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arXiv 2023年

作者： Ghazali, Rana Adabi, Sahar Rezaee, Ali Down, Douglas G. Movaghar, Ali Department of Computer Engineering North Tehran Branch Islamic Azad University Tehran Iran Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran Department of Computing and Software McMaster University 1280 Main St W HamiltonON Canada Department of Computer Engineering Sharif University of Technology Azadi Ave Tehran Iran

Modern applications can generate a large amount of data from different sources with high velocity, a combination that is difficult to store and process via traditional tools. Hadoop is one framework that is used for the parallel processing of a large amount of data in a distributed environment, however, various challenges can lead to poor performance. Two particular issues that can limit performance are the high access time for I/O operations and the recomputation of intermediate data. The combination of these two issues can result in resource wastage. In recent years, there have been attempts to overcome these problems by using caching mechanisms. Due to cache space limitations, it is crucial to use this space efficiently and avoid cache pollution (the cache contains data that is not used in the future). We propose Hadoop-oriented SVM-LRU (H-SVM-LRU) to improve Hadoop performance. For this purpose, we use an intelligent cache replacement algorithm, SVM-LRU, that combines the well-known LRU mechanism with a machine learning algorithm, SVM, to classify cached data into two groups based on their future usage. Experimental results show a significant decrease in execution time as a result of an increased cache hit ratio, leading to a positive impact on Hadoop performance. © 2023, CC BY-SA.

关键词： MapReduce

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A Survey on Learnable Evolutionary Algorithms for Scalable Multiobjective Optimization

arXiv

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arXiv 2022年

作者： Liu, Songbai Lin, Qiuzhen Li, Jianqiang Tan, Kay Chen The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China The Department of Computing The Hong Kong Polytechnic University Hong Kong

Recent decades have witnessed great advancements in multiobjective evolutionary algorithms (MOEAs) for multiobjective optimization problems (MOPs). However, these progressively improved MOEAs have not necessarily been equipped with scalable and learnable problem-solving strategies for new and grand challenges brought by the scaling-up MOPs with continuously increasing complexity from diverse aspects, mainly including expensive cost of function evaluations, many objectives, large-scale search space, time-varying environments, and multitask. Under different scenarios, divergent thinking is required in designing new powerful MOEAs for solving them effectively. In this context, research studies on learnable MOEAs with machine learning techniques have received extensive attention in the field of evolutionary computation. This paper begins with a general taxonomy of scaling-up MOPs and learnable MOEAs, followed by an analysis of the challenges that these MOPs pose to traditional MOEAs. Then, we synthetically overview recent advances of learnable MOEAs in solving various scaling-up MOPs, focusing primarily on four attractive directions (i.e., learnable evolutionary discriminators for environmental selection, learnable evolutionary generators for reproduction, learnable evolutionary evaluators for function evaluations, and learnable evolutionary transfer modules for sharing or reusing optimization experience). The insight of learnable MOEAs is offered to readers as a reference to the general track of the efforts in this field. Copyright © 2022, The Authors. All rights reserved.

关键词： Multiobjective optimization

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A Lightweight Cloud-Edge Collaborative Intelligence Inference Framework with Runtime Dynamic Optimization for Resource-Constrained Consumer Electronics

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IEEE Transactions on Consumer Electronics 2025年

作者： Wang, Chenlu Peng, Yuhuai Zhang, Dawei Alturki, Ryan Alshawi, Bandar Alotaibi, Majid Northeastern University College of Computer Science and Engineering Shenyang110819 China Zhiyuan Research Institute Xihu District Hangzhou China Zhengzhou University School of Computer and Artificial Intelligence Henan Zhengzhou450001 China Umm Al-Qura University College of Computing Department of Software Engineering Makkah24382 Saudi Arabia Umm Al-Qura University College of Computing Department of Computer and Network Engineering Makkah24382 Saudi Arabia

