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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Long, Tao Zhang, Lei Zhang, Liang Cui, Laizhong College of Computer Science and Software Engineering Shenzhen University Guangdong Shenzhen China Shenzhen Research Institute of Big Data Guangdong Shenzhen China

ISBN: (纸本)157735897X

With the advancement of graph representation learning, self-supervised graph contrastive learning (GCL) has emerged as a key technique in the field. In GCL, positive and negative samples are generated through data augmentation. While recent works have introduced model-based methods to enhance positive graph augmentations, they often overlook the importance of negative samples, relying instead on rule-based methods that can fail to capture meaningful graph patterns. To address this issue, we propose a novel model-based adversarial contrastive graph augmentation (ACGA) method that automatically generates both positive graph samples with minimal sufficient information and hard negative graph samples. Additionally, we provide a theoretical framework to analyze the process of positive and negative graph augmentation in self-supervised GCL. We evaluate our ACGA method through extensive experiments on representative benchmark datasets, and the results demonstrate that ACGA outperforms state-of-the-art baselines. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Contrastive Learning

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ION-C: Integration of Overlapping Networks via Constraints

arXiv

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arXiv 2024年

作者： Nair, Praveen Bhandari, Payal Abavisani, Mohammadsajad Plis, Sergey Danks, David Department of Computer Science and Engineering University of California San Diego United States Department of Electrical and Computer Engineering Georgia Institute of Technology United States TReNDS Center Department of Computer Science Georgia State University United States Halicioglu Data Science Institute Department of Philosophy University of California San Diego United States

In many causal learning problems, variables of interest are often not all measured over the same observations, but are instead distributed across multiple datasets with overlapping variables. Tillman et al. [2008] presented the first algorithm for enumerating the minimal equivalence class of ground-truth DAGs consistent with all input graphs by exploiting local independence relations, called ION. In this paper, this problem is formulated as a more computationally efficient answer set programming (ASP) problem, which we call ION-C, and solved with the ASP system clingo. The ION-C algorithm was run on random synthetic graphs with varying sizes, densities, and degrees of overlap between subgraphs, with overlap having the largest impact on runtime, number of solution graphs, and agreement within the output set. To validate ION-C on real-world data, we ran the algorithm on overlapping graphs learned from data from two successive iterations of the European Social Survey (ESS), using a procedure for conducting joint independence tests to prevent inconsistencies in the input. © 2024, CC BY.

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Pre-Training and Fine-Tuning for Efficient Routing in Opportunistic Networks

Pre-Training and Fine-Tuning for Efficient Routing in Opport...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Jia Hao Xiaorui Wu Winston K. G. Seah Feng Zhang Gang Xu Ministry of Education College of Computer Science Engineering Research Center of Ecological Big Data Inner Mongolia University Hohhot China School of Engineering & Computer Science Victoria University of Wellington Wellington New Zealand School of Computer and Information Technology Shanxi University Taiyuan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

In opportunistic networks, it is a challenge to find the best relay node instead of blindly selecting from among available nodes to forward messages and effectively transmit them to their destinations. By comparing the similarity between nodes, the next hop node that is most similar to the destination node is found in time. Existing node similarity-based opportunistic networks routing algorithms only calculate similarity between nodes according to nodes' properties that can be directly obtained from the network, such as the historical meeting record between nodes, etc. However, this superficial similarity calculation is obviously inadequate to describe the inherent dynamic nature of opportunistic networks at both spatial and temporal levels, and ignores the salient movement characteristics of nodes, resulting in poor routing performance. Therefore, this paper proposes an opportunistic network routing strategy based on pre-training and fine-tuning (PTFT) model. Firstly, an autoencoder is added to the graph neural network model to encode node movement behavior. Then, a pre-training graph neural network model in large scale opportunistic network scenarios is applied to learn potential features of nodes through fine-tuning. Finally, we calculate the similarity between nodes based on the node potential feature, thereby assisting the nodes to achieve an efficient routing decision. The simulation results show that our PTFT-based algorithm not only has superior performance compared to traditional routing algorithms, but also faster learning speed than other machine learning-based routing algorithms.

