共查询到20条相似文献，搜索用时 203 毫秒
1.
《Information processing & management》2023,60(5):103470
Previous federated recommender systems are based on traditional matrix factorization, which can improve personalized service but are vulnerable to gradient inference attacks. Most of them adopt model averaging to fit the data heterogeneity of federated recommender systems, requiring more training costs. To address privacy and efficiency, we propose an efficient federated item similarity model for the heterogeneous recommendation, called FedIS, which can train a global item-based collaborative filtering model to eliminate user feature dependencies. Specifically, we extend the neural item similarity model to the federated model, where each client only locally optimizes the shared item feature matrix. We then propose a fast-convergent federated aggregation method inspired by meta-learning to address heterogeneous user updates and accelerate the convergence of global training. Furthermore, we propose a two-stage perturbation method to protect both local training and transmission while reducing communication costs. Finally, extensive experiments on four real-world datasets validate that FedIS can provide more competitive performance on federated recommendations. Our proposed method also shows significant training efficiency with less performance degradation. 相似文献
2.
《Information processing & management》2023,60(1):103150
Nowadays, researchers are investing their time and devoting their efforts in developing and motivating the 6G vision and resources that are not available in 5G. Edge computing and autonomous vehicular driving applications are more enhanced under the 6G services that are provided to successfully operate tasks. The huge volume of data resulting from such applications can be a plus in the AI and Machine Learning (ML) world. Traditional ML models are used to train their models on centralized data sets. Lately, data privacy becomes a real aspect to take into consideration while collecting data. For that, Federated Learning (FL) plays nowadays a great role in addressing privacy and technology together by maintaining the ability to learn over decentralized data sets. The training is limited to the user devices only while sharing the locally computed parameter with the server that aggregates those updated weights to optimize a global model. This scenario is repeated multiple rounds for better results and convergence. Most of the literature proposed client selection methods to converge faster and increase accuracy. However, none of them has targeted the ability to deploy and select clients in real-time wherever and whenever needed. In fact, some mobile and vehicular devices are not available to serve as clients in the FL due to the highly dynamic environments and/or do not have the capabilities to accomplish this task. In this paper, we address the aforementioned limitations by introducing an on-demand client deployment in FL offering more volume and heterogeneity of data in the learning process. We make use of containerization technology such as Docker to build efficient environments using any type of client devices serving as volunteering devices, and Kubernetes utility called Kubeadm to monitor the devices. The performed experiments illustrate the relevance of the proposed approach and the efficiency of the deployment of clients whenever and wherever needed. 相似文献
3.
《Information processing & management》2023,60(2):103149
This paper develops a cooperative federated reinforcement learning (RL) strategy that enables two unmanned aerial vehicles (UAVs) to cooperate in learning and predicting the movements of an intelligent deceptive target in a given search area. The proposed strategy allows the UAVs to autonomously cooperate, through information exchange of the gained experience to maximize the target detection performance and accelerate the learning speed while maintaining privacy. Specifically, we consider a monitoring model that includes a search area, a charging station, two cooperative UAVs, an intelligent deceptive uncertain moving target, and a fake (false) target. Each UAV is equipped with a limited-capacity rechargeable battery and a communication unit for exchanging the gained experience. The problem of maximizing the detection probability of the uncertain deceptive target using cooperative UAVs is mathematically modeled as a search-benefit maximization problem, which is then reformulated as a Markov decision process (MDP) due to the uncertainty nature of the problem. Because there is no prior information on the targets’ movement, a cooperative RL, is utilized to tackle the problem. The proposed cooperative RL-based algorithm is a distributed collaborative mechanism that enables the two UAVs, i.e., agents, to individually interact with the operating environment and maximize their cumulative rewards by converging to a shared policy while achieving privacy. Simulation results indicate that a cooperative RL-based dual UAV system can noticeably improve the target detection probability, reduce the detection performance, and accelerate the learning speed. 相似文献
4.
