检索结果-内蒙古大学图书馆

On-device diagnostic recommendation with heterogeneous federated BlockNets

Science China(Information Sciences) 2025年第4期68卷 33-49页

作者： Minh Hieu NGUYEN Thanh Trung HUYNH Thanh Toan NGUYEN Phi Le NGUYEN Hien Thu PHAM Jun JO Thanh Tam NGUYEN School of Information and Communication Technology Griffith University School of Computer and Communication Sciences Ecole Polytechnique Federale de Lausanne Faculty of Information Technology HUTECH University School of Information and Communication Technology Hanoi University of Science and Technology Commonwealth Scientific and Industrial Research Organization (CSIRO)

The evolution of edge computing has advanced the accessibility of E-health recommendation services, encompassing areas such as medical consultations, prescription guidance, and diagnostic assessments. Traditional methodologies predominantly utilize centralized recommendations, relying on servers to store client data and dispatch advice to ***, these conventional approaches raise significant concerns regarding data privacy and often result in computational inefficiencies. E-health recommendation services, distinct from other recommendation domains, demand not only precise and swift analyses but also a stringent adherence to privacy safeguards, given the users' reluctance to disclose their identities or health information. In response to these challenges, we explore a new paradigm called on-device recommendation tailored to E-health diagnostics, where diagnostic support(such as biomedical image diagnostics), is computed at the client *** leverage the advances of federated learning to deploy deep learning models capable of delivering expert-level diagnostic suggestions on clients. However, existing federated learning frameworks often deploy a singular model across all edge devices, overlooking their heterogeneous computational capabilities. In this work, we propose an adaptive federated learning framework utilizing BlockNets, a modular design rooted in the layers of deep neural networks, for diagnostic recommendation across heterogeneous devices. Our framework offers the flexibility for users to adjust local model configurations according to their device's computational power. To further handle the capacity skewness of edge devices, we develop a data-free knowledge distillation mechanism to ensure synchronized parameters of local models with the global model, enhancing the overall accuracy. Through comprehensive experiments across five real-world datasets, against six baseline models, within six experimental setups, and various data distribution scenario

关键词： intelligent recommendation federated learning heterogeneous devices E-health diagnostics

来源：评论

学校读者我要写书评

暂无评论

Bimodal Stock Price Prediction Based on Holt-Winters Exponential Smoothing and PCA Whitening Transformation

IAENG International Journal of Computer Science

引用

IAENG International Journal of computer Science 2025年第1期52卷 187-200页

作者： Zhu, Changsheng Lu, Pengyun Feng, Wenfang Wang, Yanbo School of Computer and Communication Lanzhou University of Technology Lanzhou730050 China School of Computer and Communication Lanzhou University of Technology Lanzhou730050 China School of Economics and Management Lanzhou University of Technology Lanzhou730050 China School of Computer and Communication Lanzhou University of Technology Lanzhou730050 China

Aiming at the nonlinear and high frequency characteristics of stock data, a hybrid stock price prediction model is proposed, which combines the Holt-Winters triple exponential smoothing method, PCA whitening transformation, PatchTST and BERT, called HW-PCAW-PB. The PCA whitening transformation is performed on the feature vector of investor stock reviews constructed by the BERT model, and the stock price features are processed using the Holt-Winters triple exponential smoothing algorithm and the PatchTST model. The cross-attention mechanism fuses the processed stock review feature information, stock price feature information, and multi-source features such as stock review reading volume and number of comments to predict stock prices. The prediction performance of the HW-PCAW-PB model is compared with benchmark models such as SVR, BiLSTM, TimesNet, KNN, ANN, LSTM, Autoformer and Informer using RMSE, MAE, MAPE and R2 evaluation metrics. The experimental results show that the HW-PCAW-PB model achieves better scores than the benchmark models in all evaluation metrics. © (2025), (International Association of Engineers). All rights reserved.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

Application of maximum rank distance codes in designing of STBC-OFDM system for next-generation wireless communications