关键词： Representation learning Machine learning algorithms Simulation Heuristic algorithms Autoencoders Routing Feature extraction Graph neural networks Relays Cybernetics

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Follow Everything: A Leader-Following and Obstacle Avoidance Framework with Goal-Aware Adaptation

arXiv

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arXiv 2025年

作者： Zhang, Qianyi Ma, Shijian Liu, Boyi Liu, Jingtai Jiao, Jianhao Kanoulas, Dimitrios Institute of Robotics and Automatic Information System Nankai University Tianjin300350 China Electrical and Computer Engineering Department Hong Kong University of Science and Technology China Centre for Data Science University of Macau China Department of Computer Science University College London United Kingdom

Robust and flexible leader-following is a critical capability for robots to integrate into human society. While existing methods struggle to generalize to leaders of arbitrary form and often fail when the leader temporarily leaves the robot’s field of view, this work introduces a unified framework addressing both challenges. First, traditional detection models are replaced with a segmentation model, allowing the leader to be anything. To enhance recognition robustness, a distance frame buffer is implemented that stores leader embeddings at multiple distances, accounting for the unique characteristics of leader-following tasks. Second, a goal-aware adaptation mechanism is designed to govern robot planning states based on the leader’s visibility and motion, complemented by a graph-based planner that generates candidate trajectories for each state, ensuring efficient following with obstacle avoidance. Simulations and real-world experiments with a legged robot follower and various leaders (human, ground robot, UAV, legged robot, stop sign) in both indoor and outdoor environments show competitive improvements in follow success rate, reduced visual loss duration, lower collision rate, and decreased leader-follower distance. Visit the website for the video and code. © 2025, CC BY.

关键词： Motion planning

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Leveraging Deep Learning for Improved Sentiment Analysis in Natural Language Processing 3

Leveraging Deep Learning for Improved Sentiment Analysis in ...

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3rd Odisha International Conference on Electrical Power engineering, Communication and Computing Technology, ODICON 2024

作者： Kulkarni, Aniket Gollavilli, Venkata Surya Bhavana Harish Alsalami, Zaid Bhatia, Manpreet Kaur Jovanovska, Sashka Absur, Md Nurul Drata Inc San DiegoCA United States Under Armour MD United States College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Asian School of Media Studies Department of Data Science Noida India Goce Delcev University Faculty of Philology Department of English Language and Literature Stip Macedonia Cuny Graduate Center Computer Science New York United States

ISBN: (纸本)9798350354379

Sentiment analysis is viewed as quite possibly of the main works in mental science and normal language handling. To work on the productivity of sentiment analysis techniques, it is crucial for separate the useful words that add to the grouping choice as well as characterize the sentences as indicated by their profound names. Profound brain networks that depend on the consideration component have taken huge steps toward this path as of late. Reads up on consideration processes for message arrangement, and especially sentiment analysis, are as yet not many, in any case. This research fills this gap by presenting a Convolution Neural Network (CNN) combined with an attention layer that can extract relevant words and give them greater weights according to the context. The suggested model uses a context vector at the attention layer and attempts to gauge a word's relevance based on how similar the word vector and context vector are to one another. New vectors from the consideration layer are incorporated into sentence vectors and utilised for organization once they have been supplied. The suggested model was validated using a small number of tests on the Stanford datasets. The trial discoveries show that the recommended model works far superior to past research studies and can separate significant expressions from setting that have an incentive for analysis and application. © 2024 IEEE.

