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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Fangfang Zhang Yi Mei Su Nguyen Mengjie Zhang School of Engineering and Computer Science Victoria University of Wellington New Zealand Centre for Data Analytics and Cognition La Trobe University Melbourne Victoria Australia

ISBN: (纸本)9781665487696

Dynamic flexible job shop scheduling (DFJSS) is an important combinatorial optimisation problem with a large number of real-world applications such as component production in manufacturing. Genetic programming (GP), as a hyper-heuristic approach, has been widely used to learn scheduling heuristics for DFJSS. Brood recombination has been shown its effectiveness to improve the performance of GP by generating more offspring and preselecting only promising individuals into the next generation. However, evaluating more individuals requires more computational cost. Phenotype based surrogate models have been successfully used with GP to speed up the evaluation in single-objective dynamic job shop scheduling. However, its effectiveness on multi-objective dynamic job shop scheduling is unknown. To fill this gap, this paper proposes a novel surrogate-assisted multi-objective GP based on the phenotype of GP individuals for DFJSS. Specifically, we first use phenotypic vector to represent the behaviour of GP individuals in DFJSS. Second, K-nearest neighbour based surrogates are built according to the phenotypic characterisations and multiple fitness values of the evaluated individuals. Last, the built surrogate models are used to predict the fitness of newly generated offspring in GP with brood recombination. The results show that with the same training time, the proposed algorithm can achieve significantly better scheduling heuristics than the compared algorithm. The analyses of population diversity, feature importance, and the number of non-dominated individuals have also shown the effectiveness of the proposed algorithm in different aspects.

关键词： Training Job shop scheduling Processor scheduling Heuristic algorithms Sociology Genetic programming Production

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Deep Learning for Automated Classification of Kidney Lesions in Chronic Kidney Disease Patients

Deep Learning for Automated Classification of Kidney Lesions...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： B. Seetha Lakshmi Vijay Anand R Habeeb Omotunde M. Birundadevi Mahalakshmi S Madala Guru Brahmam Department of Artificial Intelligence and Data Science Sri Shanmugha College of Engineering and Technology Namakkal Tamilnadu India School of Computer Science Engineering & Information Systems Vellore Institute of Technology Vellore India Department of Information Systems College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University Riyadh Saudi Arabia Department of Maruthuvam Sri Sairam Siddha Medical College and Research Centre Chennai Department of Computer Science and Design R.M.K. Engineering College Chennai Tamilnadu India

Deep Learning was utilized to autonomously classify renal abnormalities, which improved the diagnostic accuracy for individuals with chronic kidney disease (CKD), according to this study. During training on a meticulously selected dataset, the proposed model outperformed the most recent approaches. By utilizing medical imaging data— specifically quantitative and qualitative data—to differentiate between discrete kidney abnormalities, the model demonstrated exceptional levels of accuracy, precision, and recall. Furthermore, apart from effectively resolving ethical considerations pertaining to data privacy, this innovative methodology has the potential to fundamentally transform the existing landscape of chronic kidney disease (CKD) diagnosis and treatment. The results demonstrate that the model effectively manages intricate renal disorders, signifying a noteworthy progression in the field of medical image processing. This research paves the way for additional fruitful lines of inquiry, which ought to enhance nephrology diagnostics and patient care.

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USED: Universal Speaker Extraction and Diarization

arXiv

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arXiv 2023年

作者： Ao, Junyi Yıldırım, Mehmet Sinan Tao, Ruijie Ge, Meng Wang, Shuai Qian, Yanmin Li, Haizhou Shenzhen Research Institute of Big Data School of Data Science The Chinese University of Hong Kong Shenzhen518172 China Department of Electrical and Computer Engineering National University of Singapore Singapore119077 Singapore Saw Swee Hock School of Public Health National University of Singapore Singapore117549 Singapore Shenzhen Research Institute of Big Data Shenzhen518172 China Auditory Cognition and Computational Acoustics Lab Department of Computer Science and Engineering China MoE Key Laboratory of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai200240 China

