed to improve results. Analysis can include applying feature engineering techniques. Aggregation methods, such as mean calculation, are recommended as effective approaches for data processing. This Systematic Literature Review found that facial expressions do have relevant patterns related to MDD. © 2023 IEEE.

关键词： deep learning facial expression GRU LSTM major depressive disorder

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SELF-JOINT SUPERVISED LEARNING 10

SELF-JOINT SUPERVISED LEARNING

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10th International Conference on Learning Representations, ICLR 2022

作者： Kardan, Navid Hill, Mitchell Shah, Mubarak Center for Research in Computer Vision Department of Computer Science Department of Statistics and Data Science University of Central Florida United States

Supervised learning is a fundamental framework used to train machine learning systems. A supervised learning problem is often formulated using an i.i.d. assumption that restricts model attention to a single relevant signal at a time when predicting. This contrasts with the human ability to actively use related samples as reference when making decisions. We hypothesize that the restriction to a single signal for each prediction in the standard i.i.d. framework contributes to well-known drawbacks of supervised learning: making overconfident predictions and vulnerability to overfitting, adversarial attacks, and out-of-distribution data. To address these limitations, we propose a new supervised learning paradigm called self-joint learning that generalizes the standard approach by modeling the joint conditional distribution of two observed samples, where each sample is an image and its label. Rather than assuming samples are independent, our models explicitly learn the sample-to-sample relation of conditional independence. Our framework can naturally incorporate auxiliary unlabeled data to further improve the performance. Experiments on benchmark image datasets show our method offers significant improvement over standard supervised learning in terms of accuracy, robustness against adversarial attacks, out-of-distribution detection, and overconfidence mitigation. © 2022 ICLR 2022 - 10th International Conference on Learning Representationss. All rights reserved.

关键词： Supervised learning

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PROVABLE SIM-TO-REAL TRANSFER IN CONTINUOUS DOMAIN WITH PARTIAL OBSERVATIONS 11

PROVABLE SIM-TO-REAL TRANSFER IN CONTINUOUS DOMAIN WITH PART...

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11th International Conference on Learning Representations, ICLR 2023

作者： Hu, Jiachen Zhong, Han Jin, Chi Wang, Liwei School of Computer Science Peking University China Center for Data Science Peking University China Department of Electrical and Computer Engineering Princeton University United States National Key Laboratory of General Artificial Intelligence School of Intelligence Science and Technology Peking University Center for Data Science Peking University Beijing Institute of Big Data Research China

Sim-to-real transfer, which trains RL agents in the simulated environments and then deploys them in the real world, has been widely used to overcome the limitations of gathering samples in the real world. Despite the empirical success of the sim-to-real transfer, its theoretical foundation is much less understood. In this paper, we study the sim-to-real transfer in continuous domain with partial observations, where the simulated environments and real-world environments are modeled by linear quadratic Gaussian (LQG) systems. We show that a popular robust adversarial training algorithm is capable of learning a policy from the simulated environment that is competitive to the optimal policy in the real-world environment. To achieve our results, we design a new algorithm for infinite-horizon average-cost LQGs and establish a regret bound that depends on the intrinsic complexity of the model class. Our algorithm crucially relies on a novel history clipping scheme, which might be of independent interest. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

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Multi-Task Learning Frameworks to Classify Food and Estimate Weight From a Single Image 20

Multi-Task Learning Frameworks to Classify Food and Estimate...

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20th International Joint Conference on computer science and Software Engineering, JCSSE 2023

作者： Siwathammarat, Pakin Jesadaporn, Panas Chawachat, Jakarin Chiang Mai University Data Science Research Center Faculty of Science Thailand Chiang Mai University Faculty of Medicine Department of Internal Medicine Thailand Faculty of Science Department of Computer Science Chiang Mai Thailand

ISBN: (纸本)9798350300505

Usually, elderly patients in hospitals suffer from malnutrition because they are unable to consume food as prescribed by doctors or nutritionists. Analyzing food intake is labor-intensive and time-consuming. Therefore, machine learning is used to analyze the food intake. Major food analysis tasks include food classification and food weight estimation. The basic machine learning approach to this problem is to combine a food classification model with a food weight estimate model sequentially. When we deployed it, we found that a large amount of memory and models were required. One solution is to use multi-task learning. In this study, we proposed multi-task learning frameworks that could recognize food and predict weight based on a single image. The performance of our frameworks was compared to the baseline models, which only utilized either regression or classification. Although baseline accuracy is higher, our framework has MAPE values that are lower than the baseline. To improve the performance, we explored different approaches for weighting loss, including manual weighting and auto weighting, using uncertainty and auxiliary tasks. From the experiment, our results showed that our multi-task learning framework that adjusted the weight of loss using auxiliary tasks outperformed the baseline models in terms of MAPE and Accuracy. Moreover, we demonstrate our framework when scaling up the backbone from ResNet50 to ResNet101 and ResNet152. © 2023 IEEE.

