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International Conference on Distributed Computing Systems

作者： Zheyuan Hu Jianwei Niu Tao Ren State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Zhengzhou University Research Institute of Industrial Technology School of Information Engineering Zhengzhou University Zhengzhou China Laboratory for Internet Software Technologies Institute of Software Chinese Academy of Sciences Beijing China

Mobile edge computing (MEC) has been proposed as a promising paradigm to provide mobile devices with both satisfactory computing capacity and task latency. One key issue in MEC is computation offloading (CompOff), which has attracted numerous research interests. Most existing CompOff approaches are developed based on iterative programming (IterProg), that calculates a CompOff action based on system dynamics each time mobile tasks arrive. Due to the heavy dependency of IterProg on reliable system dynamics, as well as the online computational burden, recent years have seen a popular trend to develop CompOff approaches based on deep reinforcement learning (DRL), which could generate real-time model-free CompOff actions. However, due to the intrinsic poor generalization of DRL, it is hard to directly apply DRL-based policies in new MEC environments, and long-time fine-tuning is often required. To address the challenge, this paper proposes a fast transferable CompOff framework (named TransOff), based on the idea of embedded reinforcement learning. Specifically, TransOff is composed of multiple primitive CompOff policies (pCOPs) and a multiplicative composition function (MCF). The pCOPs and MCF are pre-trained in a diverse variety of MEC environments. When encountering new MEC environments, pCOPs are kept fixed to prevent catastrophic forgetting of pre-trained CompOff skills, while only MCF is fine-tuned to produce new compositions of pCOPs to achieve fast transfer. We conduct extensive experiments via both numerical simulation and real testbed, indicating the fast transfer ability of TransOff compared to the state-of-the-art DRL-based and meta learning-based CompOff approaches.

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Code Prediction Based on Graph Embedding Model 16th

Code Prediction Based on Graph Embedding Model

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16th EAI International Conference on Collaborative Computing: Networking, Applications, and Worksharing, CollaborateCom 2020

作者： Yang, Kang Yu, Huiqun Fan, Guisheng Yang, Xingguang Chen, Liqiong Department of Computer Science and Engineering East China University of Science and Technology Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing China Shanghai China Shanghai Engineering Research Center of Smart Energy Shanghai China Department of Computer Science and Information Engineering Shanghai Institute of Technology Shanghai200235 China

ISBN: (纸本)9783030675394

Code prediction aims to accelerate the efficiency of programmer development. However, its prediction accuracy is still a great challenge. To facilitate the interpretability of the code prediction model and improve the accuracy of prediction. In this paper, the source code’s Abstract Syntax Tree (AST) is used to extract relevant structural paths between nodes and convert them into training graphs. The embedded model can convert the feature of the node sequence in the training graph into a vector that is convenient for quantization. We calculate the similarity between the candidate value and the parent node vector of the predicted path to obtain the predicted value. Experiments show that by using prediction data to increase the weight of related nodes in the graph, the model can extract more useful structural features, especially in Value prediction tasks. Adjusting the parameters embedded in the graph can improve the accuracy of the model. © 2021, ICST institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Trees (mathematics)

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Synergetic KDRF Classifier: A Random Forest-Based Hybrid Approach for Multi-Class Classification Task

Synergetic KDRF Classifier: A Random Forest-Based Hybrid App...

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International Conference on computer and information Technology (ICCIT)

作者： Israt Jahan Khan Md. Fahim Bin Amin Kamrul Islam Shahin Huu-Hoa Nguyen Dewan Md. Farid Department of Computer Science and Engineering United International University Dhaka Bangladesh Software Engineering The Maersk Mc-Kinney Moller Institute University of Southern Denmark Odense M Denmark College of Information and Communication Technology Can Tho University Can Tho City Vietnam

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

Multi-class classification presents a significant challenge in supervised machine learning, and it is frequently applied across various real-world domains. Random Forest (RF) stands out as a popular ensemble technique for multi-class classification tasks that utilizes attribute bagging to construct random trees and employs a majority-voting scheme for classification. However, the existing Random Forest approach often faces limitations in accuracy and efficiency, particularly for more critical tasks. In this study, we have addressed this gap by introducing a novel methodology that integrates the K-Nearest Neighbor (KNN) classifier and Decision Trees into the Random Forest framework to enhance performance. The proposed method has involved training a K-Nearest Neighbor and a Decision Tree (DT) classifier on the extracted features. The output probabilities from these classifiers have been used as meta-features, forming an enriched feature vector fed into a Random Forest as the meta-learner for final classification. Through extensive experimentation on 5 datasets, the proposed approach has demonstrated superior performance in accuracy and efficiency compared to the traditional method. By integrating Random Forest and K-Nearest Neighbor techniques, this research has offered a versatile solution applicable across diverse domains where precise multi-class classification is essential.

