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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Lu, Feng Hou, Yuxiang Li, Wei Yang, Xiangying Zheng, Haibo Luo, Wenxi Chen, Leqing Cao, Yuyang Liao, Xiaofei Zhang, Yu Yang, Fan Zomaya, Albert Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China Australia-China Joint Research Centre for Energy Informatics and Demand Response Technologies Centre for Distributed and High Performance Computing School of Computer Science University of Sydney Australia Tongji Hospital Tongji Medical College Huazhong University of Science and Technology China

ISBN: (纸本)157735897X

Breast cancer remains a leading cause of mortality among women, with millions of new cases diagnosed annually. Early detection through screening is crucial. Using neural networks to improve the accuracy of breast cancer screening has become increasingly important. In accordance with radiologists’ practices, we propose using images from the unaffected side to create adversarial samples with critical medical implications in our adversarial learning process. By introducing beneficial perturbations, this method aims to reduce overconfidence and improve the precision and robustness of breast cancer classification. Our proposed framework is an adversarial four-view classification network (NaFV-Net) incorporating images from both affected and unaffected perspectives. By comprehensively capturing local and global information and implementing adversarial learning from four mammography views, this framework allows for the fusion of features and the integration of medical principles and radiologist evaluation techniques, thus facilitating the accurate identification and characterization of breast tissues. Extensive experiments have shown the high effectiveness of our model in accurately distinguishing between benign and malignant findings, demonstrating state-of-the-art classification performance on both internal and public datasets. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Mammography

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Electronic Circuit in the Two-Scroll Hyperchaotic System with a Unique Saddle-Point Rest Point and its Synchronization via Integral Sliding Mode Control

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IAENG International Journal of Applied Mathematics 2024年第3期54卷 452-464页

作者： Mujiarto Ayob, Ahmad Faisal Mohamad Vaidyanathan, Sundarapandian Sambas, Aceng Benkouider, Khaled Nawawi, Mohd Kamal Mohd Khalid, Kamal Madi, Elissa Nadia Department of Mechanical Engineering Universitas Muhammadiyah Tasikmalaya Indonesia Department of Mechanical Engineering Universiti Malaysia Terengganu Malaysia Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Terengganu Malaysia Research and Development Centre Vel Tech University Avadi Tamil Nadu Chennai600062 India Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Kuala TerengganuGong Badak Malaysia Department of Mechanical Engineering Universitas Muhammadiyah Tasikmalaya Indonesia Department of Electronics Faculty of technology Badji-Mokhtar University Annaba23000 Algeria Institute of Strategic Industrial Decision Modeling School of Quantitative Sciences Universiti Utara Malaysia UUM SintokKedah 06010 Malaysia Institute of Strategic Industrial Decision Modeling School of Quantitative Sciences Universiti Utara Malaysia UUM SintokKedah 06010 Malaysia

A new 4-D two-scroll hyperchaotic system with three quadratic nonlinearities is detailed in this research paper. It is shown that the new hyperchaotic system has a unique saddle-point rest point at the origin and it exhibits self-excited attractor. A detailed stability analysis is carried out for the new hyperchaotic *** is also proved that the new hyperchaotic system has multistability with coexisting attractors. Next, we invoke integral sliding mode control for the global self-synchronization of the new hyperchaotic system taken as leader-follower *** electronic circuit design of the new two-scroll hyperchaotic system is developed in MultiSim, which is beneficial for engineering applications. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Sliding mode control

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FBM-FA: Frame-Level-Attention-Based Facial Behavior Mining for Face Anti-Spoofing

FBM-FA: Frame-Level-Attention-Based Facial Behavior Mining f...

