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Identification of human microRNA-disease association via low-rank approximation-based link propagation and multiple kernel learning

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Frontiers of Computer Science 2024年第2期18卷 217-229页

作者： Yizheng WANG Xin ZHANG Ying JU Qing LIU Quan ZOU Yazhou ZHANG Yijie DING Ying ZHANG Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of ChinaChengdu 610054China Yangtze Delta Region Institute(Quzhou) University of Electronic Science and Technology of ChinaQuzhou 324000China Beidahuang Industry Group General Hospital Harbin 150088China School of Informatics Xiamen UniversityXiamen 361005China Department of Anesthesiology Hospital(T.C.M)Affiliated to Southwest Medical UniversityLuzhou 646000China Software Engineering College Zhengzhou University of Light IndustryZhengzhou 450002China

Numerous studies have demonstrated that human microRNAs(miRNAs)and diseases are associated and studies on the microRNA-disease association(MDA)have been *** developed a model using a low-rank approximation-based link propagation algorithm with Hilbert–Schmidt independence criterion-based multiple kernel learning(HSIC-MKL)to solve the problem of the large time commitment and cost of traditional biological experiments involving miRNAs and diseases,and improve the model *** constructed three kernels in miRNA and disease space and conducted kernel fusion using *** propagation uses matrix factorization and matrix approximation to effectively reduce computation and time *** results of the experiment show that the approach we proposed has a good effect,and,in some respects,exceeds what existing models can do.

关键词： human miRNA-disease association multiple kernel learning link propagation miRNA similarity disease similarity

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Review of on-board conductive charger topologies for electric transportation

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International Journal of Power Electronics 2023年第4期17卷 406-442页

作者： Vijaya Anand, N. Jaya Sai Praneeth, A.V. Yalla, Naveen Sood, Vijay K. Department of EEE P.V.P. Siddhartha Institute of Technology Kanuru Vijayawada520007 India Advance Engineering Group Borg Warner Technical Center Bascharage Luxembourg Varanasi221005 India Department of Electrical Computer and Software Engineering Faculty of Engineering and Applied Science Ontario Tech University 2000 Simcoe Street North OshawaONL1H 7K4 Canada

This paper presents an overview of on-board conductive charger topologies for electric vehicles (EVs). Battery packs in electric and plug-in hybrid electric vehicles (EVs/PHEVs) need frequent energy refills to fulfil their duty cycles and overcome the range anxiety of vehicle owners. Due to the limited availability of fast charging infrastructures, all automotive manufacturers prefer an on-board battery charger in their vehicles. However, there are power level limitations for these chargers because of the weight, space and cost constraints. On-board charging systems can be either conductive or inductive. This paper reviews the topologies that are involved in conductive charging only. Globally, all automotive companies provide levels 1 and 2 for residential charging of vehicles. Single- and two-stage power converter topologies are summarised with their limitations. Furthermore, the current trends in the DC-DC converters for the two-stage conversion battery charger topologies are also outlined. This paper provides an overview of various topologies and configurations involved in EV charging with their limitations. © 2023 Inderscience Enterprises Ltd.

关键词： Topology

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Multi-Demand Forecasting for Electric Vehicle Charging Stations under Time-of-Use Strategy via Attention-Based Deep Neural Network

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IEEE Internet of Things Journal 2025年

作者： Zhou, Zhide Jiang, He Yao, Lei Wang, Shaolin Che, Haoyang Gu, Ying Dalian University of Technology School of Software Dalian China DUT Artificial Intelligence Dalian China Zeekr Group Hangzhou China Geely Holding Group/Zhejiang Automotive Engineering Institute Hangzhou China

