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MOCBOA:Multi-Objective Chef-Based Optimization Algorithm Using Hybrid Dominance Relations for Solving Engineering Design Problems

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computer Modeling in Engineering & sciences 2025年第4期143卷 967-1008页

作者： Nour Elhouda Chalabi Abdelouahab Attia Abdulaziz S.Almazyad Ali Wagdy Mohamed Frank Werner Pradeep Jangir Mohammad Shokouhifar Computer Science Department University of M’silaIchebiliaM’sila28000Algeria Computer ScienceDepartment Mohamed El Bachir El IbrahimiUniversity of Bordj BouArreridjBordj BouArreridj34000Algeria Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11931Jordan Faculty of Mathematics Otto-von-Guericke UniversityMagdeburg39016Germany University Centre for Research and Development Chandigarh UniversityMohali140413India Department of Electrical and Computer Engineering Shahid Beheshti UniversityTehran14399Iran DTU AI and Data Science Hub(DAIDASH) Duy Tan UniversityDa Nang550000Vietnam

Multi-objective optimization is critical for problem-solving in engineering,economics,and *** study introduces the Multi-Objective Chef-Based Optimization Algorithm(MOCBOA),an upgraded version of the Chef-Based Optimization Algorithm(CBOA)that addresses distinct *** approach is unique in systematically examining four dominance relations—Pareto,Epsilon,Cone-epsilon,and Strengthened dominance—to evaluate their influence on sustaining solution variety and driving convergence toward the Pareto *** comparison investigation,which was conducted on fifty test problems from the CEC 2021 benchmark and applied to areas such as chemical engineering,mechanical design,and power systems,reveals that the dominance approach used has a considerable impact on the key optimization measures such as the hypervolume *** paper provides a solid foundation for determining themost effective dominance approach and significant insights for both theoretical research and practical applications in multi-objective optimization.

关键词： Multi-objective optimization chef-based optimization algorithm(CBOA) pareto dominance epsilon dominance cone-epsilon dominance strengthened dominance

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Rule-Based Dialect of Tulang Bawang Stemmer

Rule-Based Dialect of Tulang Bawang Stemmer

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2025 International Conference on Advancement in Data science, E-learning and Information System, ICADEIS 2025

作者： Abidin, Zaenal Junaidi, Akmal Wamiliana Lumbanraja, Favorisen Rosyking Kurniasari, Dian Borman, Rohmat Indra Doctoral Program of Mathematics and Natural Sciences Universitas Lampung Bandar Lampung Indonesia Universitas Lampung Department of Computer Science Bandar Lampung Indonesia Universitas Lampung Department of Mathematics Bandar Lampung Indonesia Universitas Teknokrat Indonesia Faculty of Engineering and Computer Science Bandar Lampung Indonesia

ISBN: (纸本)9798331513320

Stemming, an essential procedure in natural language processing (NLP), diminishes words to their base forms, facilitating tasks such as information retrieval and sentiment analysis. Although stemming techniques for highresource languages are well-developed, numerous low-resource languages, including dialect of Tulang Bawang, suffer from inadequate solutions owing to a scarcity of linguistic data and resources. Existing systems, including rule-based stemmers, have demonstrated efficacy in processing low-resource languages such as Indonesian and Javanese by utilizing established morphological rules. Nonetheless, these methods encounter considerable obstacles, such as restricted adaptability, inability to accommodate unusual root structures, and excessive dependence on fixed rules that might result in over- or understemming. Rule-based methodologies frequently misidentify roots when faced with intricate affixes or unconventional word forms. We introduce an improved rule-based Tulang Bawang Stemmer aimed at overcoming these constraints by enhancing current linguistic rules and integrating new patterns specific to the language's morphology. Assessed on 500 test samples and 200 independent test samples, our improved stemmer attained gold standard evaluation metrics of 96.2% and 94%, respectively, surpassing prior implementations in both precision and generalization. The findings demonstrate the potential of enhanced rule-based techniques to improving NLP for lowresource languages. Improved stemming performance enables better downstream applications, promotes more efficient text analysis, and advances research in underrepresented languages. © 2025 IEEE.