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International Conference on Computer Supported Cooperative Work in Design

作者： Gaoyu Luo Zhibin Gao Sheng Zhang Lianfen Huang Linran Chen School of Informatics Xiamen University Xiamen China Navigation Institute Jimei University Xiamen China College of Computing and Information Science Fuzhou Institute of Technology Fuzhou China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Face anti-spoofing (FAS) is crucial for enhancing the security of face recognition systems. Although previous multi-frame face anti-spoofing methods utilized consecutive images for decision fusion, they lacked the extraction and detection of facial behavior, which is an essential attribute to distinguish real faces from presentation attacks (PAs). Besides, people tend to display unconscious facial behaviors at specific moments, other intervals are often redundant for classification. To address these issues, in this paper, we propose the frame-level-attention-based facial behavior mining for FAS (FBM-FA). An adaptive key-frame mining module is introduced to locate potential peak frames of facial behavior in a video. Moreover, a two-stream feature extraction network is designed to extract temporal features between facial behaviors and single facial behavior representations from position information. Comprehensive experiments are performed on four datasets including OULU-NPU, 3DMAD, CASIA-FASD, and REPLAY-ATTACK. Experiments demonstrate that our method can achieve perfect detection performance on 2D and 3D datasets.

关键词： Protocols Three-dimensional displays Error analysis Face recognition Feature extraction Vectors Skin

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RGB Color Model Aware Computational Color Naming and Its Application to Data Augmentation

RGB Color Model Aware Computational Color Naming and Its App...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Yan, Zipei Xu, Linchuan Suzuki, Atsushi Wang, Jing Cao, Jiannong Huang, Jun The Hong Kong Polytechnic University Department of Computing Kowloon Hung Hom Hong Kong King's College London Department of Informatics London United Kingdom University of Greenwich Department of Computing and Mathematical Sciences London United Kingdom Anhui University of Technology School of Computer Science and Technology Ma'anshan China

ISBN: (纸本)9781665480451

Computational color naming (CCN) aims to learn a mapping from pixels into semantic color names, e.g., red, green and blue. CCN has wide applications including color vision deficiency assistance and color image retrieval. Existing research on CCN mainly studies pixels collected under laboratory settings or studies images collected from the web. However, laboratory pixels are very limited such that the learned mapping may not generalize well on unseen pixels, and the mapping discovered from images is usually data-specific. In this paper, we aim to learn a universal mapping by studying pixels collected from the web. To this end, we formulate a novel classification problem that incorporates both the pixels and the RGB color model. The RGB color model is beneficial for learning the mapping because it characterizes the production of colors, e.g., the addition of red and green produces yellow. However, the characterization is rather qualitative. To solve this problem, we propose ColorMLP, which is a multilayer perceptron (MLP) embedded with graph attention networks (GATs). Here, the GATs are designed to capture color relations that we construct by referring to the RGB color model. In this way, the parameters of the MLP can be regularized to comply with the RGB model. We conduct comprehensive experiments to demonstrate the superiority of ColorMLP to alternative *** expand the application of CCN, we design a novel data augmentation method named partial color jitter (PCJ), which performs color jitter (CJ) on a subset of pixels belonging to the same color of an image. In this way, PCJ partially changes the color properties of images, thereby significantly increasing images' diversity. We conduct extensive experiments on CIFAR10/100 and ImageNet datasets, showing that PCJ can consistently improve the classification performance. Our data and software can be found at https://https://***/yanzipei/CCN_and_ItsApp. © 2022 IEEE.

关键词： Mapping

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INCREASE: Inductive Graph Representation Learning for Spatio-Temporal Kriging

arXiv

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arXiv 2023年

作者： Zheng, Chuanpan Qi, Jianzhong Fan, Xiaoliang Chen, Chaochao Wang, Cheng Chen, Longbiao Fujian Key Laboratory of Sensing and Computing for Smart Cities School of Informatics Xiamen University China School of Computing and Information Systems University of Melbourne Australia College of Computer Science and Technology Zhejiang University China