《Journal of The Franklin Institute》2022,359(11):5634-5657
This paper develops a new dual ML-ADHDP method to solve the optimal consensus problem (OCP) of a class of heterogeneous discrete-time nonlinear multi-agent systems (MASs) with unknown dynamics and time delay. A hierarchical and distributed control strategy is used to transform the original problem into nonlinear model reference adaptive control (MRAC) problems and an OCP of virtual linear MASs. For the nonlinear MRAC problems, a new multi-layer action-dependent heuristic dynamic programming (ML-ADHDP) method is developed to overcome the unknown dynamics and neural network estimation errors, which has higher control accuracy. In order to solve the OCP of virtual linear MASs and improve the convergence speed, a new multi-layer performance index is proposed. Then the ML-ADHDP method is used to solve the coupled Hamiltonian–Jacobi–Bellman equation and obtain the optimal virtual control. Theoretical analysis proves that the original MASs can achieve Nash equilibrium, and simulation results show that the developed dual ML-ADHDP method ensures better convergence speed and higher control accuracy of original MASs. 相似文献
5.
本文从宁波S公司案例背景入手,介绍采用平衡计分卡对宁波S股份有限公司进行业绩评价。作者建立了评价模型,同时选择主成分分析法对S公司业绩指标进行筛选,最后分别从财务、客户、内部流程、学习和创新四个方面选择了一套适合于S公司当前业绩评价的指标体系,通过专家打分和加权平均方法计算出指标数值,得出S公司当前的业绩评价结论,并提出有效利用平衡计分卡对S公司进行业绩评价的建议。 相似文献
6.
《Information processing & management》2023,60(4):103153
Few-Shot Event Classification (FSEC) aims at assigning event labels to unlabeled sentences when limited annotated samples are available. Existing works mainly focus on using meta-learning to overcome the low-resource problem that still requires abundant held-out classes for model learning and selection. Thus we propose to deal with the low-resource problem by utilizing prompts. Further, existing methods suffer from severe trigger biases that may result in ignorance of the context. That is, the correct classifications are gained by looking at only the triggers, which hurts the model’s generalization ability. Thus, we propose a knowledgeable augmented-trigger prompt FSEC framework (AugPrompt), which can overcome the bias issues and alleviates the classification bottleneck brought by insufficient data. In detail, we first design an External Knowledge Injection (EKI) module to incorporate an external knowledge base (Related Words) for trigger augmentation. Then, we propose an Event Prompt Generation (EPG) module to generate appropriate discrete prompts for initializing the continuous prompts. After that, we propose an Event Prompt Tuning (EPT) module to automatically search prompts in the continuous space for FSEC and finally predict the corresponding event types of the inputs. We conduct extensive experiments on two public English datasets for FSEC, i.e., FewEvent and RAMS. The experimental results show the superiority of our proposal over the competitive baselines, where the maximum accuracy increase compared to the strongest baseline reaches 10.8%. 相似文献
7.
基于最小二乘支持向量机的数据挖掘应用研究 总被引：6，自引：0，他引：6
随着数据仓库技术、联机分析技术的发展。基于数据库的数据挖掘已成为一种重要的数据处理手段。最小二乘支持向量机作为一种新的机器学习方法。具有全局收敛性和良好的泛化能力。本文将其应用于数据挖掘的分类与预测研究。通过棱函数的选择及参数优化，并结合支持向量机、多层感知器神经网络模型及判别分析方法进行比较研究，证明最小二乘支持向量机作为一种有效的数据挖掘算法具有较高精度。 相似文献
8.
This article addresses the issue of extracting contexts and answers of questions from posts of online discussion forums. In previous work, general-purpose graphical models have been employed without any customization to this specific extraction problem. Instead, in this article, we propose a unified approach to context and answer extraction by customizing the structural support vector machine method. The customization enables our proposal to explore various relations among sentences of posts and complex structures of threads. We design new inference algorithms to find or approximate the most violated constraint by utilizing the specific structure of forum threads, which enables us to efficiently find the global optimum of the customized optimizing problem. We also optimize practical performance measures by varying loss functions. Experimental results show that our methods are both promising and flexible. 相似文献
9.
