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Ensuring Minority Group Rights in Social IoT with Fairness-aware Federated Graph Node Classification 21

Ensuring Minority Group Rights in Social IoT with Fairness-a...

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21st IEEE international Symposium on Parallel and Distributed Processing with Applications, 13th IEEE international conference on Big data and Cloud Computing, 16th IEEE international conference on Social Computing and Networking and 13th international conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Mao, Qinghua Lin, Xi Li, Gaolei Chen, Lixing Liu, Yuchen Qi, Yuxin Li, Jianhua Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai China Shanghai Key Laboratory of Integrated Administration Technologies for Information Security Shanghai China North Carolina State University Department of Computer Science NC United States

ISBN: (纸本)9798350329223

Graph neural networks (GNNs) are a powerful tool to predict the categories of IoT nodes for providing diverse high-quality services in the social Internet of Things (SIoT). Intuitively, SIoT could be divided into several communities based on similar interests and mutual trust, whereas interaction between cross-community nodes may intrigue data privacy. With the popularity of federated learning (FL), a global GNN model can be collaboratively trained by exploiting knowledge from multiple parties in a privacy-preserving way. However, since node attributes across different SIoT communities are usually multi-source and heterogeneous, the performance of an optimum global model may exhibit potential bias against minority social groups, i.e, unfairness, thereby undermining the efficiency of node classification and social behavior analysis. To address this problem, we propose PFedNC, a proportional fairness-aware federated learning framework for node classification in SIoT system. Specifically, we incorporate proportional fairness and related Nash bargaining solution into FL optimization objective, which imposes more fairness while maintaining a similar average performance. Moreover, we design a class-balanced re-weighting loss function to mitigate imbalanced class distribution caused by non-IID node distribution. Extensive experiments on two node-level SIoT simulation datasets demonstrate that our PFedNC framework effectively encourages performance fairness among social groups and achieves a favorable tradeoff between fairness and efficiency. © 2023 IEEE.

关键词： Internet of things

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Size Measurement of stone Images Based on Improved UNet 21

Size Measurement of Stone Images Based on Improved UNet

引用

21st IEEE international conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2022

作者： Chen, Ning Ma, Xinkai Peng, Jun Jin, Shangzhu Wu, Xiao Wu, Yan Luo, Haixia College of Intelligent Technology and Engineering Chongqing University of Science and Technology Chongqing401331 China

ISBN: (纸本)9781665490849

To address the current problem of calculating stone grain size in the field of sand and gravel aggreg es, image segmentation of stone targets is achieved by stone images, and the grain length of stone targets is finally obtained. By pre-processing the target stone images, the pre-processed stone images are segmented and predicted using deep learning image processing techniques, and the predicted result maps are subjected to morphological and image binarization operations for subsequent stone particle size calculation. The algorithm is implemented to delineate the assignment of individual stone regions and to find the boundary coordinate points of individual stone image regions, and to calculate the image grain size length of stones from them. The true grain length of the stone is calculated by the proportional mapping relationship between the camera and the pixel length of the stone taken and the real stone length. Through experiments, this operation procedure can segment and calculate the grain length of stones quickly and accurately. © 2022 IEEE.

关键词： Mapping

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data-Driven learning Based Secrecy Rate Optimization for RIS-Assisted Secure Wireless Networks: A Multi-Armed Bandit Approach

Data-Driven Learning Based Secrecy Rate Optimization for RIS...

引用

Consumer Communications and Networking conference, CCNC IEEE

作者： Hongshan Mao Hao Xu Department of Electrical and Biomedical Engineering University of Nevada Reno Reno US

In this paper, the secrecy rate optimization problem for Reconfigurable intelligent Surface (RIS) assisted secure wireless networks is investigated. However, to fully stimulate the potential of RIS, a novel algorithm is needed to optimize the security of RIS-assisted wireless networks. Specifically, a secrecy optimization for multiple RIS-assisted wireless networks with uncertain environments as well as eavesdropping is given first. Then, to solve the formulated optimal secrecy problem, the Multi-Armed Bandit (MAB) has been adopted and further integrated with a deep reinforcement learning algorithm along with Deep Deterministic Policy Gradients (DDPG) technique. This developed algorithm can effectively enhance the secrecy of RIS-assisted wireless networks, i.e. minimizing secrecy outage probability, etc., not only through selecting appropriate RIS, but also by optimizing transmit power and phase shift matrix in selected RIS. Eventually, the effectiveness of the developed algorithm has been demonstrated through a series of numerical simulations.

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HBC: Combining Lossy and Lossless Hybrid Bilayer Compression Framework on Time-Series data 21

HBC: Combining Lossy and Lossless Hybrid Bilayer Compression...