Spatio-temporal kriging is an important problem in web and social applications, such as Web or Internet of Things, where things (e.g., sensors) connected into a web often come with spatial and temporal properties. It aims to infer knowledge for (the things at) unobserved locations using the data from (the things at) observed locations during a given time period of interest. This problem essentially requires inductive learning. Once trained, the model should be able to perform kriging for different locations including newly given ones, without retraining. However, it is challenging to perform accurate kriging results because of the heterogeneous spatial relations and diverse temporal patterns. In this paper, we propose a novel inductive graph representation learning model for spatio-temporal kriging. We first encode heterogeneous spatial relations between the unobserved and observed locations by their spatial proximity, functional similarity, and transition probability. Based on each relation, we accurately aggregate the information of most correlated observed locations to produce inductive representations for the unobserved locations, by jointly modeling their similarities and differences. Then, we design relation-aware gated recurrent unit (GRU) networks to adaptively capture the temporal correlations in the generated sequence representations for each relation. Finally, we propose a multi-relation attention mechanism to dynamically fuse the complex spatio-temporal information at different time steps from multiple relations to compute the kriging output. Experimental results on three real-world datasets show that our proposed model outperforms state-of-the-art methods consistently, and the advantage is more significant when there are fewer observed locations. Our code is available at https://***/zhengchuanpan/INCREASE. Copyright © 2023, The Authors. All rights reserved.

关键词： Kriging

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Editorial: A glimpse of EVC2023

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Transportation Research Procedia 2023年 70卷 1-4页

作者： Muhammad-Sukki, Firdaus Bani, Nurul Aini Tingas, Efstathios-Al School of Computing Engineering and the Built Environment Edinburgh Napier University Merchiston Campus 10 Colinton Road Edinburgh EH10 5DT United Kingdom Razak Faculty of Technology and Informatics Universiti Teknologi Malaysia Jalan Sultan Yahya Petra Kuala Lumpur 54100 Malaysia

The Editorial describes the main aim and results of the 8th International Electric Vehicle Conference (EVC2023) held in Edinburgh Napier University, Edinburgh, United Kingdom from 21 – 23 June 2023.

关键词： electric vehicle policy technology

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Multivariate Aggregated NOMA for Resource Aware Wireless Network Communication Security

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Computers, Materials & Continua 2023年第1期74卷 1693-1708页

作者： V.Sridhar K.V.Ranga Rao Saddam Hussain Syed Sajid Ullah Roobaea Alroobaea Maha Abdelhaq Raed Alsaqour ECE Department Vidya Jyothi Institute of TechnologyIndia CSE Department Neil Gogte Institute of TechnologyIndia School of Digital Science Universiti Brunei DarussalamJalanTungku LinkGadong BE1410Brunei Darussalam Department of Information and Communication Technology University of Agder(UiA)N-4898 GrimstadNorway Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourahbint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia Department of Information Technology College of Computing and InformaticsSaudi Electronic University93499RiyadhSaudi Arabia

NonorthogonalMultiple Access(NOMA)is incorporated into the wireless network systems to achieve better connectivity,spectral and energy effectiveness,higher data transfer rate,and also obtain the high quality of services(QoS).In order to improve throughput and minimum latency,aMultivariate Renkonen Regressive Weighted Preference Bootstrap Aggregation based Nonorthogonal Multiple Access(MRRWPBA-NOMA)technique is introduced for network *** the downlink transmission,each mobile device’s resources and their characteristics like energy,bandwidth,and trust are *** by,the Weighted Preference Bootstrap Aggregation is applied to recognize the resource-efficient mobile devices for aware data transmission by constructing the different weak hypotheses i.e.,Multivariate Renkonen Regression *** on the classification,resource and trust-aware devices are selected for *** of the proposed MRRWPBA-NOMA technique and existing methods are carried out with different metrics such as data delivery ratio,throughput,latency,packet loss rate,and energy efficiency,signaling *** simulation results assessment indicates that the proposed MRRWPBA-NOMA outperforms well than the conventional methods.

关键词： Mobile network multivariate renkonen regression weighted preference bootstrap aggregation resource-aware secure data communication NOMA

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A Knowledge-Based System to Predict Crime from Criminal Records in the Case of Hossana Police Commission

A Knowledge-Based System to Predict Crime from Criminal Reco...

