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Dynamic Cascaded Flow-Shop Scheduling Using an Evolutionary Greedy Algorithm

IEEE Transactions on Evolutionary Computation 2025年

作者： Li, Qiu-Ying Pan, Quan-Ke Wang, Ling Gao, Liang Li, Wei-Min Shanghai University School of Mechatronic Engineering and Automation Shanghai200072 China Tsinghua University Department of Automation Beijing100084 China Huazhong University of Science and Technology State Key Lab of Intelligent Manufacturing Equipment and Technology Wuhan430074 China Shanghai University School of Computer Engineering and Science Shanghai200000 China

Production processes are inherently complex, often involving multiple production phases from raw materials to finished products, making joint scheduling problems a focal point of research. This paper addresses the dynamic cascaded flowshop joint scheduling problem, which integrates a distributed permutation flowshop in Phase 1 and a hybrid flowshop in Phase 2. The challenge involves both initial scheduling and dynamic response mechanisms for new job insertions. We propose an evolutionary greedy algorithm (EGA) aimed at minimizing total flowtime. The EGA employs a multi-start cooperative framework tailored to problem characteristics, alternating between a population-based evolutionary greedy algorithm for Phase 1 and an elitist-based greedy algorithm for Phase 2 to generate a robust and complete schedule. Upon new job insertions, three heuristic-driven response strategies enhance solution stability and adaptability. Additionally, phase-specific hybrid local search operators and an adaptive insertion strategy, leveraging knowledge-based problem properties, further improve solution quality and search efficiency. The experimental results indicate that the EGA outperforms five state-of-the-art algorithms, achieving an average improvement of 39% in RPI values across 480 instances. Moreover, the proposed local search mechanisms and dynamic response strategies significantly enhance its performance. Thus, the EGA is well-suited for addressing the studied problem. © 1997-2012 IEEE.

关键词： Job shop scheduling

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Sparse-view CT reconstruction based on total variation with alternating gradient constraint edge-preserving algorithm

引用

Inverse Problems 2025年第5期41卷

作者： Li, Yihong Zhang, Yingfang Li, Yu Zhao, Xiaojie Chen, Ping Department of Mathematics North University of China Taiyuan030051 China Department of Information and Communication Engineering North University of China Taiyuan030051 China The State Key Lab for Electronic Testing Technology North University of China Taiyuan030051 China Department of computer science and technology North University of China Taiyuan030051 China

Sparse-view computed tomography (CT) offers a wide range of options for medical diagnosis. However, conventional CT reconstruction algorithms cannot meet the requirements for medical diagnosis in the presence of incomplete scan data. According to the compressed sensing theory, this problem can be solved by the introduction of regular terms. Using total variation (TV) regularization to reconstruct the CT image can effectively improve the quality of the image reconstruction to some extent, but excessive smoothing occurs at the edges of the image. Therefore, in this study, the problems of noise reduction and edge preservation in sparse-view reconstruction based on TV were investigated. The alternating edge-preserving diffusion and smoothing (AEDS) algorithm achieves the performance of edge detail preservation using high sparsity l0-norm. Then, we introduce a gradient prior term in the AEDS algorithm to fully utilize the information before and after iteration to better preserve the edge information of the image, named alternating gradient constraint edge-preserving. Based on the above approach, we propose a double-constrained sparse-view CT reconstruction algorithm with coupled denoising and edge preservation performances. And we verify the convergence of the model. Compared to other algorithms, the experimental results show that the proposed algorithm preserves more image details while suppressing noise and improving the quality of the reconstructed image. The algorithm cleverly combines l0-norm and l1-norm, to solve the problem of using only one constraint in sparse-view CT reconstruction, which does not balance denoising and edge protection well. © 2025 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.

