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FREDIS: A Fusion Framework of Refinement and Disambiguation for Unreliable Partial Label Learning 40

FREDIS: A Fusion Framework of Refinement and Disambiguation ...

引用

40th international conference on Machine Learning

作者： Qiao, Congyu Xu, Ning Lv, Jiaqi Ren, Yi Geng, Xin Southeast Univ Sch Comp Sci & Engn Nanjing 210096 Peoples R China RIKEN Ctr Adv Intelligence Project Beijing Peoples R China Zhejianglab Res Ctr Healthcare Data Sci Hangzhou Peoples R China

To reduce annotation difficulty, Partial label learning (PLL) uses ambiguous annotations with candidate labels instead of the exact correct label. It assumes the candidate label set contains the correct label, inducing disambiguation, which is commonly adopted in PLL methods. However, this assumption is impractical as no one could guarantee the existence of the correct label in the candidate label set under real-world scenarios. Therefore, Unreliable Partial Label Learning (UPLL) is investigated where the correct label of each example may not exist in the candidate label set. In this paper, we propose a fusion framework of refinement and disambiguation named FREDIS to handle the UPLL problem. Specifically, with theoretical guarantees, not only does disambiguation move incorrect labels from candidate labels to non-candidate labels but also refinement, an opposite procedure, moves correct labels from non-candidate labels to candidate labels. Besides, we prove that the classifier trained by our framework could eventually approximate the Bayes optimal classifier. Extensive experiments on widely used benchmark datasets validate the effectiveness of our proposed framework. Source code is available at https://***/palm-ml/fredis.

关键词： Machine learning

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The Intelligent Smart City Deployment Via Artificial intelligence Software Networking 2

The Intelligent Smart City Deployment Via Artificial Intelli...

引用

2nd international conference on Advance computing and Innovative Technologies in Engineering, ICACITE 2022

作者： Gupta, Ayan Das Kishore, K. Prasad, Arun B Kannadasan, B. Narayana, M Srinivasa Singh, Prabhdeep Chandernagore Government College Postgraduate Department of Geography West Bengal Hooghly India Chandernagore Government College Affiliated to the University of Burdwan India Voorhees College Department of Commerce Vellore India Institute of Law Nirma University Gujarat Ahmedabad India B.S.Abdur Rahman Crescent Institute of Science and Technology Department of Civil Engineering Vandalur Chennai India Kl Business School K L University Koneru Lakshmaiah Education Foundation Vijayawada India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9781665437899

The Software Defined Network (SDN) structure is a novel approach to network management. Switches in SDN do not process incoming packets in the same way that they do in a traditional network computing environment. They look in the linking tables for incoming packets, and if none are found, they are sent to the control system for processing, which is the SDN's operating system. The most serious threat to cyber security in an SDN network is a Spread Denial of Service (Dodos) attack. The attack will take place at the network or application layers of the infected devices that are linked to the network. In this paper, an intelligent machine learning-based method for detecting whether incoming packets are infected or not is proposed Smart city activities seek to go beyond the limitations of traditional urban planning, which regulates critical infrastructures in silos, and to profit on the pervasiveness of data and services given by electronic technologies such as machine learning, the internet of things, affordable and big data. © 2022 IEEE.

关键词： Machine learning

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A Multi-Head Attention Mechanism Base Multi-Dimensional data Quality Evaluation Model 22

A Multi-Head Attention Mechanism Base Multi-Dimensional Data...

引用

7th international conference on big data and computing, ICBDC 2022

作者： Liu, Xiaobao Li, Qi Zhu, Shaosong Wang, Cong Meng, Lingzhen State Grid Shandong Electric Power Company Information and Communication Company Jinan China

ISBN: (纸本)9781450396097

High-quality power data is the basis for reliable operation of power systems, efficient data processing, and effective mining of the potential value of power data. How to use big data, artificial intelligence and other technologies to evaluate the quality of power data is a hot research topic in the field of electric power. At present, most of the power data quality evaluation methods are simple and lack the research of general data quality evaluation model. Therefore, this paper proposes a multi-dimensional data quality evaluation model based on a multi-head attention mechanism. The model measures multiple indicators such as completeness, accuracy, smoothness, and correlation. The corresponding methods are used to quantify these indicators to form a data quality evaluation index system oriented to multi-dimensional indicators;then, an application feedback mechanism based on a multi-head attention network is used to correct the calculation weights and score outputs, so as to achieve the evaluation of power data quality. Finally, the validation analysis is carried out based on the electricity data of a region in China. The experimental results show that the proposed method can effectively evaluate the quality of electric power data. © 2022 ACM.

