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4th International Conference on Neural Networks, Information and Communication engineering, NNICE 2024

作者： Yu, Zhouqian Zhang, Wei Xu, Tao Beijing Information Science and Technology University Computer School Beijing China Beijing Information Science and Technology University Beijing Laboratory of National Economic Security Early-Warning Engineering Beijing China Tsinghua University Department of Computer Science and Technology Beijing China

ISBN: (纸本)9798350394375

Code similarity analysis is a versatile technique that can be applied across various domains, including code clone detection, code search, malware detection, patch analysis, and vulnerability search. The core of source-binary code similarity analysis lies in effective modeling of the both of source code and binary code. Existing researches have overlooked the fact that code has both text and graph structure features, so they usually only use one of these features for modeling. In this paper, we propose a method that combines sequence features based on intermediate representation and program graph features derived from intermediate representation to enhance the capture of program semantics. Our approach involves the parallel embedding and learning of LLVM IR and the derived program graphs, leading to the extraction of the final code feature representation. Furthermore, we employ a triplet-loss network to identify code characteristic disparities and produce similarity rankings. The experimental results have demonstrated that our approach outperforms other models on the baseline, achieving a 6% improvement in accuracy for source-binary code similarity tasks. © 2024 IEEE.

关键词： Graph neural networks

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Enhanced Frequency Information for Image Dehazing 12th

Enhanced Frequency Information for Image Dehazing

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12th International Conference on Image and Graphics, ICIG 2023

作者： Guo, Fei Fan, Junkai Li, Jun Yang, Jian PCA Lab School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China

ISBN: (纸本)9783031463044

The restoration of images affected by severe weather conditions such as heavy fog is a trending topic in the field of computer vision. Despite the fact that many image dehazing methods have achieved impressive performance, it is common that frequency information attenuation is overlooked in both feature space and frequency domain. In this paper, we propose a novel frequency guidance (FG) framework for image dehazing, which contains a recurrent frequency enhancement (RFE) module and a reconstruction module. To begin with, we develop a multi-scale decomposition (MD) block to separate the feature map into high-frequency and low-frequency components. Subsequently, both components are enhanced using the same network guided by the attention map, but with different weights. In addition to enhancing the frequency information within the feature space, we introduce a Fourier frequency loss (FFL) to provide guidance in the frequency domain, obtained via the fast Fourier transform. Extensive experiments demonstrate that our method achieves state-of-the-art performance on multiple dehazing datasets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Image enhancement

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Uncertainty-aware coarse-to-fine alignment for text-image person retrieval

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Visual Intelligence 2025年第1期3卷 1-14页

作者： Deng, Yifei Chen, Zhengyu Li, Chenglong Tang, Jin Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei 230601 China Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Provincial Key Laboratory of Security Artificial Intelligence School of Artificial Intelligence Anhui University Hefei 230601 China

Text-to-image person retrieval, a fine-grained cross-modal retrieval problem, aims to search for person images from an image library that match a given textual caption. Existing text-to-image person retrieval methods usually use fixed-point embedding to express the semantics of the two modalities and perform multi-granularity alignment between modalities in the embedding space. However, owing to the inherent mutual one-to-many correspondence between images and texts, it is often difficult for fixed-point embedding methods to adequately capture this relationship, leading to erroneous retrieval results. To address this problem, we propose a novel uncertainty-aware coarse-to-fine alignment method, which first maps fixed-point embedding to probability distributions and then aligns two modalities in terms of distributions and sampling points at a coarse-to-fine granularity, for accurate text-to-image person retrieval. Specifically, we first introduce two contrastive learning tasks of distribution contrast learning and point contrast learning, to achieve coarse-grained inter-modal alignment with uncertainty-aware. The distribution contrast learning task ensures that distributions with the same identity are as similar as possible across modalities through distribution-based contrastive learning. The point contrast learning task performs the contrastive learning of inter-modal and intra-modal sampling points, which not only models rich and diverse cross-modal associations, but also optimizes the learning of distributions. For the fine-grained association requirements of text-to-image person retrieval, we design the task of uncertainty-aware attribute masking language reconstruction, which achieves fine-grained alignment by randomly masking attribute words in the text and reconstructing them via inter-modal sample point interactions. Extensive experiments on two public datasets demonstrate the superior performance of our method. © The Author(s) 2025.