关键词： Image classification

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Seer: Accelerating Block chain Transaction Execution by Fine-Grained Branch Prediction 51st

Seer: Accelerating Block chain Transaction Execution by Fine...

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51st International Conference on Very Large data Bases, VLDB 2025

作者： Zhang, Shijie Cheng, Ru Liu, Xinpeng Xiao, Jiang Jin, Hai Li, Bo National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong

Increasingly popular decentralized applications (dApps) with complex application logic incur significant overhead for executing smart contract transactions, which greatly limits public block chain performance. Pre-executing transactions off the critical path can mitigate substantial I/O and computation costs during execution. However, pre-execution does not yield any state transitions, rendering the system state inconsistent with actual execution. This inconsistency can lead to deviations in pre-execution paths when processing smart contracts with multiple state-related branches, thus diminishing pre-execution effectiveness. In this paper, we develop Seer, a novel public block chain execution engine that incorporates fine-grained branch prediction to fully exploit pre-execution effectiveness. Seerpredicts state-related branches using a two-level prediction approach, reducing inconsistent execution paths more efficiently than executing all possible branches. To enable effective reuse of pre execution results, Seer employs checkpoint-based fast-path execution, enhancing transaction execution for both successful and unsuccessful predictions. Evaluations with realistic block chain workloads demonstrate that Seer delivers an average of 27.7× transaction-level speedup and an overall 20.6× speedup in the execution phase over vanilla Ethereum, outperforming existing block chain execution acceleration solutions. © 2025, VLDB Endowment, All rights reserved.

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Imitation Learning from Purified Demonstrations 41

Imitation Learning from Purified Demonstrations

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41st International Conference on Machine Learning, ICML 2024

作者： Wang, Yunke Dong, Minjing Zhao, Yukun Du, Bo Xu, Chang School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Wuhan Institute of Data Intelligence Wuhan University China Department of Computer Science City University of Hong Kong Hong Kong School of Computer Science Faculty of Engineering The University of Sydney Australia

Imitation learning has emerged as a promising approach for addressing sequential decision-making problems, with the assumption that expert demonstrations are optimal. However, in real-world scenarios, most demonstrations are often imperfect, leading to challenges in the effectiveness of imitation learning. While existing research has focused on optimizing with imperfect demonstrations, the training typically requires a certain proportion of optimal demonstrations to guarantee performance. To tackle these problems, we propose to purify the potential noises in imperfect demonstrations first, and subsequently conduct imitation learning from these purified demonstrations. Motivated by the success of diffusion model, we introduce a two-step purification via diffusion process. In the first step, we apply a forward diffusion process to smooth potential noises in imperfect demonstrations by introducing additional noise. Subsequently, a reverse generative process is utilized to recover the optimal demonstration from the diffused ones. We provide theoretical evidence supporting our approach, demonstrating that the distance between the purified and optimal demonstration can be bounded. Empirical results on MuJoCo and RoboSuite demonstrate the effectiveness of our method from different aspects. Copyright 2024 by the author(s)

关键词： Contrastive Learning

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CDA-GNN: A Chain-driven Accelerator for Efficient Asynchronous Graph Neural Network 24

CDA-GNN: A Chain-driven Accelerator for Efficient Asynchrono...

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61st ACM/IEEE Design Automation Conference, DAC 2024

作者： Yu, Hui Zhang, Yu He, Ligang He, Donghao Li, Qikun Zhao, Jin Liao, Xiaofei Jin, Hai Gu, Lin Liu, Haikun National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computer Science University of Warwick United Kingdom

ISBN: (纸本)9798400706011

Asynchronous Graph Neural Network (AGNN) has attracted much research attention because it enables faster convergence speed than the synchronous GNN. However, existing software/hardware solutions suffer from redundant computation overhead and excessive off-chip communications for AGNN due to irregular state propagations along the dependency chains between vertices. This paper proposes a chain-driven asynchronous accelerator, CDA-GNN, for efficient AGNN inference. Specifically, CDA-GNN proposes a chain-driven asynchronous execution approach into novel accelerator design to regularize the vertex state propagations for fewer redundant computations and off-chip communications and also designs a chain-aware data caching method to improve data locality for AGNN. We have implemented and evaluated CDA-GNN on a Xilinx Alveo U280 FPGA card. Compared with the cutting-edge software solutions (i.e., Dorylus and AMP) and hardware solutions (i.e., BlockGNN and FlowGNN), CDA-GNN improves the performance of AGNN inference by an average of 1,173x, 182.4x, 10.2x, and 7.9x and saves energy by 2,241x, 242.2x, 12.4x, and 8.9x, respectively. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Graph neural networks

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