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International Conference on data engineering

作者： Bo Peng Zhiyun Ren Srinivasan Parthasarathy Xia Ning Department of Computer Science and Engineering The Ohio State University Department of Biomedical Informatics The Ohio State University Department of Computer Science and Engineering Translational Data Analytics Institute The Ohio State University Department of Biomedical Informatics Department of Computer Science and Engineering Translational Data Analytics Institute The Ohio State University

Sequential recommendation aims to identify and recommend the next few items of users’ interest. It becomes an effective tool to help users select their favorite items from a variety of options. A key challenge in sequential recommendation is to learn the patterns and dynamics, which are most pertinent to inform future interactions of users. With the prosperity of deep learning, many deep models, particularly based on recurrent neural networks [1] and with attention mechanisms [2] , [3] , have been developed for sequential recommendation purposes. However, our analysis demonstrates that, these deep models, particularly those with attention mechanisms, may not always learn meaningful attention weights from the extremely sparse recommendation data, and thus, could degrade the recommendation performance. Therefore, in this study, instead of deep models, we develop novel, effective and efficient hybrid associations models (HAM) to better learn from the sparse and limited recommendation data. This study has been published in IEEE Transactions on Knowledge and data engineering. Please refer to the full manuscript [4] for more details.

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Differentially private worst-group risk minimization 24

Differentially private worst-group risk minimization

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Proceedings of the 41st International Conference on Machine Learning

作者： Xinyu Zhou Raef Bassily Department of Computer Science & Engineering The Ohio State University Department of Computer Science & Engineering and the Translational Data Analytics Institute (TDAI) The Ohio State University

We initiate a systematic study of worst-group risk minimization under (ε, δ)-differential privacy (DP). The goal is to privately find a model that approximately minimizes the maximal risk across p sub-populations (groups) with different distributions, where each group distribution is accessed via a sample oracle. We first present a new algorithm that achieves excess worst-group population risk of $\tilde{O}(\frac{p\sqrt{d}}{K\epsilon} + \sqrt{\frac{p}{K}})$, where K is the total number of samples drawn from all groups and d is the problem dimension. Our rate is nearly optimal when each distribution is observed via a fixed-size dataset of size K/p. Our result is based on a new stability-based analysis for the generalization error. In particular, we show that Δ-uniform argument stability implies $\tilde{O}(\Delta + \frac{1}{\sqrt{n}})$ generalization error w.r.t. the worst-group risk, where n is the number of samples drawn from each sample oracle. Next, we propose an algorithmic framework for worst-group population risk minimization using any DP online convex optimization algorithm as a subroutine. Hence, we give another excess risk bound of $\tilde{O}\left( \sqrt{\frac{d^{1/2}}{\epsilon K}} +\sqrt{\frac{p}{K\epsilon^2}} + \sqrt{\frac{p}{K}} \right)$. Assuming the typical setting of ε = Θ(1), this bound is more favorable than our first bound in a certain range of p as a function of K and d. Finally, we study differentially private worst-group empirical risk minimization in the offline setting, where each group distribution is observed by a fixed-size dataset. We present a new algorithm with nearly optimal excess risk of $\tilde{O}(\frac{p\sqrt{d}}{K\epsilon})$.

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Impact of radiation and heat source/sink on dissipative MHD mixed convection flow of casson nanofluid over a stretching sheet with chemical reaction

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Journal of Radiation Research and Applied sciences 2025年第3期18卷

作者： Jayaramireddy Konda Charankumar Ganteda Zabidin Salleh Indira Sundaram Mustafa Inc B. Ramakrishna Saiful Islam Vediyappan Govindan Department of Mathematics Koneru Lakshmaiah Education Foundation (KLEF) Vaddeswaram 522302 India Special Interest Group on Modelling and Data Analytics Faculty of Computer Science and Mathematics Universiti Malaysia Terengganu Kuala Nerus Terengganu 21030 Malaysia Department of Humanities and Sciences CVR College of Engineering Mangal Ally Hyderabad 501510 Telangana India Department of Mathematics Firat University 23119 Elazig Turkiye Department of Computer Engineering Biruni University 34010 Istanbul Turkiye Department of Mechanical Engineering Aditya Institute of Technology and Management Tekkali 532201 India Civil Engineering Department College of Engineering King Khalid University Abha 61421 Saudi Arabia Department of Mathematics Hindustan Institute of Technology and Science Chennai India