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Information and Communication technology for Development for Africa (ICT4DA), International Conference on

作者： Betelhem Zewdu Wubineh Biniyam Yirsaw Metekiya Faculty of Information and Communication Technology Wroclaw University of Science and Technology Wroclaw Poland Department of Information Technology School of Computing & Informatics Wachemo University Hossana Ethiopia Department of Information System College of Informatics University of Gondar Gondar Ethiopia

Crime is a complex social phenomenon, and its cost continues to rise over time. Police have collected a large amount of data on criminals every day for different crimes. However, extracting valuable information from these data takes time and effort for the law enforcement organization at hand. Therefore, effective crime prevention strategies and policies are necessary to prevent crime. Criminology is one of the most important areas for data mining. The purpose of this study is to extract useful crime patterns from past data and develop a knowledge-based system to prevent crime. Different criminals commit different crimes, but we do not know which criminal commits which crime. For this purpose, the 910 criminal records are used to train and test the model. To predict a crime, the classification technique and the J48 decision tree, random forest (RF), PART, and ensemble algorithms are used. As a result, the algorithm scored an accuracy of 75.38%, 80.4%,73.7%, and 81.3% in the J48, RF, PART, and ensemble methods, respectively, in a 10- fold cross-validation. Additionally, the offender's job is the determining factor that causes and predicts crimes. Finally, we integrate the rules extracted from a data mining task into a knowledge base system using PROLOG, which is used to make decisions.

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A Feasible Compression Scheme for Bangla Natural Text

A Feasible Compression Scheme for Bangla Natural Text

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Innovative Data Communication Technologies and Application (ICIDCA), International Conference on

作者： Md. Ashiq Mahmood Md Ashiqul Amin Pallab Kumar Nandi Bishwajit Kumar Mondal Institute of Information and Communication Technology Khulna University of Engineering & Technology Khulna Bangladesh School of Informatics Computing and Cyber System Northern Arizona University AZ USA

The most significant zone of study for Natural Language Processing (NLP) is text compression. Since the number of characters in Unicode are many, 16 bits are needed to process the entire code. This study suggests a useful method for compressing Bangla natural text called the 5 Bit Compression Scheme for Bangla Text(5BCS), which uses a 5-bit encoding method to change the 16-bit Bangla letters into a 5-bit format. This technique provides an encoding algorithm which representing the 16-bit Bangla characters to 5 bits by utilizing a query table. The query table contains 4 sets which comprises all the Bangla characters as well as transliterations and special symbols. An m-series technique is used for logically determining index of a given characters combination while translating characters into 5 bits. By utilizing the 5BCS, a natural text can be compressed by a maximum of 84%. The algorithm for decompress the compressed data to translate the original data is also detailed in this paper. Additionally, a comparison section is developed to compare our 5BCS technique with the widely used Huffman and LZW technique. The experimental result of the comparison looks very much optimistic.

关键词： Dictionaries Codes Databases Symbols Programming Load management Encoding

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LibreHealth Cost-of-Care Explorer: Mobile Application for Patient-friendly Access to Hospital Chargemasters

LibreHealth Cost-of-Care Explorer: Mobile Application for Pa...

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2022 IEEE-EMB Special Topic Conference on Healthcare Innovations and Point of Care Technologies, HI-POCT 2022

作者： Merine, Regina Pinnamraju, Jahnavi Singh, Darshpreet Gichoya, Judy W. Purkayastha, Saptarshi Indiana University Purdue University Indianapolis School of Informatics and Computing IndianapolisIN46202 United States Institute of Technology Management New Delhi10053 India Emory University School of Medicine Emory University AtlantaGA30322 United States

ISBN: (纸本)9781665496155

The Centers for Medicare Medicaid Services (CMS) mandated that Charge Description Master (CDM) be available online in a machine-readable format by January 1, 2019. These changes were made to increase availability of hospital price information in order to provide patients with more access to their health information and allow physicians to spend more time with their patients. However, the mandated machine-readable formats make it impossible for a patient to interpret the pricing and hence limit price transparency. To bridge this gap, we developed a mobile application that can be used by patients to reveal the cost of a medical procedure or service before receiving it. Patients are able to compare the costs of medical procedures available in surrounding hospitals by viewing the hospital's CDM and determine which hospital delivers the best care/medical procedures at the best price. Clinical relevance - The LibreHealth cost-of-care explorer app presents the cost of medical procedures in a consumer-friendly format. It provides patient-friendly cost estimates for medical procedures performed in US hospitals and promotes price transparency to the end users. © 2022 IEEE.

关键词： Hospitals

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