关键词： Quality control

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Evaluation of data poisoning attacks on federated learning-based network intrusion detection system 24

Evaluation of data poisoning attacks on federated learning-b...

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24th IEEE international conference on High Performance computing and Communications, 8th IEEE international conference on data Science and Systems, 20th IEEE international conference on Smart City and 8th IEEE international conference on Dependability in Sensor, Cloud and big data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Zhang, Yuemeng Zhang, Yong Zhang, Zhao Bai, Haonan Zhong, Tianyi Song, Mei School of Electronic Engineering Beijing University of Posts and Telecommunications Beijing100876 China School of Computer and Information Technology Beijing Jiaotong University Beijing100876 China

ISBN: (纸本)9798350319934

Recently, federated learning-based network intrusion detection system (FL-based NIDS) has been considered as an essential tool to protect network security. It enables learning an effective intrusion detection model collaboratively without data privacy leakage. However, FL- based NIDS has exhibited inherent vulnerabilities on the data poisoning attacks launched by malicious clients. In this paper, we conduct the first systematic robustness evaluations of FL-based NIDS under data poisoning attack. Firstly, in consideration of the traffic domain constraints, we design the clean-label data poisoning attack against FL-based NIDS. Specifically, we propose an improved poisoned sample generation model based on Generative Adversarial Network, called PT-GAN, which optimizes with a new loss function that incorporates the feedback of the target intrusion detection model. The minimally-perturbed and correctly-labeled traffic samples generated by PT-GAN are then injected in the local training dataset to corrupt the intrusion detection model. Then, we explore the potential defense mechanism for these data poisoning attacks against FL- based NIDS and propose a novel defense method based on poisoned sample detection. Concretely, we propose the important neuron activations extraction method based on the layerwise relevance propagation method and then apply Oneclass-SVM to detect the poisoned sample. Experiments show that the proposed PT-GAN can degrade the performance of FL-based NIDS up to 28% on UNSW-NB15 dataset. We also demonstrate the robustness of our proposed defense methods against data poisoning attacks. © 2022 IEEE.

关键词： Network security

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Weather Forecasting and Analysis Based on Machine Learning 2

Weather Forecasting and Analysis Based on Machine Learning

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2022 2nd international conference on Artificial intelligence, big data and Algorithms, CAIBDA 2022

作者： Chen, Boyang Chen, Erqian Lang, Heran Shenzhen Middle School No.18 Shenzhong Street North Renmin Road Guangdong Province Shenzhen City518000 China School of Information Technology and Electrical Engineering The University of Queensland St LuciaQLD4072 Australia School of Computing Department National university of Singapore 21 Lower Kent Ridge Road 119077 Singapore

ISBN: (纸本)9783800760268

Weather forecasting has become a topic of increasing concern due to unnatural causes such as human activities. Although the weather forecasting methods in the past: traditional synoptic method and numerical prediction method can predict the change of the weather very accurately, but these two methods need to monitor and process a large amount of data, which consumes resources and manpower. When people just want to get the weather of a particular area, it's not worth it. Our study provides a new way to predict weather from small pieces of data. The data includes temperature, rainfall, wind direction, wind speed, humidity, pressure, cloud and sunshine which previous research did not use all of them. Firstly, a small part of data was obtained from reliable databases. Then this paper compared different methods (Drop the incomplete samples, using mean value of the column, K-nearest-neighbors) to select the best method for refining data sets. Then this paper compared different models (Random Forest Regression, Logistic Regression, Support Vector machine) to select the best model suitable for small data for machine learning. Finally, this paper formed tables, bar charts and broken line charts to show the weather prediction and evaluation of each model through data visualization, as well as the prediction of evaporation, rainfall and other future weather data. This study created an interactive web page to showcase the final result and make it easy for people to view at any time. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Weather forecasting

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Application strategy of big data and cloud computing in the Internet of things 2

Application strategy of big data and cloud computing in the ...