关键词： Convolutional neural networks

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4D foetal cardiac ultrasound image detection based on deep learning with weakly supervised localisation for rapid diagnosis of evolving hypoplastic left heart syndrome

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CAAI Transactions on Intelligence Technology 2024年第6期9卷 1485-1499页

作者： Gang Wang Weisheng Li Mingliang Zhou Haobo Zhu Guang Yang Choon Hwai Yap Chongqing Key Laborotory of Image Rocognition Chongqing University of Posts and TelecommunicationsChongqingChina School of Computing and Data Engineering NingboTech UniversityNingboChina Department of Bioengineering Imperial College LondonLondonUK Key Laboratory of Cyberspace Big Data Intelligent Security(Chongqing University of Posts and Telecommunications) Ministry of EducationChongqingChina School of Computer Science Chongqing UniversityChongqingChina University of Oxford OxfordUK Cardiovascular Research Centre Royal Brompton HospitalLondonUK National Heart and Lung Institute Imperial College LondonLondonUK

Hypoplastic left heart syndrome(HLHS)is a rare,complex,and incredibly foetal congenital heart *** decrease neonatal mortality,evolving HLHS(eHLHS)in pregnant women should be critically diagnosed as soon as ***,diagnosis is currently heavily dependent on skilled medical professionals using foetal cardiac ultrasound images,making it difficult to rapidly and easily examine for this ***,the authors propose a cost-effective deep learning framework for rapid diagnosis of eHLHS(RDeH),which we have named ***,the framework implements a coarseto-fine two-stage detection approach,with a structure classification network for 4D human foetal cardiac ultrasound images from various spatial and temporal domains,and a fine detection module with weakly-supervised localisation for high-precision nidus localisation and physician *** experiments extensively compare the authors’network with other state-of-the-art methods on a 4D human foetal cardiac ultrasound image dataset and show two main benefits:(1)it achieved superior average accuracy of 99.37%on three categories of foetal ultrasound images from different cases;(2)it demonstrates visually fine detection performance with weakly supervised *** framework could be used to accelerate the diagnosis of eHLHS,and hence significantly lessen reliance on experienced medical physicians.

关键词： 4D deep learning fetal cardiac ultrasound hypoplastic left heart syndrome weakly-supervised learning

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The Utilization of Google Cloud in the Healthcare Industry

The Utilization of Google Cloud in the Healthcare Industry

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Rishikesh Kapse Palash Gourshettiwar Dept. of Artificial Intelligence and Data Science Faculty of Engineering and Technology DMIHER (DU) Sawangi(M) Wardha Maharashtra Dept. of Computer Science And Medical Engineering Faculty of Engineering and Technology DMIHER (DU) Sawangi(M) Wardha Maharashtra

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

This essay's primary focus is on Google Cloud's use in healthcare and how it affects data management, teamwork, and cost-effectiveness. With a focus on Google Cloud, which improves the processing, storing, and accessibility of medical data, the study examines the trends, advantages, and persistent challenges that come with the increasing usage of cloud computing technology. These consist of remote patient monitoring, “artificial intelligence,” and “machine learning” applications in diagnostics, diagnostics, and personalized medicine. data privacy, security issues, and concerns with the HIPAA incident are among the problems that are discussed along with all the significant elements that could facilitate the cloud migration of healthcare.

关键词： Industries Cloud computing data privacy Technological innovation Medical services Machine learning Teamwork Security Predictive analytics Medical diagnostic imaging

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HATTM: A Novel Hybrid Attention Model for Ethereum Phishing Scams Detection

HATTM: A Novel Hybrid Attention Model for Ethereum Phishing ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Bo Cui Zhenyu Zhang Wenhan Hou College of Computer Science Inner Mongolia University Hohhot China Engineering Research Center of Ecological Big Data Ministry of Education Hohhot China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Detecting Ethereum phishing scams is extremely urgent. In this paper, we propose a novel Hybrid Attention Model for Ethereum phishing scams detection called HATTM to solve the problem of irregular transaction series in Ethereum, fully extract account features and then improve detection performance. Specifically, we take a novel perspective by regarding each transaction of an account as a separate amount-time point to handle irregular data. In the hybrid attention model, we capture intra-account and inter-account trading features through intra-account attention of EPS-FORMER and inter-account attention of EPSGAT, respectively. We further extract Intra-account and Inter-account statistical features to enrich the account representation. The complete representation of accounts is composed of the above four types of features to detect phishing accounts. Experimental results on the real-world Ethereum dataset show that HATTM outperforms existing models and is far ahead in the recall, which indicates that our model can effectively detect Ethereum phishing scams.