关键词： Coarse-to-fine alignment Contrastive learning Cross-modal retrieval Probalility distribution Uncertainty-aware

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High-quality Trajectory Generation for Autonomous Driving: A Lightweight Federated Learning-based Diffusion Model

High-quality Trajectory Generation for Autonomous Driving: A...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Gao, Runquan Kang, Jiawen Lai, Bingkun Xu, Minrui Sun, Geng Zhang, Tao Zhang, Weiting Yang, Dong Guangdong University of Technology School of Automation Guangzhou China Nanyang Technological University School of Computer Science and Engineering Singapore Jilin University College of Computer Science and Technology Changchun China Beijing Jiaotong University School of Software Engineering Beijing China Beijing Jiaotong University School of Electronic and Information Engineering Beijing China

ISBN: (纸本)9798350351255

Vehicle trajectory data plays a pivotal role in simulation testing for autonomous driving. Hence, there exist well-established trajectory generation methods employing deep generative models to generate trajectories mapping the distribution of the original dataset, thereby augmenting existing trajectory datasets. However, these methods typically rely on large datasets gathered by governmental or organizational entities for central training, which may pose data privacy, security, and accessibility issues. Therefore, it is challenging to generate high-quality traffic trajectory data while preserving privacy which involves a delicate balance between these two objectives. To deal with this challenge, we introduce Federated Learning into the diffusion model and propose a Federated Learning-based diffusion model (FedDifftraj) to generate traffic trajectory data. Unlike existing central training methods, FedDifftraj aggregates model parameters uploaded by different vehicles and then updates a global model. Additionally, there is a substantial communication overhead incurred during the training of the federated diffusion model. Therefore, we quantize the local diffusion model before uploading it to the parameter server. Through extensive simulations on real-world datasets, FedDifftraj can generate high-quality traffic trajectory data that is consistent with the results of the central training while preserving privacy and reducing communication overhead by 93.74% when utilizing 8-bit quantization. © 2024 IEEE.

关键词： Differential privacy

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Cardiac Arrest Prediction Model Using Ensemble Technique 4

Cardiac Arrest Prediction Model Using Ensemble Technique

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Sinha, Dibakar More, Sarla Jeyakrishnan, V. Mishra, Durgesh Manipal University Jaipur Department of Computer Science Engineering Rajasthan Jaipur India Symbiosis University of Applied Sciences School of Computer Science and Information Technology MP Indore India

ISBN: (纸本)9798331518981

The physician employs a diverse range of laboratory diagnostics, physical examinations, and on occasion, even invasive procedures to ascertain the presence of diseases. It is necessary to have a physician that possesses exceptional training, expertise and skill in the specific field, for detection of the disease in its initial phases. Despite the widespread use of ensemble techniques in disease prediction, here is a notable absence of comprehensive evaluation of regularly utilized ensemble algorithms for highly studied diseases. Consumers typically resort to seeking medical assistance from a doctor or other medical expert only after exhausting all other home remedies without finding relief for their ailments. This is probably a result of insufficient comprehension, as well as other factors related to behavior. This paper introduces a conventional ensemble LSTM model that incorporates data preparation and feature engineering. The model proposed in this research represents major gains in numerous statistical parameters like precision, f score, accuracy and ROC curve in comparison to the other models. © 2024 IEEE.

关键词： Diseases

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A low redox potential and long life organic anode material for sodium-ion batteries

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Journal of Energy Chemistry 2025年第1期100卷 557-564页

作者： Zhi Li Yang Wei Kang Zhou Xin Huang Xing Zhou Jie Xu Taoyi Kong Junwei Lucas Bao Xiaoli Dong Yonggang Wang Department of Chemistry and Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials Institute of New EnergyCollaborative Innovation Center of Chemistry for Energy Materials(iChEM)Fudan UniversityShanghai 200433China Department of Chemistry Boston CollegeChestnut HillMA 02467USA School of Materials Science and Engineering Anhui University of TechnologyMa’anshan 243002AnhuiChina