引用

2nd IEEE international conference on Artificial intelligence in Everything, AIE 2022

作者： Li, Mindong Shi, Yuhong Shandong Huanghe River Affairs Bureau Information Center Shandong China

ISBN: (数字)9781665474009

ISBN: (纸本)9781665474009

The Internet has become a new growth point of the country's future economy. Modern network is built by combining wireless technology, optical fiber transmission technology, computer network technology and computer software. In the future, with the expansion of the scale of sensor networks, the data obtained from sensors will consist of TB and Pb data. They include sensor layer, transport layer, integration layer and application layer. In this process, the combination of big data and cloud brings opportunities and challenges to the network. This paper describes the relevance and characteristics of big data and cloud in Intranet, laying the foundation for more effective network development. © 2022 IEEE.

关键词： big data

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Improving Public Transport Efficiency in Mauritius using Artificial intelligence and Internet of Things solutions 4

Improving Public Transport Efficiency in Mauritius using Art...

引用

4th IEEE international conference on Emerging Trends in Electrical, Electronic and Communications Engineering, ELECOM 2022

作者： Mahomed, Mouneer A. O. Oree, Vishwamitra Murdan, Anshu Prakash University of Mauritius Electrical and Electronic Engineering Department Reduit Mauritius

ISBN: (数字)9781665466974

ISBN: (纸本)9781665466974

Bus transport has been an important driver of the economic growth of Mauritius for decades. In recent years, it has been facing several challenges, such as competition from the Metro Express and an inability to upgrade its services. In addition, public buses still face inefficiencies that discourage the public to travel by them. These include bus drivers tending to drive slowly on purpose or dangerously fast resulting in their schedules at bus stops being unreliable. This paper exploits advanced technologies such as artificial intelligence, Internet of Things, cloud computing and big data analytics to enhance the efficiency of bus operations and provide a better service to the public. It presents the design and implementation of a microcontroller-based system that uses real-time data from onboard camera and GPS to monitor the condition of the road and the bus speed. The system can detect whether the bus is driving too fast or purposely too slow and upload pictures as proof. A web application is also developed to process data uploaded by the system on the cloud to provide the passengers with the exact location of the buses on a map so that they can better plan their trip. © 2022 IEEE.

关键词： Buses

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Cognition-aware Knowledge Graph Reasoning for Explainable Recommendation 23

Cognition-aware Knowledge Graph Reasoning for Explainable Re...

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16th international conference on Web Search and data Mining

作者： Bing, Qingyu Zhu, Qiannan Dou, Zhicheng Renmin Univ China Sch Informat Beijing Peoples R China Renmin Univ China Gaoling Sch Artificial Intelligence Beijing Peoples R China

ISBN: (纸本)9781450394079

Knowledge graphs (KGs) have been widely used in recommendation systems to improve recommendation accuracy and interpretability effectively. Recent research usually endows KG reasoning to find the multi-hop user-item connection paths for explaining why an item is recommended. The existing path-finding process is well designed by logic-driven inference algorithms, while there exists a gap between how algorithms and users perceive the reasoning process. Factually, human thinking is a natural reasoning process that can provide more proper and convincing explanations of why particular decisions are made. Motivated by the Dual Process Theory in cognitive science, we propose a cognition-aware KG reasoning model CogER for Explainable Recommendation, which imitates the human cognition process and designs two modules, i.e., System 1 (making intuitive judgment) and System 2 (conducting explicit reasoning), to generate the actual decision-making process. At each step during the cognition-aware reasoning process, System 1 generates an intuitive estimation of the next-step entity based on the user's historical behavior, and System 2 conducts explicit reasoning and selects the most promising knowledge entities. These two modules work iteratively and are mutually complementary, enabling our model to yield high-quality recommendations and proper reasoning paths. Experiments on three real-world datasets show that our model achieves better recommendation results with explanations compared with previous methods.