关键词： Training Accuracy Nearest neighbor methods Feature extraction Transformers Vectors Diabetes Random forests Classification tree analysis Diseases

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EFMLP: A Novel Model for Web Service QoS Prediction 16th

EFMLP: A Novel Model for Web Service QoS Prediction

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16th EAI International Conference on Collaborative Computing: Networking, Applications, and Worksharing, CollaborateCom 2020

作者： Ye, Kailing Yu, Huiqun Fan, Guisheng Chen, Liqiong Department of Computer Science and Engineering East China University of Science and Technology Shanghai200237 China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai201112 China Shanghai Engineering Research Center of Smart Energy Shanghai China Department of Computer Science and Information Engineering Shanghai Institute of Technology Shanghai201418 China

ISBN: (纸本)9783030675394

With the emergence of service-oriented architecture, quality of service (QoS) has become a crucial factor in describing the non-functional characteristics of Web services. In the real world, the user only requests limited Web services, the QoS record of Web services is sparsity. In this paper, we propose an approach named factorization machine and multi-layer perceptron model based on embedding technology (EFMLP) to solve the problem of sparsity and high dimension. First, the input data will be sent to embedding layer to reduce the data dimension. Then, the embedded feature vector will send to the factorization machine. After that, the first-order and second-order weights of the factorization machine are used as the initial weights of the first layer of the multi-layer perceptron. And the multi-layer perceptron is trained to adjust the weights. Finally, 1,974,675 pieces of data from an open dataset is used as experiment data to validate the model, and the result shows that our EFMLP model can predict QoS value accurately on the client side. © 2021, ICST institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Forecasting

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Efficient Federated Learning Using Dynamic Update and Adaptive Pruning with Momentum on Shared Server Data

arXiv

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arXiv 2024年

作者： Liu, Ji Jia, Juncheng Zhang, Hong Yun, Yuhui Wang, Leye Zhou, Yang Dai, Huaiyu Dou, Dejing Hithink RoyalFlush Information Network Co. Ltd. Hangzhou China School of Computer Science and Technology Soochow University Collaborative Innovation Center of Novel Software Technology and Industrialization Suzhou China School of Computer Science and Technology Soochow University Suzhou China Baidu Research Beijing China Key Lab of High Confidence Software Technologies Ministry of Education Software Institute Peking University Beijing China Department of Computer Science and Software Engineering Auburn University Auburn United States Department of Electrical and Computer Engineering North Carolina State University NC United States BEDI Cloud and School of Computer Science Fudan University Beijing China

Despite achieving remarkable performance, Federated Learning (FL) encounters two important problems, i.e., low training efficiency and limited computational resources. In this paper, we propose a new FL framework, i.e., FedDUMAP, with three original contributions, to leverage the shared insensitive data on the server in addition to the distributed data in edge devices so as to efficiently train a global model. First, we propose a simple dynamic server update algorithm, which takes advantage of the shared insensitive data on the server while dynamically adjusting the update steps on the server in order to speed up the convergence and improve the accuracy. Second, we propose an adaptive optimization method with the dynamic server update algorithm to exploit the global momentum on the server and each local device for superior accuracy. Third, we develop a layer-adaptive model pruning method to carry out specific pruning operations, which is adapted to the diverse features of each layer so as to attain an excellent trade-off between effectiveness and efficiency. Our proposed FL model, FedDUMAP, combines the three original techniques and has a significantly better performance compared with baseline approaches in terms of efficiency (up to 16.9 times faster), accuracy (up to 20.4% higher), and computational cost (up to 62.6% smaller). © 2024, CC BY.