Electric vehicle charging stations (EVCSs) have become a pivotal infrastructure within the EV industry. In particular, many EV companies construct self-owned EVCSs to provide better charging service for their customers. For these self-owned EVCSs, to ensure the quality of service for self-owned users and third-party users, dynamic pricing based on the time-of-use (TOU) strategy has been extensively employed. This makes the demand forecasting of EVCSs important since it depicts the relationship between the charging price and the demand of an EVCS. Unfortunately, the existing techniques cannot accurately predict the demand of multiple users simultaneously. Consequently, this paper examines the problem of multi-demand forecasting of EVCSs, and proposes an efficient method to resolve this issue. The key insight of the proposed method is to train a deep neural network consisting of two subnetworks that can jointly forecast the demand of the self-owned user and the third-party user simultaneously. First, six kinds of features of EVCSs are extracted. Then, a novel deep neural network Atlas based on the attention mechanism is proposed to forecast the multi-demand of EV charging stations under the TOU strategy. Finally, to resolve the scarcity of historical charging demand data, a coarse-fine training process is proposed to train Atlas for each EVCS. The evaluation based on the real-world data set of 771 EVCSs from an EV company demonstrates that Atlas significantly outperforms seven state-of-the-art techniques by up to 34.82% 61.92%. © 2014 IEEE.

关键词： Charging stations

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FedVC: Virtual Clients for Federated Learning with Imbalanced Label Distribution 24

FedVC: Virtual Clients for Federated Learning with Imbalance...

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3rd International Conference on Algorithms, Data Mining, and Information technology, ADMIT 2024

作者： Chen, Zhouguo Chen, Hongda Liu, Shuai Liu, Yongdong Schoolof Computer Science and Engineering Southeast University Jiangsu Nanjing China The 30th Research Institute of China Electronics Technology Group Corporation Sichuan Chengdu China School of Software Engineering Xian Jiaotong University Shaanxi Xian China China Industrial Control Systems Cyber Emergency Response Team Beijing China

ISBN: (纸本)9798400718120

Federated Learning (FL) has gained considerable attention for collaborative training in big data analysis, particularly in terms of privacy and communication constraints. Despite its promising advantages, FL faces the challenge of dealing with imbalanced label distribution on edge devices, which complicates the learning task. In literature, numerous studies have attempted to tackle this issue by introducing additional hyperparameters, which can complicate and prolong the process of model training and tuning. This complexity may potentially lead to overfitting and limited performance on new datasets. In this paper, we present a new Federated Learning framework called FedVC, which introduces virtual clients to minimize hyperparameter reliance. FedVC aims to bridge the gap between balanced and imbalanced label distribution. Compared with traditional federated learning frameworks, the proposed FedVC model designs a dynamically balanced dataset by utilizing virtual clients, which is proposed for the first time in this work. These virtual clients determine which part of the data should be used in one single training round and manage backpropagation timing for local real clients. The balanced dataset promotes greater stability in local models, thereby enhancing the overall stability of the training process. The experimental results show that FedVC outperforms classical FedAvg and the compared baseline methods for the data with imbalanced label distribution. Especially, FedVC exhibits more stable and accurate results than alternative approaches in highly heterogeneous settings. The source code is available at https://***/aikedaerC/FedVC. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

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software fault localization based on eigenvector centrality in complex network theory 24

Software fault localization based on eigenvector centrality ...

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24th IEEE International Conference on software Quality, Reliability and Security Companion, QRS-C 2024

作者： Wu, Wentao Wang, Shihai Shao, Yuanxun Xie, Wandong School of Reliability and Systems Engineering Beihang University Beijing100191 China Science and Technology on Reliability and Environmental Engineering Laboratory China State Key Laboratory of Software Development Environment China Aerospace Science and Engineering Network Information Development CO. Ltd Beijing China Information Center of China North Industries Group Corporation Beijing China

ISBN: (纸本)9798350365658

software debugging plays a crucial role in fault localization tasks, and spectrum-based fault localization (SBFL) is a hot topic in software automation debugging research. However, existing SBFL technologies are generally limited by tie within ranks, where a large number of elements share the same suspiciousness, which in turn severely limits the performance of SBFL. To this end, we propose an SBFL model based on eigenvector centrality (FLEC). This algorithm first utilizes the statement coverage information in the program spectrum to construct a statement network, and adopts the correlation between statements as edge weights. Then, FLEC takes statement suspiciousness as node weight. On this basis, the algorithm utilizes the eigenvector centrality to calculate the weighted suspiciousness of each statement while considering both node importance and correlation between nodes. Finally, FLEC conducted experimental validation on 3 datasets of Defects4J, and the results showed an average improvement of 10.7% in ACC@N compared to the optimal baseline. © 2024 IEEE.