This study presents a numerical investigation of transverse magnetohydrodynamic (MHD) Casson nanofluid (CNF) flow over a nonlinear expanding surface, considering the effects of thermal radiation, heat generation/absorption, and chemical reactions. The research is significant due to its applications in biomedical engineering, polymer manufacturing, and industrial cooling systems, where precise control of heat and mass transfer is essential. The Casson fluid model is adopted to account for non-Newtonian behavior, and the effects of Brownian motion and thermophoresis are incorporated to enhance the understanding of nanoparticle transport mechanisms. The governing boundary-layer equations are converted into ordinary differential equations (ODEs) using similarity transformations. The Homotopy analysis method (HAM) is employed to obtain analytical solutions, providing a deeper insight into the parametric effects on flow characteristics. The study examines the influence of various parameters, including the Casson fluid parameter, Prandtl number (PN) (Pr), magnetic field parameter (M), Brownian motion parameter (Nb), nonlinear stretching parameter (n), Lewis number ( Le ), thermophoresis parameter (Nt), and chemical reaction rate, on velocity, temperature, and concentration distributions. Unlike previous studies, the PN is treated as a variable parameter to account for temperature-dependent viscosity variations. The findings reveal that increasing the Casson parameter reduces velocity due to enhanced fluid viscosity, while higher magnetic field strength suppresses flow due to Lorentz force effects. Additionally, Brownian motion and thermophoresis significantly impact temperature and nanoparticle concentration profiles, highlighting their importance in nanofluid transport. The study also presents graphical and tabular analyses of skin friction, heat transfer rate, and mass transfer rate, demonstrating their variations with key governing parameters. These results provide valu

关键词： Casson nanofluid Magnetic field Expanding sheet Radiation Heat source Substance response HAM

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Prediction of Rice Leaf Diseases at an Early Stage Using Deep Neural Networks 1

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8th International Conference on computers, Management and Mathematical sciences, ICCM 2022

作者： Mahanty, Mohan Vamsi, Bandi Srilatha, Y. Doppala, Bhanu Prakash Department of Computer Science and Engineering Vignan’s Institute of Information Technology-Duvvada Andhra Pradesh Visakhapatnam India Department of Artificial Intelligence and Data Science Madanapalle Institute of Technology & Science Andhra Pradesh Madanapalle India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur India Data Analytics Generation Australia SydneyNSW Australia

ISBN: (数字)9783031511677

ISBN: (纸本)9783031511660

Rice is the major crop in India, and India has been the biggest exporter and the second-largest producer in the entire world, so it is heavily reliant on rice for its economy and food supply. There has been an increase in rice production from 53.6 million tons in fiscal to 120 million tons over the last 40 years. Healthy and proper rice plant growth is required to maintain food production and supply in accordance with people’s needs and demands. The major diseases affecting these rice plants are leaf blast, hispa, and brown spot. The advancement of deep learning paved the way for these diseases to be detected using computer vision. Many researches have been conducted in the detection of rice leaf diseases, where the rice leaves have been deeply affected at a later stage. In this chapter, we propose an effective deep learning model for an early-stage disease detection of rice leaves. The proposed model was developed by an ensemble of the pretrained DenseNet-201 with the Naïve Inception module. The model was trained and tested over a Rice Diseases Image dataset;it attained an overall disease detection accuracy of 87.69% and surpasseed the existing models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Food supply

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PipeMoE: Accelerating Mixture-of-Experts through Adaptive Pipelining 42

PipeMoE: Accelerating Mixture-of-Experts through Adaptive Pi...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Shi, Shaohuai Pan, Xinglin Chu, Xiaowen Li, Bo Harbin Institute of Technology School of Computer Science and Technology Shenzhen China Hong Kong Baptist University Department of Computer Science Hong Kong Data Science and Analytics Thrust Hong Kong The Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong

ISBN: (纸本)9798350334142

Large models have attracted much attention in the AI area. The sparsely activated mixture-of-experts (MoE) technique pushes the model size to a trillion-level with a sub-linear increase of computations as an MoE layer can be equipped with many separate experts, but only one or two experts need to be trained for each input data. However, the feature of dynamically activating experts of MoE introduces extensive communications in distributed training. In this work, we propose PipeMoE to adaptively pipeline the communications and computations in MoE to maximally hide the communication time. Specifically, we first identify the root reason why a higher pipeline degree does not always achieve better performance in training MoE models. Then we formulate an optimization problem that aims to minimize the training iteration time. To solve this problem, we build performance models for computation and communication tasks in MoE and develop an optimal solution to determine the pipeline degree such that the iteration time is minimal. We conduct extensive experiments with 174 typical MoE layers and two real-world NLP models on a 64-GPU cluster. Experimental results show that our PipeMoE almost always chooses the best pipeline degree and outperforms state-of-the-art MoE training systems by 5%-77% in training time. © 2023 IEEE.