关键词： Photointerpretation

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Diverse Image Generation with Diffusion Models and Cross Class label Learning for Polyp Classification

arXiv

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arXiv 2025年

作者： Sharma, Vanshali Jha, Debesh Bhuyan, M.K. Das, Pradip K. Bagci, Ulas Department of Computer Science & Engineering Indian Institute of Technology Guwahati Guwahati781039 India Department of Electronics & Electrical Engineering Indian Institute of Technology Guwahati Guwahati781039 India Machine & Hybrid Intelligence Lab Department of Radiology Northwestern University Chicago60611 United States

Pathologic diagnosis is a critical phase in deciding the optimal treatment procedure for dealing with colorectal cancer (CRC). Colonic polyps, precursors to CRC, can pathologically be classified into two major types: adenomatous (malignant potential) and hyperplastic (benign). For precise classification and early diagnosis of such polyps, the medical procedure of colonoscopy has been widely adopted paired with various imaging techniques, including narrow band imaging (NBI) and white light imaging (WLI). These imaging modalities have different advantages in capturing polyp-specific features for accurate classification. However, the existing classification techniques mainly rely on a single imaging modality and show limited performance due to data scarcity. Recently, generative artificial intelligence has been gaining prominence in overcoming such issues. Additionally, various generation-controlling mechanisms using text prompts and images have been introduced to obtain visually appealing and desired outcomes in a better-controlled manner. However, such mechanisms require class labels to make the model respond efficiently to the provided control input. In the colonoscopy domain, such controlling mechanisms are rarely explored;specifically, the text prompt is a completely uninvestigated area. Moreover, the unavailability of expensive class-wise labels for diverse sets of images limits such explorations. Therefore, in this work, we develop a novel model, PathoPolyp-Diff, that generates text-controlled synthetic images with diverse characteristics in terms of pathology, imaging modalities, and quality. In the process, we introduce cross-class label learning to make the model learn features from other classes, reducing the burdensome task of data annotation. We validate the effectiveness of text-controlled synthesis and cross-class label learning by performing polyp classification (adenomatous/hyperplastic) with different imaging modalities (NBI/WLI) and text prompts. The

关键词： Image annotation

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Whistle: Data-Efficient Multilingual and Crosslingual Speech Recognition via Weakly Phonetic Supervision

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IEEE Transactions on Audio, Speech and Language Processing 2025年 33卷 1440-1453页

作者： Saierdaer Yusuyin Te Ma Hao Huang Wenbo Zhao Zhijian Ou School of Computer Science and Technology Xinjiang University Urumqi China China Unicom (Guangdong) Industrial Internet Company Ltd Guangzhou China Speech Processing and Machine Intelligence (SPMI) Lab Department of Electronic Engineering Tsinghua University Beijing China

There exist three approaches for multilingual and crosslingual automatic speech recognition (MCL-ASR) - supervised pretraining with phonetic or graphemic transcription, and self-supervised pretraining. We find that pretraining with phonetic supervision has been underappreciated so far for MCL-ASR, while conceptually it is more advantageous for information sharing between different languages. This paper explores the approach of pretraining with weakly phonetic supervision towards data-efficient MCL-ASR, which is called Whistle. We relax the requirement of gold-standard human-validated phonetic transcripts, and obtain International Phonetic Alphabet (IPA) based transcription by leveraging the LanguageNet grapheme-to-phoneme (G2P) models. We construct a common experimental setup based on the CommonVoice dataset, called CV-Lang10, with 10 seen languages and 2 unseen languages. A set of experiments are conducted on CV-Lang10 to compare, as fair as possible, the three approaches under the common setup for MCL-ASR. Experiments demonstrate the advantages of phoneme-based models (Whistle) for MCL-ASR, in terms of speech recognition for seen languages, crosslingual performance for unseen languages with different amounts of few-shot data, overcoming catastrophic forgetting, and training efficiency. It is found that when training data is more limited, phoneme supervision can achieve better results compared to subword supervision and self-supervision, thereby providing higher data-efficiency.