Speaker extraction and diarization are two enabling techniques for real-world speech applications. Speaker extraction aims to extract a target speaker’s voice from a speech mixture, while speaker diarization demarcates speech segments by speaker, annotating ‘who spoke when’. Previous studies have typically treated the two tasks independently. In practical applications, it is more meaningful to have knowledge about ‘who spoke what and when’, which is captured by the two tasks. The two tasks share a similar objective of disentangling speakers. Speaker extraction operates in the frequency domain, whereas diarization is in the temporal domain. It is logical to believe that speaker activities obtained from speaker diarization can benefit speaker extraction, while the extracted speech offers more accurate speaker activity detection than the speech mixture. In this paper, we propose a unified model called Universal Speaker Extraction and Diarization (USED) to address output inconsistency and scenario mismatch issues. It is designed to manage speech mixtures with varying overlap ratios and variable number of speakers. We show that the USED model significantly outperforms the competitive baselines for speaker extraction and diarization tasks on LibriMix and SparseLibriMix datasets. We further validate the diarization performance on CALLHOME, a dataset based on real recordings, and experimental results indicate that our model surpasses recently proposed approaches. Copyright © 2023, The Authors. All rights reserved.

关键词： Frequency domain analysis

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Automatic and Robust Segmentation of Multiple Sclerosis Lesions with Convolutional Neural Networks

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computers, Materials & Continua 2021年第1期66卷 977-991页

作者： H.M.Rehan Afzal Suhuai Luo Saadallah Ramadan Jeannette Lechner-Scott Mohammad Ruhul Amin Jiaming Li M.Kamran Afzal M.Kamran Afzal School of Electrical Engineering and Computing University of NewcastleCallaghanNSW 2308Australia Hunter Medical Research Institute New Lambton HeightsNSW 2305Australia Department of Neurology John Hunter HospitalNew Lambton HeightsNSW 2305Australia CSIRO Data61 MarsfieldNSW 2122Australia School of Computer Science and Technology Xiamen UniversityXiamen361005China

The diagnosis of multiple sclerosis(MS)is based on accurate detection of lesions on magnetic resonance imaging(MRI)which also provides ongoing essential information about the progression and status of the *** detection of lesions is very time consuming and lacks *** of the lesions are difficult to detect manually,especially within the grey *** paper proposes a novel and fully automated convolution neural network(CNN)approach to segment *** proposed system consists of two 2D patchwise CNNs which can segment lesions more accurately and *** first CNN network is implemented to segment lesions accurately,and the second network aims to reduce the false positives to increase *** system consists of two parallel convolutional pathways,where one pathway is concatenated to the second and at the end,the fully connected layer is replaced with *** routine MRI sequences T1-w,T2-w and FLAIR are used as input to the CNN,where FLAIR is used for segmentation because most lesions on MRI appear as bright regions and T1-w&T2-w are used to reduce MRI *** evaluated the proposed system on two challenge datasets that are publicly available from MICCAI and *** and qualitative evaluation has been performed with various metrics like false positive rate(FPR),true positive rate(TPR)and dice similarities,and were compared to current state-of-the-art *** proposed method shows consistent higher precision and sensitivity than other *** proposed method can accurately and robustly segment MS lesions from images produced by different MRI scanners,with a precision up to 90%.

关键词： Multiple sclerosis lesion segmentation automatic segmentation CNN automated tool lesion detection

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Mitigating Label-Noise for Facial Expression Recognition in the Wild

Mitigating Label-Noise for Facial Expression Recognition in ...

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Yan, Huan Gu, Yu Zhang, Xiang Wang, Yantong Ji, Yusheng Ren, Fuji School of Computer and Information Hefei University of Technology S2AC Lab China Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data China Anhui Province Key Laboratory of Cyberspace Security Situation Awareness and Evaluation China National Institute of Informatics Tokushima University Japan

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Label-noise constitutes a major challenge for facial expression recognition in the wild due to the ambiguity of facial expressions worsened by low-quality images. To deal with this problem, we propose a simple but effective Label-noise Robust Network (LRN) which explores the inter-class correlations for mitigating ambiguity that usually happens between morphologically similar classes. Specifically, LRN leverages a multivariate normal distribution to model such correlations at the final hidden layer of the neural network to suppress the heteroscedastic uncertainty caused by inter-class label noise. Furthermore, LRN utilizes a confidence-based label-free loss to extract compact intra-class feature representations under label noise while preserving the intrinsic inter-class relationships. Experiments on three in-the-wild facial expression datasets demonstrates the superiority of our method. © 2022 IEEE.