The proliferation of Internet of Things (IoT) and embedded computing has led to widespread deployment of smart consumer electronics requiring edge-based Artificial Intelligence (AI) capabilities. However, the heterogeneous nature of sensing data and dynamic edge environments poses significant challenges for efficient model inference on resource-constrained devices. To address these challenges, this paper presents a lightweight collaborative inference framework designed for consumer electronics. First, we formulate the inference optimization problem as a Mixed-Integer Nonlinear Programming (MINLP) problem, considering channel pruning, early exit and cloud offloading decisions to optimize the trade-off between accuracy and computational cost. Second, we propose a selective model activation mechanism based on Markov Decision Process (MDP), which employs a recursive self-attention mechanism to dynamically track inference budgets and guide decision-making through encoder-decoder architectures. The mechanism integrates entropy regularization during training to ensure robust and diverse execution paths. Comprehensive experiments demonstrate that our framework achieves 65.50% reduction in model parameters and 80.68% reduction in inference Floating Point Operations (FLOPs) while maintaining accuracy loss within 0.81% of the original model, making it suitable for real-time AI applications on resource-constrained consumer electronics. © 1975-2011 IEEE.

关键词： Markov processes

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Compromising Rechargeable Sensor Networks in Marine Environment

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IEEE Transactions on Mobile computing 2025年

作者： Wang, Qiwei Lin, Chi Dai, Haipeng Obaidat, Mohammad S. Hsiao, Kuei-Fang Fan, Xin Dalian University of Technology School of Software Technology & DUT-RU International School of Information Science and Engineering China Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province Dalian116620 China Nanjing University State Key Laboratory for Novel Software Technology Nanjing210023 China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China SRM University Department of Computational Intelligence School of Computing SRM Nagar TN Kattankulathur603203 India The Amity University School of Engineering UP Noida201301 India The King Abdullah II School of Information Technology The University of Jordan Amman11942 Jordan

Marine Wireless Rechargeable Sensor Networks (MWRSNs), enhanced by recent Wireless Power Transfer (WPT) technology, present a significant advancement in extending network life. Traditional methods improve network performance through algorithm optimization, but neglect charging security, exposing networks to potential attacks. This paper addresses this problem from an adversarial view and develops a novel attack for MWRSN through Denying of Charge (DoC) to maximize network destructiveness. We start by establishing a generalized on-demand charging model, essential for developing DoC tactics. Subsequently, we unveil the Collaborative DoC (CoDoC) algorithm, capable of manipulating and falsifying charging requests. Central to CoDoC is the Request Prediction Method (RPM), which forecasts the initiation of charging requests and facilitates rapid request surges to enhance the attack's efficacy. CoDoC is able to disguise the presence of the attack, which is able to escape from being detected by the base station. Theoretical analyses are provided to explore the features of the proposed scheme. To demonstrate the outperformed features of the proposed schemes, extensive simulations and test-bed experiments are conducted. Our analysis and extensive simulations demonstrate that CoDoC increases sensor node failures by 20% to 142% compared to traditional methods, highlighting its effectiveness in marine environments. © 2002-2012 IEEE.

关键词： Charging stations

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DAMPER: A Dual-Stage Medical Report Generation Framework with Coarse-Grained MeSH Alignment and Fine-Grained Hypergraph Matching

arXiv

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arXiv 2024年

作者： Huang, Xiaofei Chen, Wenting Liu, Jie Lu, Qisheng Luo, Xiaoling Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Electrical Engineering City University of Hong Kong Kowloon Hong Kong National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