关键词： Recommendation Systems Explainable Recommendation Knowledge Graph Reasoning Reinforcement Learning

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A data Integration Approach to Estimating Personal Exposures to Air Pollution

A Data Integration Approach to Estimating Personal Exposures...

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2022 IEEE international conference on big data, big data 2022

作者： Thomas, Matthew L Shaddick, Gavin Topping, David Morrissey, Karyn Brannan, Thomas J. Diessner, Mike Bowyer, Ruth C. E. Siegert, Stefan Coe, Hugh Evans, James Benitez-Paez, Fernando Zidek, James V. University of Exeter Joint Centre for Excellence in Environmental Intelligence Exeter United Kingdom University of London School of Engineering Physical and Mathematical Sciences Royal Holloway Egham United Kingdom University of Manchester Department of Earth and Environmental Sciences Manchester United Kingdom University of Denmark Management and Economics Technical Department of Technology Lyngby Denmark Newcastle University School of Computing Newcastle United Kingdom Alan Turing Institute Ai for Science and Government London United Kingdom University of Exeter Department of Mathematics and Statistics Exeter United Kingdom University of Manchester Department of Geography Manchester United Kingdom University of British Columbia Department of Statistics Vancouver Canada

ISBN: (纸本)9781665480451

Globally, air pollution is the largest environmental risk to public health. In order to inform policy and target mitigation strategies there is a need to increase our understanding of the (personal) exposures experienced by different population groups. The data Integration Model for Exposures (DIMEX) integrates data on daily travel patterns and activities with measurements and models of air pollution using agent-based modelling to simulate the daily exposures of different population groups. Here we present the results of a case study using DIMEX to model personal exposures to PM2.5 in Greater Manchester, UK, and demonstrate its ability to explore differences in time activities and exposures for different population groups. DIMEX can also be used to assess the effects of reductions in ambient air pollution and when run with concentrations reduced to 5 μg/m3 (new WHO guidelines) lead to an estimated (mean) reduction in personal exposures between 2.7 and 3.1 μg/m3 across population (gender-age) groups. © 2022 IEEE.

关键词： data integration

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D3still: Decoupled Differential Distillation for Asymmetric Image Retrieval

D3still: Decoupled Differential Distillation for Asymmetric ...

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IEEE/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Xie, Yi Lin, Yihong Cai, Wenjie Xu, Xuemiao Zhang, Huaidong Du, Yong He, Shengfeng South China Univ Technol Guangzhou Peoples R China State Key Lab Subtrop Bldg Sci Guangzhou Peoples R China Ocean Univ China Qingdao Peoples R China Guangdong Prov Key Lab Computat Intelligence & Cy Shenzhen Peoples R China Minist Educ Key Lab Big Data & Intelligent Robot Beijing Peoples R China Singapore Management Univ Singapore Singapore

ISBN: (纸本)9798350353006

Existing methods for asymmetric image retrieval employ a rigid pairwise similarity constraint between the query network and the larger gallery network. However, these one-to-one constraint approaches often fail to maintain retrieval order consistency, especially when the query network has limited representational capacity. To overcome this problem, we introduce the Decoupled Differential Distillation (D3still) framework. This framework shifts from absolute one-to-one supervision to optimizing the relational differences in pairwise similarities produced by the query and gallery networks, thereby preserving a consistent retrieval order across both networks. Our method involves computing a pairwise similarity differential matrix within the gallery domain, which is then decomposed into three components: feature representation knowledge, inconsistent pairwise similarity differential knowledge, and consistent pairwise similarity differential knowledge. This strategic decomposition aligns the retrieval ranking of the query network with the gallery network effectively. Extensive experiments on various benchmark datasets reveal that D3still surpasses state-of-the-art methods in asymmetric image retrieval. Code is available at https://***/SCY-X/D3still.

关键词： Asymmetric Image Retrieval Knowledge Distillation

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