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Efficient Sharing of Energy Consumption Data: A Privacy-Preserving Threshold Aggregation Approach

IEEE Internet of Things Journal 2025年

作者： Li, Guohao Zhou, Lu Lian, Jiale Liu, Siyi Yang, Li Zhong, Yantao Li, Qiang Xidian University School of Computer Science and Technology Xi’an China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Changchun China Co. Ltd Guangdong Shenzhen China

Energy consumption data collected by smart meters is increasingly used by various subscribers in the smart grid for load management, energy monitoring, and policy planning. To protect user privacy, edge-assisted privacy-preserving data aggregation (PPDA) techniques are commonly employed. However, existing methods face several challenges: i) limited scalability, ii) strict trust requirements, and iii) the risk of revealing unique consumption patterns to data collectors. To address these challenges, we propose a privacy-preserving threshold aggregation method that is easily scalable and facilitates efficient energy data sharing under limited trust assumptions. Specifically, we design VFPNTRU, a quantum-resistant homomorphic proxy re-encryption scheme with fault tolerance and re-encryption verification. In VFP-NTRU, smart meters can encrypt data with a public key without the need for prior negotiation of decryption keys with multiple subscribers. Additionally, we develop a privacy threshold collection protocol that uses a verifiable oblivious pseudorandom function to provide privacy guarantees similar to k-anonymity for SM data collection. We further introduce an energy consumption model to determine optimal collection strategies, improving system responsiveness. We provide correctness analysis and prove the security of our scheme. Experimental results demonstrate that our approach outperforms existing PPDA methods, making it particularly suitable for resource-constrained smart meters and central servers managing large-scale energy data. © 2014 IEEE.

关键词： Differential privacy

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Vertical Traffic Scheduling Control Method Based On Dual Fuzzy Neural Network

Vertical Traffic Scheduling Control Method Based On Dual Fuz...

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Complex Systems and Intelligent Science (CSIS-IAC), International Annual Conference on

作者： Xinhao Sun Siqi An Xiaoting Gao Enchang Cui College of Light Industry Liaoning University Shenyang China Key Laboratory of Symbolic Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China

With the complexity of the functions of modern buildings, the problem of vertical traffic in buildings is becoming more and more prominent. As the only vertical transportation, the elevator is a necessary prerequisite for the development of modern buildings to solve its group control distribution problem. In this paper, a vertical traffic scheduling control method is proposed based on dual fuzzy neural network, a recognition strategy is designed to identify the passenger flow models. The dual fuzzy neural network is used to first identify the traffic network model in which the elevator is located, and then combine the corresponding weights and the confidence of the group control allocation to complete the group control scheduling. Finally, the proposed method is verified through a semi-physical simulation platform, proving the correctness and effectivness for traffic pattern recognition and scheduling strategy optimization of group controllers.

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GazePuffer: Hands-Free Input Method Leveraging Puff Cheeks for VR

GazePuffer: Hands-Free Input Method Leveraging Puff Cheeks f...

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IEEE Annual International Symposium Virtual Reality

作者： Yunfei Lai Minghui Sun Zhuofeng Li College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China

Gaze input is a popular hands-free input method that allows for intuitive and rapid pointing but lacks a confirmation mechanism. This study introduces GazePuffer, an interaction method that combines puffing cheeks with gaze. We explored the design space of mouth gestures, proposed a set of candidate gestures, filtered them through user subjective evaluation, and selected five basic gestures and four variations. We determined the corresponding virtual reality (VR) actions for these gestures through brainstorming. We achieved an accuracy of 93.8% in recognizing the five basic mouth gestures using the built-in sensors of the head-mounted display devices. We compared GazePuffer with two baseline methods in target selection tasks, demonstrating that GazePuffer is on par with Gaze&Pinch in throughput and speed, slightly outperforming Gaze&Dwell. Finally, we showcased the applicability of GazePuffer in real VR interaction tasks, with users generally finding it usable and effortless.

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Multimodal Medical Image Feature Representation and Fusion for AD Early Diagnosis

Multimodal Medical Image Feature Representation and Fusion f...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Qu, Jing Qu, Ying Bai, Xueliang Xu, Lanxin Wang, Han Liu, Guixia Jilin University College of Computer Science and Technology Changchun China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun China North China University of Science and Technology College of Science Tangshan China Institute of Computational Biology Northeast Normal University School of Information Science and Technology Changchun China

ISBN: (纸本)9798350386226

Alzheimer's disease (AD) is progressive and gets worse with time. Early detection is an effective treatment for the disease, which can timely implement effective interventions. The multi-modal medical image contains more comprehensive and abundant information about the disease, which can reflect the different aspects of AD and reduce the potential bias in single-modal diagnosis. However, an effective and reliable solution is still lacking, which can integrate multimodal neuroimaging effectively for the precision diagnosis and treatment of AD. In this work, we proposed a feature representation and fusion model of MRI and PET scans based on a multiscale convolution network and cross-attention for AD early diagnosis, named camAD. Compared with the existing models, camAD performs better with fewer pre-processing steps. Using the Alzheimer's disease neuroimaging initiative (ADNI) datasets, we demonstrated that integrating multi-modality data outperforms single-modality models in accuracy, specificity, sensitivity, AUC, and F1 scores. Our models exhibited significantly good accuracy and generality in four classification tasks, which provided a promising way to understand the underlying mechanisms of the disorder's progression. © 2024 IEEE.