Medical report generation is crucial for clinical diagnosis and patient management, summarizing diagnoses and recommendations based on medical imaging. However, existing work often overlook the clinical pipeline involved in report writing, where physicians typically conduct an initial quick review followed by a detailed examination. Moreover, current alignment methods may lead to misaligned relationships. To address these issues, we propose DAMPER, a dual-stage framework for medical report generation that mimics the clinical pipeline of report writing in two stages. In the first stage, a MeSH-Guided Coarse-Grained Alignment (MCG) stage that aligns chest X-ray (CXR) image features with medical subject headings (MeSH) features to generate a rough keyphrase representation of the overall impression. In the second stage, a Hypergraph-Enhanced Fine-Grained Alignment (HFG) stage that constructs hypergraphs for image patches and report annotations, modeling high-order relationships within each modality and performing hypergraph matching to capture semantic correlations between image regions and textual phrases. Finally,the coarse-grained visual features, generated MeSH representations, and visual hypergraph features are fed into a report decoder to produce the final medical report. Extensive experiments on public datasets demonstrate the effectiveness of DAMPER in generating comprehensive and accurate medical reports, outperforming state-of-the-art methods across various evaluation metrics. Copyright © 2024, The Authors. All rights reserved.

关键词： Coarse-grained modeling

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Detached and Interactive Multimodal Learning

arXiv

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arXiv 2024年

作者： Fan, Yunfeng Xu, Wenchao Wang, Haozhao Liu, Junhong Guo, Song Department of Computing The Hong Kong Polytechnic University Hong Kong School of Computer Science and Technology Huazhong University of Science and Technology Hubei China School of Software Beihang University Beijing China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong

Recently, Multimodal Learning (MML) has gained significant interest as it compensates for single-modality limitations through comprehensive complementary information within multimodal data. However, traditional MML methods generally use the joint learning framework with a uniform learning objective that can lead to the modality competition issue, where feedback predominantly comes from certain modalities, limiting the full potential of others. In response to this challenge, this paper introduces DI-MML, a novel detached MML framework designed to learn complementary information across modalities under the premise of avoiding modality competition. Specifically, DI-MML addresses competition by separately training each modality encoder with isolated learning objectives. It further encourages cross-modal interaction via a shared classifier that defines a common feature space and employing a dimension-decoupled unidirectional contrastive (DUC) loss to facilitate modality-level knowledge transfer. Additionally, to account for varying reliability in sample pairs, we devise a certainty-aware logit weighting strategy to effectively leverage complementary information at the instance level during inference. Extensive experiments conducted on audio-visual, flow-image, and front-rear view datasets show the superior performance of our proposed method. The code is released at https://***/fanyunfeng-bit/DI-MML. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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C++ and Interoperability Between Libraries: The GraphBLAS C++ Specification

C++ and Interoperability Between Libraries: The GraphBLAS C+...

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IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Benjamin Brock Scott McMillan Aydın Buluç Timothy G. Mattson José E. Moreira Parallel Computing Lab Intel Software Engineering Institute Carnegie Mellon University Computational Research Department Lawrence Berkeley National Laboratory EECS Department University of California Berkeley IBM Thomas J. Watson Research Center

Interoperability between libraries is often hindered by incompatible data formats, which can necessitate creating new copies of data when transferring data back and forth between different libraries. This additional data movement incurs additional runtime costs, particularly for sparse applications, where the costs of data movement often dwarf compute costs. In this paper, we investigate interoperability in the context of the C++ GraphBLAS Specification, where C++ concepts allow GraphBLAS algorithms to accept any matrix type as long as it follows the matrix interface defined in the GraphBLAS matrix concept. We first develop non-owning, lazily evaluated adapted views for a number of external data structures, including two categories of graphs defined in the Northwest Graph Library (NWGraph) and traditional pointer-based CSR data structures. These adapted views fulfill the C++ GraphBLAS matrix concept, allowing them to be used inside GraphBLAS algorithms. We then evaluate the performance of these adapted views across two kernels, matrix reduction and sparse times dense matrix multiplication (SpMM), where the performance achieved using a single generic implementation with these views largely matches the performance achieved operating directly on the original data structures, with a slight performance loss in one case. We then propose a mechanism for automatically discovering the availability of these views, allowing algorithms to directly accept external data structures. We also discuss potential extensions to the C++ GraphBLAS specification that might eliminate the small performance dip observed for one of the views.

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Fast Second-order Method for Neural Networks under Small Treewidth Setting

Fast Second-order Method for Neural Networks under Small Tre...