Sodium-ion batteries (SIBs) with organic electrodes are an emerging research direction due to the sustainability of organic materials based on elements like C,H,O,and sodium ***,organic electrode materials for SIBs are mainly used as cathodes because of their relatively high redox potentials(>1 V).Organic electrodes with low redox potential that can be used as anode are ***,a novel organic anode material (tetrasodium 1,4,5,8-naphthalenetetracarboxylate,Na_(4)TDC) has been developed with low redox potential (<0.7 V) and excellent cyclic *** three-sodium storage mechanism was demonstrated with various in-situ/ex-situ spectroscopy and theoretical calculations,showing a high capacity of 208 mAh/g and an average decay rate of merely 0.022%per ***,the Na_(4)TDC-hard carbon composite can further acquire improved capacity and cycling stability for 1200 cycles even with a high mass loading of up to 20 mg cm^(-2).By pairing with a thick Na_(3)V_(2)(PO_(4))_(3)cathode (20.6 mg cm^(-2)),the as-fabricated full cell exhibited high operating voltage (2.8 V),excellent rate performance and cycling stability with a high capacity retention of 88.7% after 200 cycles,well highlighting the Na_(4)TDC anode material for SIBs.

关键词： Organic anode material Low redox potential Composite anode Sodium-ion batteries

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A Survey of Artificial Hummingbird Algorithm and Its Variants: Statistical Analysis, Performance Evaluation, and Structural Reviewing

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Archives of Computational Methods in engineering 2025年第1期32卷 269-310页

作者： Hosseinzadeh, Mehdi Rahmani, Amir Masoud Husari, Fatimatelbatoul Mahmoud Alsalami, Omar Mutab Marzougui, Mehrez Nguyen, Gia Nhu Lee, Sang-Woong Institute of Research and Development Duy Tan University Da Nang Viet Nam School of Medicine and Pharmacy Duy Tan University Da Nang Viet Nam Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan Department of Communication and Computer Engineering Faculty of Engineering Cihan University-Erbil Kurdistan Region Iraq Department of Electrical Engineering College of Engineering Taif University Taif21944 Saudi Arabia Department of Computer Engineering College of Computer Science King Khalid University Abha61413 Saudi Arabia School of Computer Science Duy Tan University Da Nang Viet Nam Pattern Recognition and Machine Learning Lab Gachon University 1342 Seongnamdaero Sujeonggu Seongnam13120 Korea Republic of

In the last few decades, metaheuristic algorithms that use the laws of nature have been used dramatically in numerous and complex optimization problems. The artificial hummingbird algorithm (AHA) is one of the metaheuristic algorithms that was invented in 2022 based on the foraging and migration behavior of the hummingbird for modeling and solving optimization problems. The algorithm initially starts with an initial random population of solutions. It then uses iterative processes and hummingbird position updates to balance exploration and exploitation toward the most optimal solutions. This paper has a detailed and extensive review of the AHA algorithm considering the aspects of hybrid, improved, binary, multi-objective, and optimization problems. In addition, a wide range of applications of AHA in various fields such as feature selection, image processing, scheduling, Internet of Things, classification, clustering, financial and economic issues, forecasting, wireless sensor networks, and many engineering challenges are explored. The statistical and numerical results showed that the AHA algorithm with deep learning methods, Levy flight, and opposition-based learning had the best performance. Also, the AHA algorithm is most widely used in solving multimodal optimization problems and continuous functions. © The Author(s) under exclusive licence to International Center for Numerical Methods in engineering (CIMNE) 2024.

关键词： Optimization

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A Novel Diversified API Recommendation for Power System Sensors 17

A Novel Diversified API Recommendation for Power System Sens...

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IEEE Congress on Cybermatics: 17th IEEE International Conference on Internet of Things, iThings 2024, 20th IEEE International Conference on Green Computing and Communications, GreenCom 2024, 17th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2024, 10th IEEE International Conference on Smart Data, SmartData 2024

作者： Zhu, Minhao Gu, Huanhuan Che, Xun Chen, Jingfei Zhao, Qian Liu, Fan Zheng, Yu Nari Group Corporation Nanjing China Nanjing University of Science and Technology School of Cyber Science and Engineering Nanjing China Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing China Guodian Nanjing Automation Co. Ltd. Nanjing China

ISBN: (纸本)9798350351637

The collaborative interaction between power systems and the Internet of Things (IoT) is strengthening, with IoT devices facilitating real-time monitoring and governance of the power grid, pushing power systems to the next frontier: the smart grid. Nevertheless, the reliance of power systems on a vast array of IoT devices, each with its unique API, makes the development of a unified smart grid software solution extremely complex. Most existing research focuses on accuracy in recommendations, thus neglecting users' needs for functional diversity. To address this issue, we propose a diversified API recommendation approach that suggests a variety of functional APIs from power system sensors to users. We start by converting API labels into feature vectors based on a pre-trained language model and calculate the similarity between APIs through clustering. Subsequently, we construct an API graph to model the functional similarity relationships between APIs. Finally, we generate a minimum weighted tree to obtain a combination of APIs that meets the requirements and ensures diversity. We demonstrate the advantages of our proposed method in terms of diversity through a case study. © 2024 IEEE.