关键词： Neuroimaging

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Distributed solving Sylvester equations with fractional order dynamics

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Control Theory and Technology 2021年第2期19卷 249-259页

作者： Songsong Cheng Shu Liang Yuan Fan Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Electrical Engineering and AutomationAnhui UniversityHefei230601AnhuiChina Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical EngineeringUniversity of Science and Technology BeijingBeijing100083China

This paper addresses distributed computation Sylvester equations of the form AX+XB=C with fractional order *** partitioning parameter matrices A,B and C,we transfer the problem of distributed solving Sylvester equations as two distributed optimization models and design two fractional order continuous-time algorithms,which have more design freedom and have potential to obtain better convergence performance than that of existing first order ***,rewriting distributed algorithms as corresponding frequency distributed models,we design Lyapunov functions and prove that proposed algorithms asymptotically converge to an exact or least squares ***,we validate the effectiveness of proposed algorithms by providing a numerical example.

关键词： Fractional order calculus Distributed optimization Sylvester equation Frequency distributed model Convergence

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Diffuse tumors: Molecular determinants shared by different cancer types

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Computers in Biology and Medicine 2024年 178卷 108703-108703页

作者： Li, Xuan Liu, Dingyun Wu, Zhipeng Xu, Ying Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Changchun130012 China School of Medicine Southern University of Science and Technology Shenzhen China

Most cancer types have both diffuse and non-diffuse subtypes, which have rather distinct morphologies, namely scattered tiny tumors vs. one solid tumor, and different levels of aggressiveness. However, the causes for forming such distinct subtypes remain largely unknown. Using the diffuse and non-diffuse gastric cancers (GCs) as the illustrative example, we present a computational study based on the transcriptomic data from the TCGA and GEO databases, to address the following questions: (i) What are the key molecular determinants that give rise to the distinct morphologies between diffuse and non-diffuse cancers? (ii) What are the main reasons for diffuse cancers to be generally more aggressive than non-diffuse ones of the same cancer type? (iii) What are the reasons for their distinct immunoactivities? And (iv) why do diffuse cancers on average tend to take place in younger patients? The study is conducted using the framework we have previously developed for elucidation of general drivers cancer formation and development. Our main discoveries are: (a) the level of (poly-) sialic acids deployed on the surface of cancer cells is a significant factor contributing to questions (i) and (ii);(b) poly-sialic acids synthesized by ST8SIA4 are the key to question (iii);and (c) the circulating growth factors specifically needed by the diffuse subtype dictate the answer to question (iv). All these predictions are substantiated by published experimental studies. Our further analyses on breast, prostate, lung, liver, and thyroid cancers reveal that these discoveries generally apply to the diffuse subtypes of these cancer types, hence indicating the generality of our discoveries. © 2024

关键词： Tumors

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Medical Image Segmentation using PCNN based on Multi-feature Grey Wolf Optimizer Bionic Algorithm

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Journal of Bionic engineering 2021年第3期18卷 711-720页

作者： Xue Wang Zhanshan Li Heng Kang Yongping Huang Di Gai College of Computer Science and Technology Jilin UniversityChangchun 130012China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin UniversityChangchun 130012China ZICT Technology Co. Ltd.Shenzhen 518063China

Medical image segmentation is a challenging task especially in multimodality medical image *** this paper,an improved pulse coupled neural network based on multiple hybrid features grey wolf optimizer(MFGWO-PCNN)is proposed for multimodality medical image ***,a two-stage medical image segmentation method based on bionic algorithm is presented,including image fusion and image *** image fusion stage fuses rich information from different modalities by utilizing a multimodality medical image fusion model based on maximum energy *** the stage of image segmentation,an improved PCNN model based on MFGWO is proposed,which can adaptively set the parameters of PCNN according to the features of the *** modalities of FLAIR and TIC brain MRIs are applied to verify the effectiveness of the proposed MFGWO-PCNN *** experimental results demonstrate that the proposed method outperforms the other seven algorithms in subjective vision and objective evaluation indicators.