引用

Digital communications and Networks 2024年第4期10卷 1048-1056页

作者： Arslan Khalid Prapun Suksompong School of Information Computer and Communication TechnologySirindhorn International Institute of TechnologyThammasat UniversityPathumthani12000Thailand

Space-Time Block Coded(STBC)Orthogonal Frequency Division Multiplexing(OFDM)satisfies higher data-rate requirements while maintaining signal quality in a multipath fading ***,conventional STBCs,including Orthogonal STBCs(OSTBCs),Non-Orthogonal(NOSTBCs),and Quasi-Orthogonal STBCs(QOSTBCs),do not provide both maximal diversity order and unity code rate simultaneously for more than two transmit *** paper targets this problem and applies Maximum Rank Distance(MRD)codes in designing STBCOFDM *** following the direct-matrix construction method,we can construct binary extended finite field MRD-STBCs for any number of transmitting *** uses MRD-STBCs built over Phase-Shift Keying(PSK)modulation to develop an MRD-based STBC-OFDM *** MRD-based STBC-OFDM system sacrifices minor error performance compared to traditional OSTBC-OFDM but shows improved results against NOSTBC and *** also provides 25%higher data-rates than OSTBC-OFDM in configurations that use more than two transmit *** tradeoffs are minor increases in computational complexity and processing delays.

关键词： Bit error rate(BER) Galois field Maximum rank distance(MRD)codes Orthogonal frequency division multiplexing (OFDM) Primitive polynomials Space-time block codes(STBC)

来源：评论

学校读者我要写书评

暂无评论

Defending Federated Learning System from Poisoning Attacks via Efficient Unlearning

引用

computers, Materials & Continua 2025年第4期83卷 239-258页

作者： Long Cai Ke Gu Jiaqi Lei School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China

Large-scale neural networks-based federated learning(FL)has gained public recognition for its effective capabilities in distributed ***,the open system architecture inherent to federated learning systems raises concerns regarding their vulnerability to potential *** attacks turn into a major menace to federated learning on account of their concealed property and potent destructive *** altering the local model during routine machine learning training,attackers can easily contaminate the global *** detection and aggregation solutions mitigate certain threats,but they are still insufficient to completely eliminate the influence generated by ***,federated unlearning that can remove unreliable models while maintaining the accuracy of the global model has become a *** some existing federated unlearning approaches are rather difficult to be applied in large neural network models because of their high computational ***,we propose SlideFU,an efficient anti-poisoning attack federated unlearning *** primary concept of SlideFU is to employ sliding window to construct the training process,where all operations are confined within the *** design a malicious detection scheme based on principal component analysis(PCA),which calculates the trust factors between compressed models in a low-cost way to eliminate unreliable *** confirming that the global model is under attack,the system activates the federated unlearning process,calibrates the gradients based on the updated direction of the calibration *** on two public datasets demonstrate that our scheme can recover a robust model with extremely high efficiency.

关键词： Federated learning malicious client detection model recovery machine unlearning

来源：评论

学校读者我要写书评

暂无评论

Resonant tunneling diode cellular neural network with memristor coupling and its application in police forensic digital image protection

引用

Chinese Physics B 2025年第5期34卷 289-301页

作者： Fei Yu Dan Su Shaoqi He Yiya Wu Shankou Zhang Huige Yin School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha 410114China

Due to their biological interpretability,memristors are widely used to simulate synapses between artificial neural *** a type of neural network whose dynamic behavior can be explained,the coupling of resonant tunneling diode-based cellular neural networks(RTD-CNNs)with memristors has rarely been reported in the ***,this paper designs a coupled RTD-CNN model with memristors(RTD-MCNN),investigating and analyzing the dynamic behavior of the *** on this model,a simple encryption scheme for the protection of digital images in police forensic applications is *** results show that the RTD-MCNN can have two positive Lyapunov exponents,and its output is influenced by the initial values,exhibiting ***,a set of amplitudes in its output sequence is affected by the internal parameters of the memristor,leading to nonlinear ***,the rich dynamic behaviors described above make the RTD-MCNN highly suitable for the design of chaos-based encryption schemes in the field of privacy *** tests and security analyses validate the effectiveness of this scheme.