Despite keen interest in long-term strategic outsourcing and attention to factors affecting outsourcing success, the examination on relational performance, i.e., the difference between a vendor's performance with a particular client and that with its average client base, can be hardly found. This study adopts a relational view to offshore information systems (IS) outsourcing from a vendor's perspective to explore the source of relational performance. Results highlight the importance of client-specific capabilities and trust as a self-enforcing governance mechanism in a vendor's relational performance in terms of service quality. Project management and client-specific capabilities act as substitute for each other in affecting relational service quality. In addition, while trust and learning about client contribute to client-specific capabilities, trust is also positively related to learning about client. These findings enrich our understanding of the source of outsourcing relational performance and contribute to the literatures on vendors’ capabilities in IS outsourcing. 相似文献
10.
《Information processing & management》2022,59(3):102901
Deep learning methods have been widely applied for disease diagnosis on resting-state fMRI (rs-fMRI) data, but they are incapable of investigating global relationships between different brain regions as well as ignoring the interpretability. To address these issues, this paper presents a new graph neural network framework for brain disease diagnosis via jointly learning global relationships and selecting the most discriminative brain regions. Specifically, we first design a self-attention structure learning to capture the global interactions between brain regions for achieving diagnosis effectiveness, and theoretically integrate a feature selection method to reduce the noise influence as well as achieve interpretability. Experiment results on three neurological diseases datasets show the effectiveness of our method, compared to the comparison methods, in terms of diagnostic performance and interpretability. 相似文献
11.
[目的/意义] 在资源质量参差不齐的虚拟学习社区中,通过度量知识贡献者的信誉来间接判断资源质量,有利于解决用户的资源选择难题。[方法/过程] 采用文献分析法确定了虚拟学习社区用户信任知识贡献者的主要影响因素,分析了虚拟学习社区用户的行为结构与信任影响因素之间的关系,并在此基础上通过问卷调查法构建了虚拟学习社区知识贡献者信誉评价指标体系,最后将评价指标体系应用于"计算机技术论坛"中进行社区可信用户识别实验。[结果/结论] 研究发现,知识贡献者的信誉评价可从用户的权威性和专业知识能力两方面进行,且基于信誉评价指标体系的信誉度量方式具有较高的可信用户识别性能。 相似文献
12.
To reduce information technology (IT) development costs, more firms have begun to outsource IT-related activities by partnering with IT vendors. As knowledge is a valuable asset in IT development, knowledge sharing between vendors and business clients becomes critical. However, the motivation behind IT vendors’ willingness to share knowledge with client firms is not sufficiently understood. To shed light on the nature of knowledge sharing within vendor–client partnerships, we examine the influence of performance feedback and managerial mindset on vendors’ motivation to share knowledge with their clients. We adopt a multi-method approach involving both a scenario-based field experiment with 164 vendor managers (Study 1) and a field survey of 112 vendor managers involved in IT development (Study 2). We find that when vendors’ performance exceeds their aspiration levels, they are motivated to share knowledge with clients. Such motivational effects are more pronounced for vendor managers exhibiting abstract mindsets. Our study is of significant value to researchers and practitioners, affording both groups a keener, deeper, and more robust appreciation for how knowledge sharing in vendor–client partnerships can be managed more effectively. 相似文献
13.
《Information processing & management》2023,60(2):103235
Imbalanced sample distribution is usually the main reason for the performance degradation of machine learning algorithms. Based on this, this study proposes a hybrid framework (RGAN-EL) combining generative adversarial networks and ensemble learning method to improve the classification performance of imbalanced data. Firstly, we propose a training sample selection strategy based on roulette wheel selection method to make GAN pay more attention to the class overlapping area when fitting the sample distribution. Secondly, we design two kinds of generator training loss, and propose a noise sample filtering method to improve the quality of generated samples. Then, minority class samples are oversampled using the improved RGAN to obtain a balanced training sample set. Finally, combined with the ensemble learning strategy, the final training and prediction are carried out. We conducted experiments on 41 real imbalanced data sets using two evaluation indexes: F1-score and AUC. Specifically, we compare RGAN-EL with six typical ensemble learning; RGAN is compared with three typical GAN models. The experimental results show that RGAN-EL is significantly better than the other six ensemble learning methods, and RGAN is greatly improved compared with three classical GAN models. 相似文献
14.