关键词： grey wolf optimizer pulse coupled neural network bionic algorithm medical image segmentation

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ResolvingWord Vagueness with Scenario-guided Adapter for Natural Language Inference

arXiv

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arXiv 2024年

作者： Liu, Yonghao Li, Mengyu Liang, Di Li, Ximing Giunchiglia, Fausto Huang, Lan Feng, Xiaoyue Guan, Renchu Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education College of Computer Science and Technology Jilin University China Fudan University China University of Trento Italy

Natural Language Inference (NLI) is a crucial task in natural language processing that involves determining the relationship between two sentences, typically referred to as the premise and the hypothesis. However, traditional NLI models solely rely on the semantic information inherent in independent sentences and lack relevant situational visual information, which can hinder a complete understanding of the intended meaning of the sentences due to the ambiguity and vagueness of language. To address this challenge, we propose an innovative ScenaFuse adapter that simultaneously integrates large-scale pre-trained linguistic knowledge and relevant visual information for NLI tasks. Specifically, we first design an image-sentence interaction module to incorporate visuals into the attention mechanism of the pre-trained model, allowing the two modalities to interact comprehensively. Furthermore, we introduce an image-sentence fusion module that can adaptively integrate visual information from images and semantic information from sentences. By incorporating relevant visual information and leveraging linguistic knowledge, our approach bridges the gap between language and vision, leading to improved understanding and inference capabilities in NLI tasks. Extensive benchmark experiments demonstrate that our proposed ScenaFuse, a scenario-guided approach, consistently boosts NLI performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Bridge approaches

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Stable Learning via Triplex Learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第10期5卷 5267-5276页

作者： Yang, Shuai Jiang, Tingting Dang, Qianlong Gu, Lichuan Wu, Xindong Anhui Agricultural University School of Information and Artificial Intelligence Hefei230036 China Anhui Provincial Engineering Research Center for Agricultural Information Perception and Intelligent Computing Hefei230036 China Northwest A & F University College of Science Yangling712100 China Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data The Ministry of Education of China Hefei230601 China Hefei University of Technology School of Computer Science and Information Engineering Hefei230601 China

Stable learning aims to learn a model that generalizes well to arbitrary unseen target domain by leveraging a single source domain. Recent advances in stable learning have focused on balancing the distribution of confounders for each feature to eliminate spurious correlations. However, previous studies treat all features equally without considering the difficulties of confounder balancing associated with different features, and regard irrelevant features as confounders, deteriorating generalization performance. To tackle these issues, this article proposes a novel triplex learning (TriL) based stable learning algorithm, which performs sample reweighting, causal feature selection, and representation learning to remove spurious correlations. Specifically, first, TriL adaptively assigns weights to the confounder balancing term of each feature in accordance with the difficulties of confounder balancing, and aligns the confounder distribution of each feature by learning a group of sample weights. Second, TriL integrates the sample weights into a weighted cross-entropy model to compute causal effects of features for excluding irrelevant features from the confounder set. Finally, TriL relearns a set of sample weights and uses them to guide a new supervised dual-autoencoder containing two classifiers to learn feature representations. TriL forces the results of two classifiers to remain consistent for removing spurious correlations by using a cross-classifier consistency regularization. Extensive experiments on synthetic and two real-world datasets show the superiority of TriL compared with seven methods. © 2024 IEEE.

关键词： Feature Selection

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Decision Mamba: Reinforcement Learning via Hybrid Selective Sequence Modeling

arXiv

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arXiv 2024年

作者： Huang, Sili Hu, Jifeng Yang, Zhejian Yang, Liwei Luo, Tao Chen, Hechang Sun, Lichao Yang, Bo Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education China School of Artificial Intelligence Jilin University China Institute of High Performance Computing A*STAR Singapore Lehigh University BethlehemPA United States

Recent works have shown the remarkable superiority of transformer models in reinforcement learning (RL), where the decision-making problem is formulated as sequential generation. Transformer-based agents could emerge with self-improvement in online environments by providing task contexts, such as multiple trajectories, called in-context RL. However, due to the quadratic computation complexity of attention in transformers, current in-context RL methods suffer from huge computational costs as the task horizon increases. In contrast, the Mamba model is renowned for its efficient ability to process long-term dependencies, which provides an opportunity for in-context RL to solve tasks that require long-term memory. To this end, we first implement Decision Mamba (DM) by replacing the backbone of Decision Transformer (DT). Then, we propose a Decision Mamba-Hybrid (DM-H) with the merits of transformers and Mamba in high-quality prediction and long-term memory. Specifically, DM-H first generates high-value sub-goals from long-term memory through the Mamba model. Then, we use sub-goals to prompt the transformer, establishing high-quality predictions. Experimental results demonstrate that DM-H achieves state-of-the-art in long and short-term tasks, such as D4RL, Grid World, and Tmaze benchmarks. Regarding efficiency, the online testing of DM-H in the long-term task is 28× times faster than the transformer-based baselines. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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