关键词： Phishing Signal processing Feature extraction data models Acoustics Blockchains data mining Speech processing

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Prediction of Ionospheric TEC Based on BLS-LSTM-GRU Hybrid Model 14th

Prediction of Ionospheric TEC Based on BLS-LSTM-GRU Hybrid M...

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14th China Satellite Navigation Conference, CSNC 2024

作者： Li, Dengao Zhang, Xinfang Zhao, Jumin Wu, Fanming Feng, Ran Zhao, Jinhua Shi, Danyang College of Data Science Taiyuan University of Technology Taiyuan030024 China College of Information and Computer Taiyuan University of Technology Taiyuan030024 China Key Laboratory of Big Data Fusion Analysis and Application of Shanxi Province Taiyuan030024 China Intelligent Perception Engineering Technology Center of Shanxi Taiyuan030024 China Shanxi Province Engineering Technology Research Center of Spatial Information Network Taiyuan030024 China

ISBN: (纸本)9789819969272

In satellite communications, global navigation satellite systems(GNSS) and other important space activities, the value of total electron content (TEC) in the ionosphere directly affects the size of ionospheric delay. Achieving accurate prediction of ionospheric total electron content through modeling can effectively improve the reliability and accuracy of GNSS positioning. Considering the high prediction accuracy of long short-term memory neural network (LSTM) and the fast training speed of gated recurrent unit network (GRU), but it is easy to fall into the local optimum, while the width learning system(BLS) can effectively avoid this drawback. Therefore, this paper proposes a model for predicting the total electron content of the ionosphere, which consists of a broad learning system, an LSTM network and a GRU networks. Six parameters of TEC, geomagnetic index Dst, Kp, Ap, solar activity index F10.7, and hour (HD) are selected as input features from the observed data to predict the variation of the total ionospheric electron content. The results generate prediction maps of the total ionospheric electron content and are compared with empirical models and conventional neural network models at different times and solar activity conditions. The experimental results show that the root mean square error (RMSE) of the prediction results of this BLS-LSTM-GRU model is decreased by 23.07%, 17.48% and 9.46% compared with the IRI-2016, NeQuick and CNN-LSTM prediction models, respectively, with higher prediction accuracy. © 2024, Aerospace Information Research Institute.

关键词： Long short-term memory

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Optimizing Software Demands Using Fuzzy-Based Evaluation Techniques 1st

Optimizing Software Demands Using Fuzzy-Based Evaluation Tec...

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1st International Conference on Innovation and Emerging Trends in Computing and Information Technologies, IETCIT 2024

作者： Rubi Singh, Jagendra Sahu, Dinesh Prasad Tiwari, Mohit Shaik, Nazeer Shrivastav, A.K. School of Education Lovely Professional University Jalandhar India School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering Delhi India Department of Artificial Intelligence and Data Science Rajalakshmi Institute of Technology Tamil Nadu Chennai India Ambala Mullana India

ISBN: (纸本)9783031808418

The purpose of this study was to show the benefits of fuzzy logic in software scaling. We made use of fuzzy logic systems and logic, including sets rules and inference, to cope with uncertainty in making decisions;by the way all three of those have been used. We found different degrees of set membership during ten trials, which would change to reflect how much the software today was needed in real life. Decisions produced by fuzzy inference systems exhibited both adaptability and context-dependency, illustrating possible pathways toward better software demand optimization strategies. Further, we took measurements like response time, throughput, and resource utilization both before & after fuzzy optimization. In all experiments we put the system under realistic working conditions, looking for changes in speed, through better resource usage. The response time was reduced, throughput increased, and resource utilizations improved. These results suggest that fuzzy-based methods not only facilitate a better user experience but enhance software performance. In a word, this study represents an advance in software engineering optimization methods. It draws attention to software demand’s inherent uncertainties and emphasizes the importance of fuzzy logic. With fuzzy evaluation techniques, this research’s achievement was thus to open up practical means of improving decision-making processes and optimizing the vital performance indicator, enabling people to create computer systems that are more reliable and practical. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： computer circuits

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