关键词： Program debugging

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Image dehazing based on mist concentration estimation and nonlocal similarity constraint 9

Image dehazing based on mist concentration estimation and no...

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9th Symposium on Novel Photoelectronic Detection technology and Applications

作者： Liu, Yan Wang, Xingchen Wu, Bin Zhang, Xinlei Engineering Hohai University Changzhou China Intelligent Home Software Development Department I ZTE Corporation Nanjing China The 32nd Research Institute of China Electronics Technology Group Corporation Shanghai China

ISBN: (纸本)9781510664432

The atmospheric light scattering and absorption from the aerosols usually cause visible light imaging quality decline. The dark channel prior method is a dehazing method to estimate transmittance or background light, which is suitable for foggy image restoration with the influence of suspended particles. Based on the DCP model, the proposed method combines mist concentration level estimation and nonlocal similarity constraint to improve transmittance estimation accuracy. Firstly, the mist concentration level is calculated from natural scene statistical characteristics of the image. Then the corresponding regularization factor is chosen for transmittance estimation. The local structure similarity constraint is introduced into simplified DCP method to decreasing the uneven transmission estimation error. Weighted quadtree searching method is used to estimate the background light. Finally, the image dehazing model is conducted with the transmission map and background light. The comparative experiments are carried out among the original DCP method, the classical image enhancement method MSRCR and the neural network defogging method PDR-NET. The experimental results show that the mist concentration level prior and nonlocal similarity constraints can optimize the solution of the transmittance. The proposed method has significant effect on increasing the image contrast, reducing mist concentration, keeping texture clarity and edge intensity. © 2023 SPIE.

关键词： Image enhancement

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Universal Construction for Linearizable but Not Strongly Linearizable Concurrent Objects 10th

Universal Construction for Linearizable but Not Strongly L...

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10th International Symposium on Dependable software engineering: Theories, Tools and Applications, SETTA 2024

作者： Wang, Chao Wu, Peng Petri, Gustavo Jia, Qiaowen He, Youlin Lv, Yi Liu, Zhiming Centre for Research and Innovation in Software Engineering College of Computer and Information Science Southwest University Chongqing China and State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China Arm Research Cambridge United Kingdom China Great Wall Technology Group Co. Ltd. Shenzhen China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9789819606016

Strong linearizability is a variant of linearizability and is more suitable for verification. In this paper we investigate the following two problems: (1) for which deterministic sequential specifications there exist linearizable but not strongly linearizable objects;(2) can we capture any violation scheme of strong linearizability for practical objects that are linearizable but not strongly linearizable. To deal with the first problem, we propose two classes of deterministic sequential specifications called inclusively-permutative sequential specifications and exclusively-permutative sequential specifications. The first class of sequential specifications contains sequential specifications of counter, register, max-register, snapshot, set, queue, stack and priority queue. The second class of sequential specifications contains sequential specifications of blocking queue, blocking stack and blocking priority queue. To establish these results, we propose a uniform methodology to construct a wait-free, linearizable but not strongly linearizable object for these two classes of sequential specifications. Our universal construction is based on the classical universal construction, and it additionally permits each operation to change the "take-effect-order" of previous operations. To deal with the second problem, we use bi-branch distinguishable executions to represent violations of strong linearizability. We investigate eight objects that are linearizable but not strongly linearizable, and we show that for each object, it has a pair of bi-branch distinguishable executions such that our universal construction can generate another pair of bi-branch distinguishable executions with the same set of histories. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

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A Secure Device Management Scheme with Audio-Based Location Distinction in IoT

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Computer Modeling in engineering & Sciences 2024年第1期138卷 939-956页

作者： Haifeng Lin Xiangfeng Liu Chen Chen Zhibo Liu Dexin Zhao Yiwen Zhang Weizhuang Li Mingsheng Cao College of Economics and Management Nanjing University of Aeronautics and AstronauticsNanjing211106China Chengdu Aircraft Industrial(Group)Co. Ltd.Chengdu610073China Academy of Military Sciences of PLA Beijing100091China School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu610054China Ningbo Web King Technology Joint Stock Co. Ltd.Ningbo315000China The Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of ChinaChengdu610054China