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作者： Lu, Wanying Liu, Liang Zhai, Wenbin Chen, Haoyuan Liu, Yulei Nanjing University of Aeronautics and Astronautics China

ISBN: (纸本)9798350329223

The popularization and application of the Internet of Things (IoT) technology has brought massive time series data, which puts forward higher requirements for data compression technology. At present, most existing compression methods use only a single lossy or lossless compression algorithm to perform data compression. Furthermore, traditional lossy compression methods usually adopt a fixed error threshold. However, in practical applications, users have different accuracy requirements for time series data in different numerical ranges. In this paper, we design a Hybrid Bilayer Compression (HBC) framework, which consists of a data accuracy-aware lossy compression layer and a data feature-aware lossless compression layer. First, the original time series data is lossy compressed on the top layer of HBC, where the error threshold can be adaptively adjusted according to the user's accuracy requirements. Then, based on the features of lossy-compressed data, we use supervised learning to select the optimal lossless compression algorithm from the algorithm pool to further compress the data. Experimental results show that compared with three state-of-the-art compression algorithms, HBC reduces the storage space by 52.1%, 91.66%, and 92.27%, respectively. © 2023 IEEE.

关键词： Digital storage

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Hypergraph Ego-networks and Their Temporal Evolution 21

Hypergraph Ego-networks and Their Temporal Evolution

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21st IEEE international conference on data Mining (IEEE ICDM)

作者： Comrie, Cazamere Kleinberg, Jon Cornell Univ Ithaca NY 14853 USA

ISBN: (纸本)9781665423984

Interactions involving multiple objects simultaneously are ubiquitous across many domains. The systems these interactions inhabit can be modelled using hypergraphs, a generalization of traditional graphs in which each edge can connect any number of nodes. Analyzing the global and static properties of these hypergraphs has led to a plethora of novel findings regarding how these modelled system are structured. However, less is known about the localized structure of these systems and how they evolve over time. In this paper, we propose the study of hypergraph ego-networks, a structure that can be used to model higher-order interactions involving a single node. We also propose the temporal reconstruction of hypergraph ego-networks as a benchmark problem for models that aim to predict the local temporal structure of hypergraphs. By combining a deep learning binary classifier with a hill-climbing algorithm, we will present a model for reconstructing hypergraph ego-networks by incorporating structural patterns found across multiple domains.

关键词： data mining deep learning ego networks hypergraphs machine learning

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Multi-Objective Distributional Reinforcement learning for Large-Scale Order Dispatching 21

Multi-Objective Distributional Reinforcement Learning for La...

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21st IEEE international conference on data Mining (IEEE ICDM)

作者： Zhou, Fan Lu, Chenfan Tang, Xiaocheng Zhang, Fan Qin, Zhiwei Ye, Jieping Zhu, Hongtu Shanghai Univ Finance & Econ Shanghai Peoples R China DiDi AI Labs Mountain View CA USA

ISBN: (纸本)9781665423984

The aim of this paper is to develop a multi-objective distributional reinforcement learning framework for improving order dispatching on large-scale ride-hailing platforms. Compared with traditional RL-based approaches that focus on drivers' income, the proposed framework also accounts for the spatiotemporal difference between the supply and demand networks. Specifically, we model the dispatching problem as a two-objective Semi-Markov Decision Process (SMDP) and estimate the relative importance of the two objectives under some unknown existing policy via Inverse Reinforcement learning (IRL). Then, we combine Implicit Quantile Networks (IQN) with the traditional Deep Q-Networks (DQN) to jointly learn the two return distributions and adjusting their weights to refine the old policy through on-line planning and achieve a higher supply-demand coherence of the platform. We conduct large-scale dispatching experiments to demonstrate the remarkable improvement of proposed approach on the platform's efficiency.

关键词： Order Dispatching Multi-Objective Reinforcement learning Distributional Reinforcement learning Inverse Reinforcement learning Semi-Markov Decision Process

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A Survey of Speech Recognition Based on Deep learning 5

A Survey of Speech Recognition Based on Deep Learning

引用

5th international conference on Natural Language Processing, ICNLP 2023

作者： Liu, Youyao Chen, Jiale Gao, Jialei Gai, Shihao Xi'an University of Posts and Telecommunication School of Electronic Engineering Xi'an China

ISBN: (纸本)9798350302219

Artificial intelligence is the vane leading the world's scientific and technological development and future lifestyle change in the 21st century, and speech recognition, as one of the indispensable technical means, is inevitably the focus of human attention. There are two problems in traditional speech recognition: first, speech recognition technology cannot be significantly improved, and second, speech recognition systems cannot accurately extract data and features. In order to solve these problems, this paper first compares the traditional speech recognition GMM-HMM model and establishes a DNN-HMM model, which proposes a method to improve the speed of speech recognition and greatly improves the recognition rate. However, DNN-HMM lacks the ability to use historical information to assist in the current task, and a second model is proposed on the basis of this problem, that is, the LstM model is used to solve the problem of insufficient contextual information, which further improves the speech recognition ability. Then, in order to solve the problem of long memory loss and speed up training, the Transformer model is cited, and in order to solve the problem that the traditional language model can only predict the next word in one direction, the BERT model, which has a bidirectional language model, is invoked. © 2023 IEEE.