《Information processing & management》2022,59(2):102839
Anomalous data are such data that deviate from a large number of normal data points, which often have negative impacts on various systems. Current anomaly detection technology suffers from low detection accuracy, high false alarm rate and lack of labeled data. Anomaly detection is of great practical importance as an effective means to detect anomalies in the data and provide important support for the normal operation of various systems. In this paper, we propose an anomaly detection classification model that incorporates federated learning and mixed Gaussian variational self-encoding networks, namely MGVN. The proposed MGVN network model first constructs a variational self-encoder using a mixed Gaussian prior to extracting features from the input data, and then constructs a deep support vector network with the mixed Gaussian variational self-encoder to compress the feature space. The MGVN finds the minimum hypersphere to separate the normal and abnormal data and measures the abnormal fraction by calculating the Euclidean distance between the data features and the hypersphere center. Federated learning is finally incorporated with MGVN (FL-MGVN) to effectively address the problems that multiple participants collaboratively train a global model without sharing private data. The experiments are conducted on the benchmark datasets such as NSL-KDD, MNIST and Fashion-MNIST, which demonstrate that the proposed FL-MGVN has higher recognition performance and classification accuracy than other methods. The average AUC on MNIST and Fashion-MNIST reached 0.954 and 0.937, respectively. 相似文献
15.
《Journal of The Franklin Institute》2023,360(12):8669-8703
In the traditional distributed machine learning scenario, the user’s private data is transmitted between clients and a central server, which results in significant potential privacy risks. In order to balance the issues of data privacy and joint training of models, federated learning (FL) is proposed as a particular distributed machine learning procedure with privacy protection mechanisms, which can achieve multi-party collaborative computing without revealing the original data. However, in practice, FL faces a variety of challenging communication problems. This review seeks to elucidate the relationship between these communication issues by methodically assessing the development of FL communication research from three perspectives: communication efficiency, communication environment, and communication resource allocation. Firstly, we sort out the current challenges existing in the communications of FL. Second, we have collated FL communications-related papers and described the overall development trend of the field based on their logical relationship. Ultimately, we discuss the future directions of research for communications in FL. 相似文献
16.
Most work in the design of learning technology uses click-streams as their primary data source for modelling & predicting learning behaviour. In this paper we set out to quantify what, if any, advantages do physiological sensing techniques provide for the design of learning technologies. We conducted a lab study with 251 game sessions and 17 users focusing on skill development (i.e., user's ability to master complex tasks). We collected click-stream data, as well as eye-tracking, electroencephalography (EEG), video, and wristband data during the experiment. Our analysis shows that traditional click-stream models achieve 39% error rate in predicting learning performance (and 18% when we perform feature selection), while for fused multimodal the error drops up to 6%. Our work highlights the limitations of standalone click-stream models, and quantifies the expected benefits of using a variety of multimodal data coming from physiological sensing. Our findings help shape the future of learning technology research by pointing out the substantial benefits of physiological sensing. 相似文献
17.
《Journal of The Franklin Institute》2023,360(9):6194-6210
Privacy has raised considerable concerns recently, especially with the advent of information explosion and numerous data mining techniques to explore the information inside large volumes of data. These data are often collected and stored across different institutions (banks, hospitals, etc.), or termed cross-silo. In this context, cross-silo federated learning has become prominent to tackle the privacy issues, where only model updates will be transmitted from institutions to servers without revealing institutions’ private information. In this paper, we propose a cross-silo federated XGBoost approach to solve the federated anomaly detection problem, which aims to identify abnormalities from extremely unbalanced datasets (e.g., credit card fraud detection) and can be considered a special classification problem. We design two privacy-preserving mechanisms that are tailored to the federated XGBoost: anonymity based data aggregation and local differential privacy. In the anonymity based data aggregation scenario, we cluster data into different groups and using a cluster-level data feature to train the model. In the local differential privacy scenario, we design a federated XGBoost framework by incorporate differential privacy in parameter transmission. Our experimental results over two datasets show the effectiveness of our proposed schemes compared with existing methods. 相似文献
18.