关键词： memristor hyperchaos resonant tunneling diode-based cellular neural network(RTD-CNN) dynamic analysis image encryption

来源：评论

学校读者我要写书评

暂无评论

Vertex centrality of complex networks based on joint nonnegative matrix factorization and graph embedding

引用

Chinese Physics B 2023年第1期32卷 634-645页

作者：卢鹏丽陈玮 School of Computer and Communication Lanzhou University of TechnologyLanzhou 730050China

Finding crucial vertices is a key problem for improving the reliability and ensuring the effective operation of networks,solved by approaches based on multiple attribute decision that suffer from ignoring the correlation among each attribute or the heterogeneity between attribute and structure. To overcome these problems, a novel vertex centrality approach, called VCJG, is proposed based on joint nonnegative matrix factorization and graph embedding. The potential attributes with linearly independent and the structure information are captured automatically in light of nonnegative matrix factorization for factorizing the weighted adjacent matrix and the structure matrix, which is generated by graph embedding. And the smoothness strategy is applied to eliminate the heterogeneity between attributes and structure by joint nonnegative matrix factorization. Then VCJG integrates the above steps to formulate an overall objective function, and obtain the ultimately potential attributes fused the structure information of network through optimizing the objective function. Finally, the attributes are combined with neighborhood rules to evaluate vertex's importance. Through comparative analyses with experiments on nine real-world networks, we demonstrate that the proposed approach outperforms nine state-of-the-art algorithms for identification of vital vertices with respect to correlation, monotonicity and accuracy of top-10 vertices ranking.

关键词： complex networks centrality joint nonnegative matrix factorization graph embedding smoothness strategy

来源：评论

学校读者我要写书评

暂无评论

AG-GATCN:A novel method for predicting essential proteins

引用

Chinese Physics B 2023年第5期32卷 737-745页

作者：杨培实卢鹏丽张腾 School of Computer and Communication Lanzhou University of TechnologyLanzhou 730050China

Essential proteins play an important role in disease diagnosis and drug *** methods have been devoted to the essential protein prediction by using some kinds of biological ***,they either ignore the noise presented in the biological information itself or the noise generated during feature *** overcome these problems,in this paper,we propose a novel method for predicting essential proteins called attention gate-graph attention network and temporal convolutional network(AG-GATCN).In AG-GATCN method,we use improved temporal convolutional network(TCN)to extract features from gene expression *** address the noise in the gene expression sequence itself and the noise generated after the dilated causal convolution,we introduce attention mechanism and gating mechanism in *** addition,we use graph attention network(GAT)to extract protein–protein interaction(PPI)network features,in which we construct the feature matrix by introducing node2vec technique and 7 centrality metrics,and to solve the GAT oversmoothing problem,we introduce gated tanh unit(GTU)in ***,two types of features are integrated by us to predict essential *** with the existing methods for predicting essential proteins,the experimental results show that AG-GATCN achieves better performance.

关键词： complex networks essential proteins temporal convolutional network graph attention network gene expression

来源：评论

学校读者我要写书评

暂无评论

Fake Face Detection Based on Fusion of Spatial Texture and High-Frequency Noise

引用

Chinese Journal of Electronics 2025年第1期34卷 212-221页

作者： Dengyong Zhang Feifan Qi Jiahao Chen Jiaxin Chen Rongrong Gong Yuehong Tian Lebing Zhang Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation School of Computer and Communication Engineering Changsha University of Science and Technology School of Computer and Communication Engineering Changsha University of Science and Technology Changsha Social Work College Changkuangao Beijing Technology Co. Ltd. School of Computer and Artificial Intelligence Huaihua University