关键词： Face recognition

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AI-Assisted Digital Pathology for Computational distinction of adenocarcinoma vs squamous cell carcinoma

AI-Assisted Digital Pathology for Computational distinction ...

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International Conference on Mechatronics and Machine Vision in Practice (M2VIP)

作者： Vyomesh Jamwal Abzal Nurgazy Amina Izbassar Munawar Hayat Researcher Robotics & Artificial Intelligence LLP (RAIL) Nazarbayev University Astana Kazakhstan Department of Electrical and Computer Engineering School of Engg. & Digital Sc. Nazarbayev University Astana Kazakhstan Department of Data Science and AI Monash Data Futures Institute Monash University Melbourne Australia

Lung diseases owing to their high morbidity and mortality pose a significant burden to the healthcare systems. The evolution in integrated circuit technology and the consequent rise in computational power has paved the way for the realization of digital pathology (DP) tools. These DP tools equipped with recent developments in artificial intelligence (AI) can assist pathologists in improving clinical evaluations and thereby prescribing betterinformed treatments. Previously researchers have proposed such DP tools for the treatments of breast, prostate, and head and neck cancers, however, developing such tools and applying them for the treatment of lung diseases remains a challenge. The applications of AI-based DP tools on radiology images for improved characterization of lung nodules, lung cancer risk stratification, and fibrotic lung disease are well established. However, the subtypes of non-small cell lung cancer (NSCLC), namely, adenocarcinomas (AC) and squamous cell carcinoma (SCC) are still difficult to distinguish, especially in small biopsies. In this article, we discuss potential architectures of convolutional neural networks (CNN) for the construction of an appropriate AI-based DP classification tool in establishing a crisp distinction between adenocarcinoma and squamous cell carcinoma. It is further emphasized that the construction of such a DP tool will help in mitigating the current issues that the pathologists are facing while working with the conventional molecular biomarkers profiling, a process which is both, expensive and requires high technical and interpretative skills.

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Hierarchical Multi-Modal Fusion on Dynamic Heterogeneous Graph for Health Insurance Fraud Detection

Hierarchical Multi-Modal Fusion on Dynamic Heterogeneous Gra...

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Zhang, Jian Yang, Fan Lin, Kaibiao Lai, Yongxuan Xiamen University Department of Automation Xiamen China National Institute for Data Science in Health and Medicine Xiamen University Xiamen China Shenzhen Research Institute of Xiamen University Shenzhen China College of Computer and Information Engineering Xiamen University of Technology Xiamen China School of Informatics Xiamen University Xiamen China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

In this paper, we devote to aggregating longitudinal and multi-modal information from heterogeneous neighbors to obtain an accurate node embedding on the dynamic heterogeneous graph. Recently, Heterogeneous Graph Neural Networks (GNNs) have attracted extensive attention in fraud detection. However, when faced with longitudinal and multi-modal data such as health insurance records, existing GNN-based fraud detectors always discard the multi-modal information (e.g., medication and treatment) of heterogeneous neighbors and ignore the inconsistent claimer behavior in the longitudinal records. To fully utilize the information, we represent the records in the form of a dynamic heterogeneous graph, and propose Hierarchical Multi-modal Fusion Graph Neural Network (HMF-GNN) which learns not only topological information, but also embeddings of longitudinal and multi-modal entities to improve the performance of fraud detection. Experimental results on two real-world health insurance datasets demonstrate that HMF-GNN outperforms state-of-the-art graph embedding methods and GNN-based fraud detectors. © 2022 IEEE.

关键词： Health insurance

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Security-as-a-Service with Cyberspace Mimic Defense Technologies in Cloud 7th

Security-as-a-Service with Cyberspace Mimic Defense Technolo...