关键词： Linguistics

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Epileptic seizure detection in EEG signals via an enhanced hybrid CNN with an integrated attention mechanism

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Mathematical Biosciences and Engineering 2025年第1期22卷 73-105页

作者： Mekruksavanich, Sakorn Phaphan, Wikanda Jitpattanakul, Anuchit Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao56000 Thailand Department of Applied Statistics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok10800 Thailand Department of Mathematics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok10800 Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Science and Technology Research Institute King Mongkut’s University of Technology North Bangkok Bangkok10800 Thailand

Epileptic seizures, a prevalent neurological condition, necessitate precise and prompt identification for optimal care. Nevertheless, the intricate characteristics of electroencephalography (EEG) signals, noise, and the want for real-time analysis require enhancement in the creation of dependable detection approaches. Despite advances in machine learning and deep learning, capturing the intricate spatial and temporal patterns in EEG data remains challenging. This study introduced a novel deep learning framework combining a convolutional neural network (CNN), bidirectional gated recurrent unit (BiGRU), and convolutional block attention module (CBAM). The CNN extracts spatial features, the BiGRU captures long-term temporal dependencies, and the CBAM emphasizes critical spatial and temporal regions, creating a hybrid architecture optimized for EEG pattern recognition. Evaluation of a public EEG dataset revealed superior performance compared to existing methods. The model achieved 99.00% accuracy in binary classification, 96.20% in three-class tasks, 92.00% in four-class scenarios, and 89.00% in five-class classification. High sensitivity (89.00–99.00%) and specificity (89.63–99.00%) across all tasks highlighted the model’s robust ability to identify diverse EEG patterns. This approach supports healthcare professionals in diagnosing epileptic seizures accurately and promptly, improving patient outcomes and quality of life. © 2025 American Institute of Mathematical sciences. All rights reserved.

关键词： Electroencephalography

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From Infants to AI: Incorporating Infant-like Learning in Models Boosts Efficiency and Generalization in Learning Social Prediction Tasks

arXiv

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arXiv 2025年

作者： Treger, Shify Ullman, Shimon Department of Computer Science and Applied Mathematics Weizmann Institute of Science

Early in development, infants learn a range of useful concepts, which can be challenging from a computational standpoint. This early learning comes together with an initial understanding of aspects of the meaning of concepts, e.g., their implications, causality, and using them to predict likely future events. All this is accomplished in many cases with little or no supervision, and from relatively few examples, compared with current network models. In learning about objects and human-object interactions, early acquired and possibly innate concepts are often used in the process of learning additional, more complex concepts. In the current work, we model how early-acquired concepts are used in the learning of subsequent concepts, and compare the results with standard deep network modeling. We focused in particular on the use of the concepts of animacy and goal attribution in learning to predict future events. We show that the use of early concepts in the learning of new concepts leads to better learning (higher accuracy) and more efficient learning (requiring less data). We further show that this integration of early and new concepts shapes the representation of the concepts acquired by the model. The results show that when the concepts were learned in a human-like manner, the emerging representation was more useful, as measured in terms of generalization to novel data and tasks. On a more general level, the results suggest that there are likely to be basic differences in the conceptual structures acquired by current network models compared to human learning. © 2025, CC BY.