关键词： Pipelines

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Optimized Machine Learning for Classifying Colorectal Tissues

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SN computer science 2023年第5期4卷 461页

作者： Tripathi, Ashish Misra, Anuradha Kumar, Kuldeep Chaurasia, Brijesh Kumar Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Uttar Pradesh Lucknow India Center for Data Analytics Bond Business School Bond University Gold Coast QLD Australia Department of Computer Science and Engineering Pranveer Singh Institute of Technology Uttar Pradesh Kanpur India

Due to numerous deaths, colon cancer treatment and diagnosis are viewed as societal and financial challenges. The most severe reason for death worldwide is colorectal cancer. The classification of colon cancer tissues through images is presented in this paper as a multifaceted task. Classifying an illness at a premature stage increases its chances of existence, as late detection can be mortal which results in metastasis and a poor prognosis. The microscopic examination and classification of infected colon tissue sample images is a complex task. Also, the failure to manually detect the abnormality in the tissue by a pathologist might increase the severity of the disease. With the aid of intelligent machines, and automated diagnosis the classification of tissues from images can be done in much less time. These algorithms can learn by analyzing the patterns in the images and support the pathologist in completing the task with greater accuracy. In this research article, we proposed a tuned machine learning model, with the application of five machine learning techniques (K-Nearest Neighbor, Decision Trees, Random Forest, Categorical Boosting, and Gaussian Naive Bayes) for accurately classifying histopathological colon cancer tissues images of National Center for Tumor diseases Bank. The results demonstrate that the Categorical Boosting model has the best performance and is the most viable approach (accuracy: 0.9067, F1-Score: 0.9053, specificity: 0.9739, and sensitivity: 0.9757). © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Colorectal tissue classification Differential-Box-Count Histopathological image classification Machine Learning

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data driven VRP: A neural network model to learn hidden preferences for VRP 27

Data driven VRP: A neural network model to learn hidden pref...

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27th International Conference on Principles and Practice of Constraint Programming, CP 2021

作者： Mandi, Jayanta Canoy, Rocsildes Bucarey, Víctor Guns, Tias Data Analytics Laboratory Vrije Universiteit Brussel Belgium Institute of Engineering Sciences Universidad de o'Higgins Rancagua Chile Department of Computer Science KU Leuven Belgium

ISBN: (纸本)9783959772112

The traditional Capacitated Vehicle Routing Problem (CVRP) minimizes the total distance of the routes under the capacity constraints of the vehicles. But more often, the objective involves multiple criteria including not only the total distance of the tour but also other factors such as travel costs, travel time, and fuel consumption. Moreover, in reality, there are numerous implicit preferences ingrained in the minds of the route planners and the drivers. Drivers, for instance, have familiarity with certain neighborhoods and knowledge of the state of roads, and often consider the best places for rest and lunch breaks. This knowledge is difficult to formulate and balance when operational routing decisions have to be made. This motivates us to learn the implicit preferences from past solutions and to incorporate these learned preferences in the optimization process. These preferences are in the form of arc probabilities, i.e., the more preferred a route is, the higher is the joint probability. The novelty of this work is the use of a neural network model to estimate the arc probabilities, which allows for additional features and automatic parameter estimation. This first requires identifying suitable features, neural architectures and loss functions, taking into account that there is typically few data available. We investigate the difference with a prior weighted Markov counting approach, and study the applicability of neural networks in this setting. © Jayanta Mandi, Rocsildes Canoy, Víctor Bucarey, and Tias Guns.

关键词： Neural network models

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Harnessing artificial intelligence to fill global shortfalls in biodiversity knowledge

Nature Reviews Biodiversity

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Nature Reviews Biodiversity 2025年第3期1卷 166-182页

作者： Laura J. Pollock David Rolnick Justin Kitzes Sara Beery Kaitlyn M. Gaynor Marta A. Jarzyna Tanya Berger-Wolf Oisin Mac Aodha Bernd Meyer Graham W. Taylor Devis Tuia Department of Biology McGill University Montréal Quebec Canada Mila—Quebec AI Institute Montréal Quebec Canada School of Computer Science McGill University Montréal Quebec Canada Department of Biological Sciences University of Pittsburgh Pittsburgh PA USA Faculty of AI and Decision Making Massachusetts Institute of Technology Cambridge MA USA Department of Botany University of British Columbia Vancouver British Columbia Canada Department of Zoology University of British Columbia Vancouver British Columbia Canada Department of Evolution Ecology and Organismal Biology The Ohio State University Columbus OH USA Translational Data Analytics Institute The Ohio State University Columbus OH USA Department of Computer Science and Engineering The Ohio State University Columbus OH USA Department of Electrical & Computer Engineering The Ohio State University Columbus OH USA School of Informatics University of Edinburgh Edinburgh UK Department of Data Science and AI Monash University Clayton Victoria Australia School of Engineering University of Guelph Guelph Ontario Canada Vector Institute Toronto Ontario Canada School of Architecture Civil and Environmental Engineering Ecole Polytechnique Fédérale de Lausanne Lausanne Switzerland