关键词： Phonetics Multilingual Speech recognition Training Hidden Markov models Data models Symbols Error analysis Pipelines Information sharing

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Data-Filtered Prediction with Decomposition and Amplitude-Aware Permutation Entropy for Workload and Resource Utilization in Cloud Data Centers

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IEEE Internet of Things Journal 2025年

作者： Yuan, Haitao Hu, Qinglong Wang, Meijia Wang, Shen Bi, Jing Buyya, Rajkumar Shi, Shuyuan Yang, Jinhong Zhang, Jia Zhou, MengChu Beihang University School of Automation Science and Electrical Engineering Beijing100191 China City University of Hong Kong Department of Computer Science Hong Kong999077 Hong Kong Beijing University of Technology School of Software Engineering The Faculty of Information Technology Beijing100124 China Lab School of Computing and Information Systems MelbourneVIC3010 Australia Beihang University School of Integrated Circuit Science and Engineering Beijing China CSSC Systems Engineering Research Institute Beijing China Southern Methodist University Department of Computer Science The Lyle School of Engineering DallasTX75205 United States New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States

In recent years, cloud computing has witnessed widespread applications across numerous organizations. Predicting workload and computing resource data can facilitate proactive service operation management, leading to substantial improvements in quality of service and cost efficiency. However, these data often exhibit non-linearity, high volatility, and interdependencies across different categories, presenting challenges for accurate forecasting. Consequently, there is a critical need to develop a method that thoroughly and comprehensively analyzes all available data to forecast future trends effectively. This work proposes a novel integrated data-enhanced prediction model named SVAPI for achieving high-accuracy workload prediction in cloud computing systems. SVAPI employs the Savitzky-Golay filter, Variational mode decomposition, and the mode selection based on Amplitude-aware Permutation entropy for feature processing, whose features are subsequently utilized by Informer for multivariate joint analysis of the enhanced data, achieving high-precision prediction. Ablation and comparative experiments with advanced prediction models are conducted on the Google cluster trace and other typical datasets. Realistic data-driven results indicate that SVAPI improves the prediction accuracy by 37.7% compared to the original Informer, with each module contributing to the performance enhancement. Furthermore, compared with Autoformer, SVAPI enhances the prediction accuracy of workload, CPU, and memory by 65.6%, 66.9%, and 70.8%, respectively, demonstrating that SVAPI owns strong abilities in noise filtering, feature processing, and multivariate joint analysis for achieving higher prediction accuracy. © 2014 IEEE.

关键词： Quality of service

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LargeAD: Large-Scale Cross-Sensor Data Pretraining for Autonomous Driving

arXiv

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arXiv 2025年

作者： Kong, Lingdong Xu, Xiang Liu, Youquan Cen, Jun Chen, Runnan Zhang, Wenwei Pan, Liang Chen, Kai Liu, Ziwei The School of Computing Department of Computer Science National University of Singapore CNRS@CREATE Singapore The College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Hochschule Bremerhaven Germany Shanghai AI Laboratory Shanghai China The Hong Kong University of Science and Technology Hong Kong The University of Hong Kong Hong Kong S-Lab Nanyang Technological University Singapore

Recent advancements in vision foundation models (VFMs) have revolutionized visual perception in 2D, yet their potential for 3D scene understanding, particularly in autonomous driving applications, remains underexplored. In this paper, we introduce LargeAD, a versatile and scalable framework designed for large-scale 3D pretraining across diverse real-world driving datasets. Our framework leverages VFMs to extract semantically rich superpixels from 2D images, which are aligned with LiDAR point clouds to generate high-quality contrastive samples. This alignment facilitates cross-modal representation learning, enhancing the semantic consistency between 2D and 3D data. We introduce several key innovations: i) VFM-driven superpixel generation for detailed semantic representation, ii) a VFM-assisted contrastive learning strategy to align multimodal features, iii) superpoint temporal consistency to maintain stable representations across time, and iv) multi-source data pretraining to generalize across various LiDAR configurations. Our approach delivers significant performance improvements over state-of-the-art methods in both linear probing and fine-tuning tasks for both LiDAR-based segmentation and object detection. Extensive experiments on eleven large-scale multi-modal datasets highlight our superior performance, demonstrating the adaptability, efficiency, and robustness in real-world autonomous driving scenarios. The project page can be accessible at: https://***/LargeAD. © 2025, CC BY-SA.

关键词： Superpixels

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Resource-Aware Monkeypox Diagnosis: Leveraging High-Capacity and Lightweight Models with Knowledge Distillation

Resource-Aware Monkeypox Diagnosis: Leveraging High-Capacity...