Identifying a device and detecting a change in its position is critical for secure devices management in the Internet of Things(IoT).In this paper,a device management system is proposed to track the devices by using audio-based location distinction *** the proposed scheme,traditional cryptographic techniques,such as symmetric encryption algorithm,RSA-based signcryption scheme,and audio-based secure transmission,are utilized to provide authentication,non-repudiation,and confidentiality in the information interaction of the management ***,an audio-based location distinction method is designed to detect the position change of the ***,the audio frequency response(AFR)of several frequency points is utilized as a device *** device signature has the features as follows.(1)Hardware Signature:different pairs of speaker and microphone have different signatures;(2)Distance Signature:in the same direction,the signatures are different at different distances;and(3)Direction Signature:at the same distance,the signatures are different in different *** on the features above,amovement detection algorithmfor device identification and location distinction is ***,a secure communication protocol is also proposed by using traditional cryptographic techniques to provide integrity,authentication,and non-repudiation in the process of information interaction between devices,Access Points(APs),and *** experiments are conducted to evaluate the performance of the proposed *** experimental results show that the proposedmethod has a good performance in accuracy and energy consumption.

关键词： Acoustic hardware fingerprinting device management IoT location distinction

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An AI-aware Orchestration Framework for Cloud-based LLM Workloads

An AI-aware Orchestration Framework for Cloud-based LLM Work...

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Edge Computing and Scalable Cloud (EdgeCom), IEEE International Conference on

作者： Zi Ye Ruoyu Ying UM-SJTU Joint Intitute Shanghai Jiao Tong University Shanghai China Dept of Cloud Software Engineering Software and Advanced Technology Group Intel Shanghai China

ISBN: (数字)9798350377132

ISBN: (纸本)9798350377149

Large Language Models (LLMs), such as ChatGPT and Bard, have revolutionized the entire industry with their extraordinary power, and gained traction in the cloud community. Deploying LLM workloads in cloud has become increasingly common nowadays, as cloud easily fulfills their requirement on extensive computing resources. There are already tools (such as Kubernetes) that help simplify the management of Artificial Intelligence (AI) workloads, yet LLMs often require the balancing of multi-dimensional demands, which can be hard to handle by basic tools. This paper introduces an AI-aware orchestration framework that optimizes LLM performance from aspects including scheduling, scaling, and advanced data management. It can improve the cost-efficiency of the LLM workloads while ensuring its performance, while ensuring the absence of common security risks, supported by Intel hardware.

关键词： Industries Job shop scheduling Processor scheduling Large language models Computational modeling Chatbots Hardware

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Automatic Chest Radiology Report Generation Using Reinforcement Learning 7th

Automatic Chest Radiology Report Generation Using Reinforcem...

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7th International Conference on Intelligent Technologies, ICIT 2022

作者： Kourav, Mohit Agarwal, Saurabh Arya, K.V. Petrlik, Ivan Rodriguez, Ciro Multimedia and Information Security Research Group ABV-Indian Institute of Information Technology & Management Gwalior India Faculty of System Engineering Universidad Nacional Federico Villarreal Lima Peru Faculty of Software Engineering Universidad Nacional Mayor de San Marcos Lima Peru

ISBN: (纸本)9789819919116

Radiology report generation is the manual task assigned to radiologists which analyze the given chest image (x-ray) of patient and generates the report based on that analysis. Many of the research is undergoing in this field to make this process automatic. However, there are a large number of difficulties which have been faced by the researchers while developing the efficient automatic report generation model. The main difficulties include the generation of clinically inaccurate reports when analyzed with the reports of the expert radiologist. The introduction of deep learning based approaches achieved tremendous advancement in these areas and can produce chest X-ray reports in a more accurate manner. BY further optimizing the deep learning based approaches, we proposed a reinforcement learning-based model that generates automated X-ray reports based on input chest X-ray images. We also developed a lookahead inference mechanism to overcome the limitation of beam search, which selects words based on policy and value networks at each particular step of time. The proposed model achieved 0.454, 0.546, 0.466, 0.410, and 0.424 for the Bleu-1, Bleu-2, Bleu-3, Bleu-4, and Rouge evaluation metrics, respectively, surpassing all available state-of-the-art algorithms. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Reinforcement learning

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