Large, well described gaps exist in both what we know and what we need to know to address the biodiversity crisis. Artificial intelligence (AI) offers new potential for filling these knowledge gaps, but where the biggest and most influential gains could be made remains unclear. To date, biodiversity-related uses of AI have largely focused on tracking and monitoring of wildlife populations. Rapid progress is being made in the use of AI to build phylogenetic trees and species distribution models. However, AI also has considerable unrealized potential in the re-evaluation of important ecological questions, especially those that require the integration of disparate and inherently complex data types, such as images, video, text, audio and DNA. This Review describes the current and potential future use of AI to address seven clearly defined shortfalls in biodiversity knowledge. Recommended steps for AI-based improvements include the re-use of existing image data and the development of novel paradigms, including the collaborative generation of new testable hypotheses. The resulting expansion of biodiversity knowledge could lead to science spanning from genes to ecosystems — advances that might represent our best hope for meeting the rapidly approaching 2030 targets of the Global Biodiversity Framework.

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Machine Memory Intelligence: Inspired by Human Memory Mechanisms

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engineering 2025年

作者： Qinghua Zheng Huan Liu Xiaoqing Zhang Caixia Yan Xiangyong Cao Tieliang Gong Yong-Jin Liu Bin Shi Zhen Peng Xiaocen Fan Ying Cai Jun Liu School of Computer Science and Technology Xi’an Jiaotong University Xi’an 710049 China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University Xi’an 710049 China Translational Research Institute of Brain and Brain-Like Intelligence Shanghai Fourth People’s Hospital Shanghai 200434 China School of Medicine Tongji University Shanghai 200092 China Department of Computer Science and Technology Tsinghua University Beijing 100084 China Department of Computer Science Iowa State University Ames IA 50011 USA

Large models, exemplified by ChatGPT, have reached the pinnacle of contemporary artificial intelligence (AI). However, they are plagued by three inherent drawbacks: excessive training data and computing power consumption, susceptibility to catastrophic forgetting, and a deficiency in logical reasoning capabilities within black-box models. To address these challenges, we draw insights from human memory mechanisms to introduce “machine memory,” which we define as a storage structure formed by encoding external information into a machine-representable and computable format. Centered on machine memory, we propose the brand-new machine memory intelligence (M 2 I) framework, which encompasses representation, learning, and reasoning modules and loops. We explore the key issues and recent advances in the four core aspects of M 2 I, including neural mechanisms, associative representation, continual learning, and collaborative reasoning within machine memory. M 2 I aims to liberate machine intelligence from the confines of data-centric neural networks and fundamentally break through the limitations of existing large models, driving a qualitative leap from weak to strong AI.

关键词： Machine memory intelligence Neural mechanism Associative representation Continual learning Collaborative reasoning

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Visual Reasoning Indexing and Retrieval Using In-Memory Computing

Visual Reasoning Indexing and Retrieval Using In-Memory Comp...

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作者： Cao, Hongfei Li, Yu Allen, Carla M. Phinney, Michael A. Shyu, Chi-Ren Computer Science Interdisciplinary Data Analytics and Search Lab University of Missouri-Columbia ColumbiaMO65211 United States Clinical and Diagnostic Science Department University of Missouri-Columbia ColumbiaMO65211 United States Computer Science Electrical and Computer Engineering Informatics Institute Interdisciplinary Data Analytics and Search Lab University of Missouri-Columbia ColumbiaMO65211 United States

Research has shown that visual information of multimedia is critical in highly-skilled applications, such as biomedicine and life sciences, and a certain visual reasoning process is essential for meaningful search in a timely manner. Relevant image characteristics are learned and verified with accumulated experiences during the reasoning processes. However, such processes are highly dynamic and elusive to computationally quantify and therefore challenging to analyze, let alone to make the knowledge sharable across users. In this paper we study real-time human visual reasoning processes with the aid of gaze tracking devices. Temporal and spatial representations are proposed for gaze modeling, and a visual reasoning retrieval system utilizing in-memory computing under Big data ecosystems is designed for real-time search of similar reasoning models. Simulated data derived from human subject experiments show that the system has a reasonably high accuracy and provides predictive estimations for hardware requirements versus data sizes for exhaustive searches. Comparison between various visual action classifiers show challenges in modeling visual actions. The proposed system provides a theoretical framework and computing platform for advancement in visual semantic computing, as well as potential applications in medicine, social science, and arts. © 2016 World Scientific Publishing Company.

关键词： Eye tracking

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