引用

International Communication Systems and Networks and Workshops, COMSNETS

作者： Avi Deb Raha Mrityunjoy Gain Sudip Kumar Saha Md Shohanur Rahman Apurba Adhikary Rameswar Debnath Anupam Kumar Bairagi Sujit Biswas Computer Science and Engineering Discipline Khulna University Khulna Bangladesh AI Innovation Lab Khulna Bangladesh United International University Dhaka Bangladesh Dept. of Mechatronics Engineering KUET Khulna Bangladesh Dept. of ICE Noakhali Science and Technology University Noakhali Bangladesh Computer Science Department City University of London UK

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

Monkeypox, a novel zoonotic disease akin to smallpox, necessitates prompt and accurate diagnosis for effective treatment. Conventional diagnostic techniques, such as Polymerase Chain Reaction (PCR), offer high precision but require specialized equipment and trained personnel, rendering them impractical in resource-limited settings. Existing deep learning approaches for monkeypox diagnosis have predominantly relied on single, resource-intensive models, prioritizing accuracy over deployment feasibility across diverse computing platforms. In this study, we present a resource-conscious model deployment strategy that balances diagnostic accuracy with computational efficiency, enabling precise diagnosis in both resource-rich environments, such as hospitals, and resource-constrained contexts. We utilize a pretrained ConvNeXt-B model, trained on the extensive ImageNet-22K dataset, for deployment in resource-abundant scenarios, and a SqueezeNet model, optimized for resourcelimited devices using the ImageNet-1K dataset. To enhance the performance of the lightweight SqueezeNet model without increasing computational complexity, we apply Knowledge Distillation. Experimental results demonstrate that the ConvNeXt-B model achieves an accuracy of 95.75%, which is 3.47% higher than the previous studies. Similarly, the Knowledge Distillation-enhanced SqueezeNet model attains an accuracy of 91.89%, representing a 2.32% improvement over the baseline. This dualmodel approach ensures that accurate monkeypox diagnostics are accessible across a wide range of computational environments, thereby supporting more effective outbreak management and contributing to improved public health outcomes.

关键词： Accuracy Hospitals Computational modeling Telemedicine Rendering (computer graphics) Skin Security Polymers Public healthcare Diseases

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Focal volume optics for composite structuring in transparent solids

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International Journal of Extreme Manufacturing 2025年第1期7卷 357-366页

作者： Bo Zhang Zhuo Wang Dezhi Tan Min Gu Yuanzheng Yue Jianrong Qiu State Key Laboratory of Modern Optical Instrumentation College of Optical Science and EngineeringZhejiang UniversityHangzhou 310027People’s Republic of China Zhejiang Lab Hangzhou 311100People’s Republic of China School of Materials Science and Engineering Zhejiang UniversityHangzhou 310027People’s Republic of China Institute of Photonic Chips University of Shanghai for Science and TechnologyShanghaiPeople’s Republic of China Centre for Artificial-Intelligence Nanophotonics School of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and TechnologyShanghaiPeople’s Republic of China Department of Chemistry and Bioscience Aalborg University9220 AalborgDenmark

Achieving high-level integration of composite micro-nano structures with different structural characteristics through a minimalist and universal process has long been the goal pursued by advanced manufacturing research but is rarely explored due to the absence of instructive ***,we revealed a controllable ultrafast laser-induced focal volume light field and experimentally succeeded in highly efficient one-step composite structuring in multiple transparent solids.A pair of spatially coupled twin periodic structures reflecting light distribution in the focal volume are simultaneously created and independently tuned by engineering ultrafast laser-matter *** demonstrated that the generated composite micro-nano structures are applicable to multi-dimensional information integration,nonlinear diffractive elements,and multi-functional optical *** work presents the experimental verification of highly universal all-optical fabrication of composite micro-nano structures with independent controllability in multiple degrees of freedom,expands the current cognition of ultrafast laser-based material modification in transparent solids,and establishes a new scientific aspect of strong-field optics,namely,focal volume optics for composite structuring transparent solids.