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7th International Conference of Pioneering computer Scientists, Engineers and Educators, ICPCSEE 2021

作者： Wang, Junchao Pang, Jianmin Wei, Jin State Key Laboratory of Mathematical Engineering and Advanced Computing ZhengZhou China School of Computer Science Fudan University Shanghai China Shanghai Key Laboratory of Data Science Fudan University Shanghai China

ISBN: (纸本)9789811659423

Users usually focus on the application-level requirements which are quite friendly and direct to them. However, there are no existing tools automating the application-level requirements to infrastructure provisioning and application deployment. Although some security issues have been solved during the development phase, the undiscovered vulnerabilities remain hidden threats to the application’s security. Cyberspace mimic defense (CMD) technologies can help to enhance the application’s security despite the existence of the vulnerability. In this paper, the concept of SECurity-as-a-Service (SECaaS) is proposed with CMD technologies in cloud environments. The experiment on it was implemented. It is found that the application’s security is greatly improved to meet the user’s security and performance requirements within budgets through SECaaS. The experimental results show that SECaaS can help the users to focus on application-level requirements (monetary costs, required security level, etc.) and automate the process of application orchestration. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Budget control

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Detecting Backdoors During the Inference Stage Based on Corruption Robustness Consistency

arXiv

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arXiv 2023年

作者： Liu, Xiaogeng Li, Minghui Wang, Haoyu Hu, Shengshan Ye, Dengpan Jin, Hai Wu, Libing Xiao, Chaowei School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Hubei Key Laboratory of Distributed System Security China Hubei Engineering Research Center on Big Data Security China Cluster and Grid Computing Lab China School of Cyber Science and Engineering Wuhan University China Arizona State University United States

Deep neural networks are proven to be vulnerable to backdoor attacks. Detecting the trigger samples during the inference stage, i.e., the test-time trigger sample detection, can prevent the backdoor from being triggered. However, existing detection methods often require the defenders to have high accessibility to victim models, extra clean data, or knowledge about the appearance of backdoor triggers, limiting their practicality. In this paper, we propose the test-time corruption robustness consistency evaluation (TeCo)1, a novel test-time trigger sample detection method that only needs the hard-label outputs of the victim models without any extra information. Our journey begins with the intriguing observation that the backdoor-infected models have similar performance across different image corruptions for the clean images, but perform discrepantly for the trigger samples. Based on this phenomenon, we design TeCo to evaluate test-time robustness consistency by calculating the deviation of severity that leads to predictions' transition across different corruptions. Extensive experiments demonstrate that compared with state-of-the-art defenses, which even require either certain information about the trigger types or accessibility of clean data, TeCo outperforms them on different backdoor attacks, datasets, and model architectures, enjoying a higher AUROC by 10% and 5 times of stability. Copyright © 2023, The Authors. All rights reserved.

关键词： Crime

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HVIS: A Human-like Vision and Inference System for Human Motion Prediction

arXiv

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arXiv 2025年

作者： Lyu, Kedi Chen, Haipeng Liu, Zhenguang Yin, Yifang Lin, Yukang Jiao, Yingying 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 The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China Institute of Blockchain and Data Security Hangzhou China A*STAR Singapore Tsinghua Shenzhen International Graduate School Nanshan District Shenzhen China

Grasping the intricacies of human motion, which involve perceiving spatio-temporal dependence and multi-scale effects, is essential for predicting human motion. While humans inherently possess the requisite skills to navigate this issue, it proves to be markedly more challenging for machines to emulate. To bridge the gap, we propose the Human-like Vision and Inference System (HVIS) for human motion prediction, which is designed to emulate human observation and forecast future movements. HVIS comprises two components: the human-like vision encode (HVE) module and the human-like motion inference (HMI) module. The HVE module mimics and refines the human visual process, incorporating a retina-analog component that captures spatiotemporal information separately to avoid unnecessary crosstalk. Additionally, a visual cortex-analogy component is designed to hierarchically extract and treat complex motion features, focusing on both global and local features of human poses. The HMI is employed to simulate the multi-stage learning model of the human brain. The spontaneous learning network simulates the neuronal fracture generation process for the adversarial generation of future motions. Subsequently, the deliberate learning network is optimized for hard-to-train joints to prevent misleading learning. Experimental results demonstrate that our method achieves new state-of-the-art performance, significantly outperforming existing methods by 19.8% on Human3.6M, 15.7% on CMU Mocap, and 11.1% on G3D. © 2025, CC BY.

关键词： Motion estimation

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