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IEEE International Conference on Big Data

作者： Xiaoyu Li Jiangxuan Long Zhao Song Tianyi Zhou Department of Computer Science Stevens Institute of Technology Hoboken NJ School of Software Engineering South China University of Technology Guangzhou China The Simons Institute for the Theory of Computing University of California Berkeley Berkeley CA Department of Computer Science University of Southern California Los Angeles CA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Training neural networks is a fundamental problem in theoretical machine learning. Second-order methods are rarely used in practice due to their high computational cost, even they converge much faster than first-order methods. The state-of-the-art result for the second-order method to train an over-parameterized neural network can run in O(log(1/ϵ)) iterations and each iteration has O(mnd + n 3 ) running time, where n represent the number of data points, d is the dimension of the feature space of each data and m is the width of the neural network [Brand, Peng, Song and Weinstein ITCS 2021]. In this work, we further improve the convergence rate and the running time for each iteration to O(loglog(1/ϵ)) and O(mdτ 2 ) respectively, where m = Ω(n 4 ) and τ is the treewidth of the data matrix and usually very small. Our algorithm has a quadratic convergence rate, so it can be regarded as a truly second-order algorithm. To the best of our knowledge, our algorithm is the first to achieve a quadratic convergence rate for neural network training, with a per-iteration running time of O(mdτ 2 ).

关键词： Training Knowledge engineering Costs Neural networks Machine learning Big Data Computational efficiency Matrix decomposition Optimization Convergence

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How Far Have Edge Clouds Gone? A Spatial-Temporal Analysis of Edge Network Latency In the Wild

How Far Have Edge Clouds Gone? A Spatial-Temporal Analysis o...

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International Workshop on Quality of Service

作者： Heng Zhang Shaoyuan Huang Mengwei Xu Deke Guo Xiaofei Wang Victor C.M. Leung Wenyu Wang College of Intelligence and Computing Tianjin University Tianjin China Beijing University of Posts and Telecommunications Beijing China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada Paiou Cloud Computing (Shanghai) Co. Ltd Shanghai China

The emergence of next-generation latency-critical applications places strict requirements on network latency and stability. Edge cloud, an instantiated paradigm for edge computing, is gaining more and more attention due to its benefits of low latency. In this work, we make an in-depth investigation into the network QoS, especially end-to-end latency, at both spatial and temporal dimensions on a nationwide edge computing platform. Through the measurements, we collect a multi-variable large-scale real-world dataset on latency. We then quantify how the spatial-temporal factors affect the end-to-end latency, and verified the predictability of end-to-end latency. The results reveal the limitation of centralized clouds and illustrate how could edge clouds provide low and stable latency. Our results also point out that existing edge clouds merely increase the density of servers and ignore spatial-temporal factors, so they still suffer from high latency and fluctuations. Based on the observations, we propose a robust prototype edge cloud model based on lessons we learn from the measurement and evaluate its performance in the production environment. The further evaluation result shows that edge clouds achieve 84.1% latency reduction with 0.5ms latency fluctuation and 73.3% QoS improvement compared with the centralized clouds.

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Analysis of android applications permissions 3

Analysis of android applications permissions

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3rd International Conference on Data Science, E-Learning and Information Systems, DATA 2021

作者： Saif, Amal Al-Kilani, Hamzeh Qasaimeh, Malik Al-Refai, Abdullah Computer Science Department King Hussein School of Computing Sciences Princess Sumaya University for Technology Amman Jordan Software Engineering Department Faculty of Computer and Information Technology Jordan University of Science and Technology Irbid Jordan

ISBN: (纸本)9781450388382

As Android is one of the most popular mobile open-source platforms, it is very important to ensure the security and privacy of Android apps. Android has an authorization system that allows developers to announce their applications requiring essential services, and when running such applications, users need to comply with these requirements. Users frequently download applications, giving them infinite permissions easily without thinking about the impact on their privacy. In this paper, we analyze 222 applications manually to grant these permissions to see how they are compatible with user privacy depending on many criteria. © 2021 Association for computing Machinery.

关键词： Android (operating system)

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