关键词： ultrafast laser focal volume light field composite structuring transparent solids advanced manufacturing

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Emotion Recognition by Learning the Manifold of Fused Multiscale Information of EEG Signals

引用

IEEE Transactions on Affective Computing 2025年

作者： Li, Cunbo Zhang, Shuhan Mu, Yufeng Yang, Lei Peng, Yueheng Li, Fali Zhang, Yangsong Liang, Zhen Cao, Zehong Wan, Feng Yao, Dezhong Li, Peiyang Xu, Peng University of Electronic Science and Technology of China Clinical Hospital of Chengdu Brain Science Institute MOE Key Lab for Neuroinformation School of Life Science and Technology Chengdu610054 China University of Macau Centre for Cognitive and Brain Sciences Institute of Collaborative Innovation China Southwest University of Science and Technology School of Computer Science and Technology Mianyang621010 China Shenzhen University School of Biomedical Engineering Medical School Shenzhen518060 China Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging Shenzhen518060 China University of South Australia STEM Mawson Lakes Campus AdelaideSA5095 Australia School of Computer Science University of Technology Sydney Australia University of Macau Department of Electrical and Computer Engineering Faculty of Science and Technology Centre for Cognitive and Brain Sciences Institute of Collaborative Innovation China Chongqing University of Posts and Telecommunications School of Bioinfomatics Chongqing400065 China

Recent research has consistently indicated that the fusion of electroencephalography (EEG) features from multiple modalities can integrate cognitive state expressions across diverse dimensions, resulting in a substantial increase in emotion recognition accuracy. However, redundant information within the fused multimodal features could lead to the curse of dimensionality and overfitting of the learning model. In this work, we propose a multiscale EEG feature fusion and representation strategy for EEG emotion recognition named manifold of multiscale information fusion (MMIF), in which the optimal manifold of the multiscale fusion of local and global brain activation patterns can be automatically learned to realize an efficient representation of emotional EEG signals. To evaluate the performance, in this work, both off- and online EEG emotion recognition experiments were conducted, and the experimental results consistently verified the effectiveness and feasibility of the MMIF applied in real-time emotion decoding systems. Furthermore, the analytical experiments confirmed the discriminative capabilities and cognitive interpretability of the MMIF. In summary, the proposed MMIF model may provide an efficient avenue for exploring representations and enhancing the discrimination of multimodal fusion features, which may also provide a promising solution for designing online affective braincomputer interaction systems. © 2010-2012 IEEE.

关键词： Information fusion

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FarsEval-PKBETS: A new diverse benchmark for evaluating Persian large language models

arXiv

引用

arXiv 2025年

作者： Shamsfard, Mehrnoush Saaberi, Zahra Manesh, Mostafa Karimi Hashemi, Seyed Mohammad Hossein Vatankhah, Zahra Ramezani, Motahareh Pourazin, Niki Zare, Tara Azimi, Maryam Chitsaz, Sarina Khoraminejad, Sama Mortazavi, Morteza Mahdavi Chizari, Mohammad Mahdi Maleki, Sahar Majd, Seyed Soroush Masumi, Mostafa Khoeini, Sayed Ali Musavi Mohseni, Amir Alipour, Sogol NLP Lab Faculty of Computer Science and Engineering Shahid Beheshti University Tehran Iran Faculty of Computer Engineering Amirkabir University of Technology Tehran Iran Faculty of Law Qom University Qom Iran Faculty of Medicine Tehran Medical Sciences Islamic Azad University Tehran Iran Computer Engineering Department Sharif University of Technology Tehran Iran

Research on evaluating and analyzing large language models (LLMs) has been extensive for high-resource languages such as English, yet their performance in languages such as Persian has received considerably less attention. This paper introduces FarsEval-PKBETS benchmark, a subset of FarsEval project for evaluating large language models in Persian. This benchmark consists of 4,000 questions and answers in various formats, including multiple-choice, short-answer, and descriptive responses. It covers a wide range of domains and tasks, including medicine, law, religion, Persian language, encyclopedic knowledge, human preferences, social knowledge, ethics and bias, text generation, and respecting others’ rights. This benchmark incorporates linguistic, cultural, and local considerations relevant to the Persian language and Iran. To ensure the questions are challenging for current LLMs, three models—Llama3-70B, PersianMind, and Dorna—were evaluated using this benchmark. Their average accuracy was below 50%, meaning they provided fully correct answers to fewer than half of the questions. These results indicate that current language models are still far from being able to solve this *** Codes 68T50 © 2025, CC0.

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