The name ambiguity problem is especially challenging in the field of bibliographic digital libraries. The problem is amplified when names are collected from heterogeneous sources. This is the case in the Scholarometer system, which performs bibliometric analysis by cross-correlating author names in user queries with those retrieved from digital libraries. The uncontrolled nature of user-generated annotations is very valuable, but creates the need to detect ambiguous names. Our goal is to detect ambiguous names at query time by mining digital library annotation data, thereby decreasing noise in the bibliometric analysis. We explore three kinds of heuristic features based on citations, metadata, and crowdsourced topics in a supervised learning framework. The proposed approach achieves almost 80% accuracy. Finally, we compare the performance of ambiguous author detection in Scholarometer using Google Scholar against a baseline based on Microsoft Academic Search. 相似文献
19.
《Information processing & management》2023,60(2):103205
Personalized recommender systems have been extensively studied in human-centered intelligent systems. Existing recommendation techniques have achieved comparable performance in predictive accuracy; however, the trade-off between recommendation accuracy and diversity poses new challenges, as diversification may lead to accuracy loss, whereas it can solve the over-fitting problem and enhance the user experience. In this study, we propose a heuristic optimization-based recommendation model that jointly optimizes accuracy and diversity performance by obtaining a set of optimized solutions. To establish the best accuracy-diversity balance, a novel trajectory-reinforcement-based bacterial colony optimization algorithm was developed. The improved bacterial colony optimization algorithm was comprehensively evaluated by comparing it with eight popular and state-of-the-art algorithms on ten benchmark testing problems with different degrees of complexity. Furthermore, an optimization-based recommendation model was applied to a real-world recommendation dataset. The results demonstrate that the improved bacterial colony optimization algorithm achieves the best overall performance for benchmark problems in terms of convergence and diversity. In the real-world recommendation task, the proposed approach improved the diversity performance by 1.62% to 8.62% while maintaining superior (1.88% to 40.32%) accuracy performance. Additionally, the proposed personalized recommendation model can provide a set of nondominated solutions instead of a single solution to accommodate the ever-changing preferences of users and service providers. Therefore, this work demonstrates the excellence of an optimization-based recommendation approach for solving the accuracy-diversity trade-off. 相似文献
20.
《Information processing & management》2022,59(3):102863
Few-shot intent recognition aims to identify user’s intent from the utterance with limited training data. A considerable number of existing methods mainly rely on the generic knowledge acquired on the base classes to identify the novel classes. Such methods typically ignore the characteristics of each meta task itself, resulting in the inability to make full use of limited given samples when classifying unseen classes. To deal with such issues, we propose a Contrastive learning-based Task Adaptation model (CTA) for few-shot intent recognition. In detail, we leverage contrastive learning to help achieve task adaptation and make full use of the limited samples of novel classes. First, a self-attention layer is employed in the task adaptation module, which aims to establish interactions between samples of different categories so that new representations are task-specific rather than relying entirely on the base classes. Then, the contrastive-based loss functions and the semantics of the label name are respectively used for reducing the similarity between sample representations in different categories while increasing it in the same categories. Experimental results on a public dataset OOS verify the effectiveness of our proposal by beating the competitive baselines in terms of accuracy. Besides, we conduct the cross-domain experiments on three datasets, i.e., OOS, SNIPS as well as ATIS. We find that CTA gains obvious improvements in terms of accuracy in all cross-domain experiments, indicating that it has a better generalization ability than other competitive baselines in both cross-domain and single-domain settings. 相似文献