关键词： Federated learning

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GEM: Context-Aware Gaze EstiMation with Visual Search Behavior Matching for Chest Radiograph

arXiv

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arXiv 2024年

作者： Liu, Shaonan Chen, Wenting Liu, Jie Luo, Xiaoling Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University China Department of Electrical Engineering City University of Hong Kong Hong Kong AI Research Center for Medical Image Analysis and Diagnosis Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

Gaze estimation is pivotal in human scene comprehension tasks, particularly in medical diagnostic analysis. Eye-tracking technology facilitates the recording of physicians’ ocular movements during image interpretation, thereby elucidating their visual attention patterns and information-processing strategies. In this paper, we initially define the context-aware gaze estimation problem in medical radiology report settings. To understand the attention allocation and cognitive behavior of radiologists during the medical image interpretation process, we propose a context-aware Gaze EstiMation (GEM) network that utilizes eye gaze data collected from radiologists to simulate their visual search behavior patterns throughout the image interpretation process. It consists of a context-awareness module, visual behavior graph construction, and visual behavior matching. Within the context-awareness module, we achieve intricate multimodal registration by establishing connections between medical reports and images. Subsequently, for a more accurate simulation of genuine visual search behavior patterns, we introduce a visual behavior graph structure, capturing such behavior through high-order relationships (edges) between gaze points (nodes). To maintain the authenticity of visual behavior, we devise a visual behavior-matching approach, adjusting the high-order relationships between them by matching the graph constructed from real and estimated gaze points. Extensive experiments on four publicly available datasets demonstrate the superiority of GEM over existing methods and its strong generalizability, which also provides a new direction for the effective utilization of diverse modalities in medical image interpretation and enhances the interpretability of models in the field of medical imaging. https://***/Tiger-SN/GEM. Copyright © 2024, The Authors. All rights reserved.

关键词： Eye movements

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An Efficient Distributed Framework for Secured Multimedia Content Using Cloud Storage

An Efficient Distributed Framework for Secured Multimedia Co...

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computer Technologies (ICCTech), International Conference on

作者： Yunus Parvej Faniband Iskandar Ishak Fatimah Sidi Marzanah A. Jabar Deloria Shaniska Razin C. H. Yap Center for Communication & IT Research Research Institute King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia Department of Computer Science Faculty of Computer Science and Information Technology Universiti Putra Malaysia UPM Serdang Selangor Malaysia Department of Software Engineering Faculty of Computer Science and Information Technology Universiti Putra Malaysia UPM Serdang Selangor Malaysia

Applications that support multimedia data such as medical pictures and videos, video broadcasting, and secure video conferencing consume vast quantities of information that require large numbers of servers to provide services for the many client users who access them. Cloud-based sensitive data becomes increasingly important for the respective users to be kept confidential without infringing confidentiality, integrity, and availability. We propose a framework based on the OpenStack cloud-computing platform for multimedia data security. A prototype framework with a helper tool EncSwift is implemented, which allows stringent data access control and applying the data-at-rest Over-Encryption approach. The implementation is evaluated for computation of cryptography operations, cost of communications, and update policy consequences for the framework. With OpenStack Swift's open source and modular design, our multimedia data management and management framework is well suited to protecting and managing clouds' multimedia with low computational costs as shown in the experimental results.

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Advancing cancer driver gene detection via Schur complement graph augmentation and independent subspace feature extraction

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computers in Biology and Medicine 2024年 174卷 108484-108484页

作者： Ma, Xinqian Li, Zhen Du, Zhenya Xu, Yan Chen, Yifan Zhuo, Linlin Fu, Xiangzheng Liu, Ruijun School of Data Science and Artificial Intelligence Wenzhou University of Technology Wenzhou325027 China Guangzhou Xinhua University Guangzhou510520 China School of Computer Science of Information Technology Qiannan Normal University for Nationalities Guizhou Duyun558000 China Institute of Computational Science and Technology Guangzhou University Guangzhou510000 China College of Computer and Information Engineering Central South University of Forestry and Technology Hunan Changsha410004 China College of Computer Science and Electronic Engineering Hunan University Changsha410012 China School of Software Beihang University Beijing China

Accurately identifying cancer driver genes (CDGs) is crucial for guiding cancer treatment and has recently received great attention from researchers. However, the high complexity and heterogeneity of cancer gene regulatory networks limit the precition accuracy of existing deep learning models. To address this, we introduce a model called SCIS-CDG that utilizes Schur complement graph augmentation and independent subspace feature extraction techniques to effectively predict potential CDGs. Firstly, a random Schur complement strategy is adopted to generate two augmented views of gene network within a graph contrastive learning framework. Rapid randomization of the random Schur complement strategy enhances the model's generalization and its ability to handle complex networks effectively. Upholding the Schur complement principle in expectations promotes the preservation of the original gene network's vital structure in the augmented views. Subsequently, we employ feature extraction technology using multiple independent subspaces, each trained with independent weights to reduce inter-subspace dependence and improve the model's expressiveness. Concurrently, we introduced a feature expansion component based on the structure of the gene network to address issues arising from the limited dimensionality of node features. Moreover, it can alleviate the challenges posed by the heterogeneity of cancer gene networks to some extent. Finally, we integrate a learnable attention weight mechanism into the graph neural network (GNN) encoder, utilizing feature expansion technology to optimize the significance of various feature levels in the prediction task. Following extensive experimental validation, the SCIS-CDG model has exhibited high efficiency in identifying known CDGs and uncovering potential unknown CDGs in external datasets. Particularly when compared to previous conventional GNN models, its performance has seen significant improved. The code and data are publicly available at:

关键词： Genes

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A Secure Deepfake Mitigation Framework: Architecture, Issues, Challenges, and Societal Impact

Cyber Security and Applications

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Cyber Security and Applications 2024年 2卷

作者： Wazid, Mohammad Mishra, Amit Kumar Mohd, Noor Das, Ashok Kumar Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun 248 002 India Department of Computer Science and Engineering Graphic Era Hill University Dehradun 248 002 India Center for Security Theory and Algorithmic Research International Institute of Information Technology Hyderabad 500 032 India

Deepfake refers to synthetic media generated through artificial intelligence (AI) techniques. It involves creating or altering video, audio, or images to make them appear as though they depict something or someone else. Deepfake technology advances just like the mechanisms that are used to detect them. There's an ongoing cat-and-mouse game between creators of deepfakes and those developing detection methods. As the technology that underpins deepfakes continues to improve, we are obligated to confront the repercussions that it will have on society. The introduction of educational initiatives, regulatory frameworks, technical solutions, and ethical concerns are all potential avenues via which this matter can be addressed. Multiple approaches need to be combined to identify deepfakes effectively. Detecting deepfakes can be challenging due to their increasingly sophisticated nature, but several methods and techniques are being developed to identify them. Mitigating the negative impact of deepfakes involves a combination of technological advancements, awareness, and policy measures. In this paper, we propose a secure deepfake mitigation framework. We have also provided a security analysis of the proposed framework via the Scyhter tool-based formal security verification. It proves that the proposed framework is secure against various cyber attacks. We also discuss the societal impact of deepfake events along with its detection process. Then some AI models, which are used for creating and detecting the deepfake events, are highlighted. Ultimately, we provide the practical implementation of the proposed framework to observe its functioning in a real-world scenario. © 2024 The Authors

关键词： Artificial Intelligence (AI) Authentication Cyber security Deepfake Machine learning

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Optimizing potato leaf disease recognition: Insights DENSE-NET-121 and Gaussian elimination filter fusion

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Heliyon 2025年第3期11卷 e42318页

作者： Raza, Asif Pitafi, Abdul Hammed Shaikh, M. Kahsif Ahmed, Khaliq Universiti Kuala Lumpur Malaysian Institute of Information Technology (UniKL MIIT) 1016 Jalan Sultan Ismail Kuala Lumpur 50250 Malaysia Department of Computer Science and Information Technology Sir Syed University of Engineering & Technology Karachi Pakistan Department of Software Engineering Sir Syed University of Engineering & Technology Karachi Pakistan Department of Computer Science Iqra University Karachi Pakistan

Ensuring a sustainable global food security status which necessitated by achieving an equilibrium state between the anticipated and significant rise in the global population and the projected agricultural output which is essential for their food adequacy. The absence of such a harmonious balance may be a contributing factor to the emergence of food crises worldwide. Hence, it is imperative to proactively address and mitigate both direct and indirect factors that could potentially lead to this agricultural yield imbalance. Facilitating optimal plant growth and implementing effective measures against diseases play a fundamental role in meeting the global demand for food in terms of both quality and quantity. This article offered a hybrid model based on Deep learning called DENSE-NET-121 with 2D Gaussian elimination filters that can be effective deep learning tools to increase potato yield by early detection of the leaf. Three types of potato leaf classes called Early Blight, Healthy, and Late Blight are incorporated by Dataset which has been taken from the kaggle repository. Considering this proposed model, state-of-the-art DENSE-NET-121 has produced an unprecedented training and validation accuracy 0.9908, 0.9837 respectively furthermore model also produced extremely low training and validation loss 0.0683, 0.0796 and an error rate below then 0.1 as well. Furthermore model produced average Precision, and recall, 0.98, 0.96, and 0.97 respectively. © 2025 The Authors

关键词： 2D Gaussian filter Deep learning DENSE-NET 121 Machine learning Potato leaf

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