关键词： Contrastive Learning

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FreqRISE: Explaining time series using frequency masking 6

FreqRISE: Explaining time series using frequency masking

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6th Northern Lights Deep Learning Conference, NLDL 2025

作者： Brüsch, Thea Wickstrøm, Kristoffer Knutsen Schmidt, Mikkel N. Alstrøm, Tommy Sonne Jenssen, Robert Department of Applied Mathematics and Computer Science Technical University of Denmark Denmark Department of Physics and Technology UiT The Arctic University of Norway Norway Norwegian Computing Center Oslo Norway

Time series data is fundamentally important for many critical domains such as healthcare, finance, and climate, where explainable models are necessary for safe automated decision-making. To develop explainable artificial intelligence in these domains therefore implies explaining salient information in the time series. Current methods for obtaining saliency maps assumes localized information in the raw input space. In this paper, we argue that the salient information of a number of time series is more likely to be localized in the frequency domain. We propose FreqRISE, which uses masking-based methods to produce explanations in the frequency and time-frequency domain, and outperforms strong baselines across a number of tasks. The source code is available here: https://***/theabrusch/FreqRISE. © NLDL *** rights reserved.

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Multi-Level Feature Distillation of Joint Teachers Trained on Distinct Image Datasets

Multi-Level Feature Distillation of Joint Teachers Trained o...

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2025 IEEE/CVF Winter Conference on Applications of computer Vision, WACV 2025

作者： Iordache, Adrian Alexe, Bogdan Ionescu, Radu Tudor University of Bucharest Department of Computer Science Bucharest Romania 'Gheorghe Mihoc - Caius Iacob' Institute of Mathematical Statistics and Applied Mathematics The Romanian Academy Bucharest Romania

ISBN: (纸本)9798331510831

We propose a novel teacher-student framework to distill knowledge from multiple teachers trained on distinct datasets. Each teacher is first trained from scratch on its own dataset. Then, the teachers are combined into a joint architecture, which fuses the features of all teachers at multiple representation levels. The joint teacher architecture is fine-tuned on samples from all datasets, thus gathering useful generic information from all data samples. Finally, we employ a multi-level feature distillation procedure to transfer the knowledge to a student model for each of the considered datasets. We conduct image classification experiments on seven benchmarks, and action recognition experiments on three benchmarks. To illustrate the power of our feature distillation procedure, the student architectures are chosen to be identical to those of the individual teachers. To demonstrate the flexibility of our approach, we combine teachers with distinct architectures. We show that our novel Multi-Level Feature Distillation (MLFD) can significantly surpass equivalent architectures that are either trained on individual datasets, or jointly trained on all datasets at once. Furthermore, we confirm that each step of the proposed training procedure is well motivated by a comprehensive ablation study. We publicly release our code at https://***/AdrianIordache/MLFD. © 2025 IEEE.

关键词： Teaching

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Thermal-Aware Communication

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IEEE Transactions on Information Theory 2025年第6期71卷 4145-4155页

作者： Chee, Yeow Meng Etzion, Tuvi Immink, Kees A. Schouhamer Nguyen, Tuan Thanh Vu, Van Khu Weber, Jos H. Yaakobi, Eitan Singapore University of Technology and Design Singapore Technion — Israel Institute of Technology Department of Computer Science Haifa3200003 Israel National University of Singapore Department of Industrial Systems Engineering and Management Singapore Turing Machines Inc Rotterdam3016 DK Netherlands Singapore University of Technology and Design Science Mathematics and Technology Cluster 487372 Singapore Delft University of Technology Department of Applied Mathematics Delft2628 CD Netherlands

Temperature control is of utmost importance in transmission systems. In this paper, a binary channel model is considered in which the transmission of a one causes a temperature increase while communicating a zero causes a temperature drop. By putting constraints on the input sequences, it is guaranteed that the channel temperature will not exceed a certain pre-determined maximum. In the asymptotic regime, the capacity of such a channel is studied. For the non-asymptotic regime, fixed-length codes are presented, with the property that codewords can be freely cascaded without violating the temperature constraint. Optimization of the code size is investigated and codewords are enumerated using generating functions. © 1963-2012 IEEE.