关键词： Speech recognition

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Machine learning and Deep learning Methods used in Safety Management of Nuclear Power Plants: A Survey 21

Machine Learning and Deep Learning Methods used in Safety Ma...

引用

21st IEEE international conference on data Mining (IEEE ICDM)

作者： Shi, Yong Xue, Xiaodong Qu, Yi Xue, Jiayu Zhang, Linzi Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing Peoples R China Univ Nebraska Coll Informat Sci & Technol Omaha NE 68182 USA Univ Chinese Acad Sci Sch Econ & Management Beijing Peoples R China

ISBN: (纸本)9781665424271

The nuclear power industry is currently a strategic sector in the national economy, along with nuclear energy being considered to be an essential source of national power supply and security. Under such circumstances, nuclear power plants (NPPs) have been constructed globally in recent decades, providing stable and large amounts of electricity to many countries and regions for quite a long time. However, due to the specialty of NPPs, safety management is always the top priority in their daily operations, like fault diagnosis and monitoring. At the same time, with the great development of artificial intelligence, machine learning and deep learning methods have been infiltrating lots of disciplines and resulting in more intelligent transformations in real industries. By the strength of better performance, machine learning and deep learning models have been introduced into research and practice of safety management in NPPs, not only producing more academic papers but also ensuring stable and safe operations of NPPs. Focusing on the safety management of NPPs, this article starts with the common trend of data analytics, also the evolving process of algorithms applied in academia and real practice, which is from model-driven methods to data-driven approaches. Then detailed applications of conventional machine learning, advanced deep learning and other related intelligent approaches used in the safety management of NPPs are comprehensively categorized and reviewed. Further, we make necessary summaries and discussions, proposing new ideas and perspectives to better promote the theoretical and practical development of safety management in NPPs.

关键词： Safety management Nuclear power plant Machine learning Deep learning data-driven methods

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DeepMEND: Reliable and Scalable Network Metadata Geolocation from Base station Positions 21

DeepMEND: Reliable and Scalable Network Metadata Geolocation...

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21st Annual IEEE international conference on Sensing, Communication, and Networking, SECON 2024

作者： Martínez-Durive, Orlando E. Bakirtzis, stefanos Ziemlicki, Cezary Zhang, Jie Wassell, Ian James Fiore, Marco IMDEA Networks Institute Spain Universidad Carlos III de Madrid Spain University of Cambridge United Kingdom SENSE Orange Innovation France University of Sheffield Dept. of Electronic and Electrical Engineering United Kingdom

ISBN: (纸本)9798331519186

Metadata geolocation, i.e., mapping information collected at a cellular Base station (BS) to the geographical area it covers, is a central operation in the production of statistics from mobile network measurements. This task requires modeling the probability that a device attached to a BS is at a specific location, and is presently addressed with simplistic approximations based on Voronoi tessellations. As we show, Voronoi cells exhibit poor accuracy compared to real-world geolocation data, which can, in turn, reduce the reliability of research results. We propose a new approach for data-driven metadata geolocation based on a teacher-student paradigm that combines probabilistic inference and deep learning. Our DEEPMEND model: (i) only needs BS positions as input, exactly like Voronoi tessellations;(ii) produces geolocation maps that are 56% and 33% more accurate than legacy Voronoi and their state-of-the-art VoronoiBoost calibration, respectively;and, (iii) generates geolocation data for thousands of BSs in minutes. We assess its accuracy against real-world multi-city geolocation data of 5,947 BSs provided by a network operator, and demonstrate the impact of its enhanced metadata geolocation on two applications use cases. © 2024 IEEE.

关键词： Metadata

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DRIFT LENS: Real-time unsupervised Concept Drift detection by evaluating per-label embedding distributions 21

DRIFT LENS: Real-time unsupervised Concept Drift detection b...

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21st IEEE international conference on data Mining (IEEE ICDM)

作者： Greco, Salvatore Cerquitelli, Tania Politecn Torino Dept Control & Comp Engn Turin Italy

ISBN: (纸本)9781665424271

Despite the significant improvements made by deep learning models, their adoption in real-world dynamic applications is still limited. Concept drift is among the open issues preventing the widespread exploitation of deep learning models in real-life settings. The dynamic world changes very quickly, and the collected data drifts accordingly. Prediction models, usually trained on static historical data, should be promptly re-trained in case of new real-time drifted data distributions. Although some drift detection methodologies have been proposed over the years, different issues are still open since state-of-the-art solutions show limited effectiveness and efficiency. This paper proposes DRIFT LENS, a novel real-time unsupervised per-label drift detection methodology based on embedding distribution distances in deep learning models. The preliminary experiments performed on a transformer-based model fine-tuned for topic text classification show promising results in drift detection accuracy, drift characterization, and efficient execution time to support real-time concept drift detection.

关键词： data drift MLOps deep learning transformerbased models text topic classification

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