The rapid development of the Internet has led to the widespread dissemination of manipulated facial images, significantly impacting people's daily lives. With the continuous advancement of Deepfake technology, the generated counterfeit facial images have become increasingly challenging to distinguish. There is an urgent need for a more robust and convincing detection method. Current detection methods mainly operate in the spatial domain and transform the spatial domain into other domains for analysis. With the emergence of transformers, some researchers have also combined traditional convolutional networks with transformers for detection. This paper explores the artifacts left by Deepfakes in various domains and, based on this exploration, proposes a detection method that utilizes the steganalysis rich model to extract high-frequency noise to complement spatial features. We have designed two main modules to fully leverage the interaction between these two aspects based on traditional convolutional neural networks. The first is the multi-scale mixed feature attention module, which introduces artifacts from high-frequency noise into spatial textures, thereby enhancing the model's learning of spatial texture features. The second is the multi-scale channel attention module, which reduces the impact of background noise by weighting the features. Our proposed method was experimentally evaluated on mainstream datasets, and a significant amount of experimental results demonstrate the effectiveness of our approach in detecting Deepfake forged faces, outperforming the majority of existing methods.

关键词： Deepfakes Adaptation models Attention mechanisms Noise Transforms Transformers Feature extraction Internet Convolutional neural networks Faces

来源：评论

学校读者我要写书评

暂无评论

Byzantine Robust Federated Learning Scheme Based on Backdoor Triggers

引用

computers, Materials & Continua 2024年第5期79卷 2813-2831页

作者： Zheng Yang Ke Gu Yiming Zuo School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China

Federated learning is widely used to solve the problem of data decentralization and can provide privacy protectionfor data owners. However, since multiple participants are required in federated learning, this allows attackers tocompromise. Byzantine attacks pose great threats to federated learning. Byzantine attackers upload maliciouslycreated local models to the server to affect the prediction performance and training speed of the global model. Todefend against Byzantine attacks, we propose a Byzantine robust federated learning scheme based on backdoortriggers. In our scheme, backdoor triggers are embedded into benign data samples, and then malicious localmodels can be identified by the server according to its validation dataset. Furthermore, we calculate the adjustmentfactors of local models according to the parameters of their final layers, which are used to defend against datapoisoning-based Byzantine attacks. To further enhance the robustness of our scheme, each localmodel is weightedand aggregated according to the number of times it is identified as malicious. Relevant experimental data showthat our scheme is effective against Byzantine attacks in both independent identically distributed (IID) and nonindependentidentically distributed (non-IID) scenarios.

关键词： Federated learning Byzantine attacks backdoor triggers

来源：评论

学校读者我要写书评

暂无评论

Application of Adaptive Whale Optimization Algorithm Based BP Neural Network in RSSI Positioning

引用

Journal of Beijing Institute of technology 2024年第6期33卷 516-529页

作者： Duo Peng Mingshuo Liu Kun Xie School of Computer and Communication Lanzhou University of TechnologyLanzhou 730050China

The paper proposes a wireless sensor network(WSN)localization algorithm based on adaptive whale neural network and extended Kalman filtering to address the problem of excessive reliance on environmental parameters A and signal constant n in traditional signal propagation path loss *** algorithm utilizes the adaptive whale optimization algorithm to iteratively optimize the parameters of the backpropagation(BP)neural network,thereby enhancing its prediction *** address the issue of low accuracy and large errors in traditional received signal strength indication(RSSI),the algorithm first uses the extended Kalman filtering model to smooth the RSSI signal values to suppress the influence of noise and outliers on the estimation *** processed RSSI values are used as inputs to the neural network,with distance values as outputs,resulting in more accurate ranging ***,the position of the node to be measured is determined by combining the weighted centroid *** simulation results show that compared to the standard centroid algorithm,weighted centroid algorithm,BP weighted centroid algorithm,and whale optimization algorithm(WOA)-BP weighted centroid algorithm,the proposed algorithm reduces the average localization error by 58.23%,42.71%,31.89%,and 17.57%,respectively,validating the effectiveness and superiority of the algorithm.

关键词： wireless sensor network received signal strength neural network whale optimization algorithm adaptive weight factor extended Kalman filter

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：