关键词： Channel capacity

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THE INCLUSION-EXCLUSION PRINCIPLE AND RECURRENCES FOR PARTITION NUMBERS

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UPB Scientific Bulletin, Series A: applied mathematics and Physics 2025年第1期87卷 97-106页

作者： Ballantine, Cristina Merca, Mircea Department of Mathematics and Computer Science College of The Holy Cross WorcesterMA01610 United States Department of Mathematical Methods and Models Fundamental Sciences Applied in Engineering Research Center National University of Science and Technology Politehnica Bucharest BucharestRO-060042 Romania Academy of Romanian Scientists BucharestRO-050044 Romania

The inclusion-exclusion principle together with Legendre type theorems for number of distinct restricted partitions weighted by the parity of their length are used to give several recurrence relations for restricted partition numbers as well as for overpartition numbers. In the case of overpartitions, POD partitions and 3-color partitions, we give combinatorial proofs for the Legendre type theorems and arrive to combinatorial proofs for the recurrence relations. © 2025, Politechnica University of Bucharest. All rights reserved.

关键词： Combinatorial mathematics

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Latent Representation Learning for Multimodal Brain Activity Translation

Latent Representation Learning for Multimodal Brain Activity...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Afrasiyabi, Arman Bhaskar, Dhananjay Busch, Erica L. Caplette, Laurent Singh, Rahul Lajoie, Guillaume Turk-Browne, Nicholas B. Krishnaswamy, Smita Department of Computer Science Department of Applied Mathematics Department of Psychology Department of Genetics Department of Mathematics and Statistics Mila - Quebec AI Institute Canada Université de Montréal Canada Wu Tsai Institute United States Yale University United States

ISBN: (纸本)9798350368741

Neuroscience employs diverse neuroimaging techniques, each offering distinct insights into brain activity, from electrophysiological recordings such as EEG, which have high temporal resolution, to hemodynamic modalities such as fMRI, which have increased spatial precision. However, integrating these heterogeneous data sources remains a challenge, which limits a comprehensive understanding of brain function. We present the Spatiotemporal Alignment of Multimodal Brain Activity (SAMBA) framework, which bridges the spatial and temporal resolution gaps across modalities by learning a unified latent space free of modality-specific biases. SAMBA introduces a novel attention-based wavelet decomposition for spectral filtering of electrophysiological recordings, graph attention networks to model functional connectivity between functional brain units, and recurrent layers to capture temporal autocorrelations in brain signal. We show that the training of SAMBA, aside from achieving translation, also learns a rich representation of brain information processing. We showcase this classify external stimuli driving brain activity from the representation learned in hidden layers of SAMBA, paving the way for broad downstream applications in neuroscience research and clinical contexts. © 2025 IEEE.

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Sampling List Packings 16

Sampling List Packings

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16th Innovations in Theoretical computer science Conference, ITCS 2025

作者： Camrud, Evan Davies, Ewan Karduna, Alex Lee, Holden Department of Mathematics and Statistics Middlebury College VT United States Department of Computer Science Colorado State University Fort CollinsCO United States Department of Applied Mathematics and Statistics Johns Hopkins University BaltimoreMD United States

ISBN: (纸本)9783959773614

We initiate the study of approximately counting the number of list packings of a graph. The analogous problem for usual vertex coloring and list coloring has attracted substantial attention. For list packing the setup is similar, but we seek a full decomposition of the lists of colors into pairwise-disjoint proper list colorings. The existence of a list packing implies the existence of a list coloring, but the converse is false. Recent works on list packing have focused on existence or extremal results of on the number of list packings, but here we turn to the algorithmic aspects of counting and sampling. In graphs of maximum degree ∆ and when the number of colors is at least Ω(∆2), we give a fully polynomial-time randomized approximation scheme (FPRAS) based on rapid mixing of a natural Markov chain (the Glauber dynamics) which we analyze with the path coupling technique. Some motivation for our work is the investigation of an atypical spin system, one where the number of spins for each vertex is much larger than the graph degree. © Evan Camrud, Ewan Davies, Alex Karduna, and Holden Lee.

关键词： Markov chains

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