关键词： Power systems computer aided design

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Remote Sensing Data-Based Satellite Image Analysis in Water Quality Detection for Public Health Data Modelling

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 532-541页

作者： Balakrishnan, S. Preetam Raj, P Michael Somasekar, J. Kumar, Kambala Vijaya Amutha, S. Sangeetha, A. Department of Computer Science and Engineering Aarupadai Veedu Institute of Technology (AVIT) Vinayaka Mission’s Research Foundation (Deemed to be University) Chennai India Department of Computer Science and Engineering MLR Institute of Technology Telangana Dundigal Hyderabad 500043 India Department of Computer Science and Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be-University) Karnataka Bengaluru 562112 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram AP India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R & amp D Institute of Science and Technology Tamilnadu Chennai India Department of Artificial Intelligence and Data Science Kongunadu College of Engineering and Technology TamilNadu Tholurpatti India

One of the most important aspects of environmental sustainability is water quality monitoring. The ecology is impacted by poor water quality in addition to aquatic life. With the volume of data being collected from satellite systems, the issue of effectively and automatically extracting water bodies is growing more pressing. One of the most important things to do for managing and safeguarding water resources is to conduct extensive monitoring of water quality parameters, or WQPs. It is still difficult to monitor optically inactive WQPs in inland waters, such as total nitrogen (TN), ammoniacal nitrogen (AN), and total phosphorus (TP). Water quality (WQ) classification and prediction can be greatly enhanced with the use of artificial intelligence (AI). This research proposes novel method in healthcare detection by water quality analysis based on classification utilising ML model. Input is collected as healthcare dataset based on water quality as well as processed for noise removal and normalisation. The processed data features are extracted and classified utilising reinforcement Gaussian stacked layer network with variational fuzzy Markov basis neural network. In order to suggest best course of action for the water bodies, it is imperative that any study look into the spatiotemporal changes of the dominant water quality parameters (WQPs). WQP concentrations have typically been determined by extensive fieldwork. These findings could help identify and regulate non-point source pollution, offer a large-scale spatial picture of the water quality, and pinpoint vulnerable locations and times of water pollution. Experimental analysis is carried out for various water sample dataset in terms of detection accuracy, mean precision, recall, F-1 score and AUC. The proposed technique obtained 97% of detection accuracy, 95% of recall, 91% of F-1 score, 96% of AUC, and 94% of MAP. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Classification Fuzzy Markov basis Healthcare detection Machine learning model Water quality analysis

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Cyberbullying and Cyberviolence Detection:A Triangular User-Activity-Content View

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IEEE/CAA Journal of Automatica Sinica 2022年第8期9卷 1384-1405页

作者： Shuwen Wang Xingquan Zhu Weiping Ding Amir Alipour Yengejeh Department of Electrical Engineering and Computer Science Florida Atlantic UniversityBoca RatonFL 33431 USA School of Information Science and Technology Nantong UniversityNantong 226019China

Recent years have witnessed the increasing popularity of mobile and networking devices,as well as social networking sites,where users engage in a variety of activities in the cyberspace on a daily and real-time *** such systems provide tremendous convenience and enjoyment for users,malicious usages,such as bullying,cruelty,extremism,and toxicity behaviors,also grow noticeably,and impose significant threats to individuals and *** this paper,we review computational approaches for cyberbullying and cyberviolence detection,in order to understand two major factors:1)What are the defining features of online bullying users,and 2)How to detect cyberbullying and *** achieve the goal,we propose a user-activities-content(UAC)triangular view,which defines that users in the cyberspace are centered around the UAC triangle to carry out activities and generate ***,we categorize cyberbully features into three main categories:1)User centered features,2)Content centered features,and 3)Activity centered *** that,we review methods for cyberbully detection,by taking supervised,unsupervised,transfer learning,and deep learning,etc.,into *** UAC centered view provides a coherent and complete summary about features and characteristics of online users(their activities),approaches to detect bullying users(and malicious content),and helps defend cyberspace from bullying and toxicity.

关键词： Classification clustering cyberbullying natural language processing social network

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