Article
Computer Science, Information Systems
Jianping Dong, Gexiang Zhang, Biao Luo, Haina Rong
Summary: An extended numerical spiking neural (ENSN P) system is proposed to solve continuous constrained optimization problems. In ENSN P systems, production functions are selected by probability to achieve updated parameters. Experimental results show that the proposed method outperforms or is competitive with other 28 optimization algorithms in five benchmarks.
INFORMATION SCIENCES
(2023)
Article
Thermodynamics
Wenzhuo Wang, Qing Ai, Yong Shuai, Heping Tan
Summary: A topology optimization method is proposed for the design of a thermal cloak. The initial topology of the cloak is pre-defined using thermal scattering theory to reduce design variables. The method uses bilayer concentric rings with insulating and high thermal conductivity materials to mask heat flow and correct thermal properties. The cloak's optimal configuration and deflection angle of thermal flux at the surface boundary are investigated, and numerical simulations show excellent cloaking performance. The pre-defined bilayer structure is easy to manufacture and provides a general and efficient means for designing thermally functional materials.
INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER
(2023)
Article
Computer Science, Information Systems
Jianping Dong, Gexiang Zhang, Biao Luo, Qiang Yang, Dequan Guo, Haina Rong, Ming Zhu, Kang Zhou
Summary: This paper proposes a distributed adaptive optimization spiking neural P system (DAOSNPS) that can solve combinatorial optimization problems without the help of evolutionary algorithms or swarm intelligence algorithms. Extensive experiments demonstrate its superiority over other methods.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Shanchen Pang, Tong Ding, XiaoBing Mao, Neal N. Xiong
Summary: This study presents a new model of P system called conditional enzymatic numerical P system (DENPS), which introduces a series of decisional enzymes and rebuilds the cell structures to achieve a more flexible decision-making mechanism. The validation experiments demonstrate that DENPS is logical and efficient in processing large-scale decision tasks, with prior results being achieved 188.28 times faster and decision tree based on DENPS being 119.85 times faster than the general serial framework.
INFORMATION SCIENCES
(2021)
Article
Mathematics
David Orellana-Martin, Antonio Ramirez-de-Arellano, Jose Antonio Andreu-Guzman, Alvaro Romero-Jimenez, Mario J. Perez-Jimenez
Summary: This paper discusses the class R of recognizer membrane systems that can provide polynomial-time and uniform solutions for NP-complete problems, defining it as an efficient class. By representing R as a class of efficient recognizer cell-like P systems with object evolution rules, communication rules, and dissolution rules, the polynomial-time complexity class PMCR is obtained, encompassing both the NP and co-NP classes. The DP class, which includes languages that can be expressed as the difference between any two languages in NP, is considered as a more complex class than the NP class and serves as promising candidates for studying the P vs NP problem. This paper extends previous results to include any class R of efficient recognizer tissue-like membrane systems and presents a detailed protocol for transforming solutions of NP-complete problems into solutions of DP-complete problems.
Article
Computer Science, Artificial Intelligence
Ming Zhu, Qiang Yang, Jianping Dong, Gexiang Zhang, Xiantai Gou, Haina Rong, Prithwineel Paul, Ferrante Neri
Summary: OSNPS is a membrane computing model that directly derives an approximate solution to combinatorial problems, specifically the 0/1 knapsack problem, using a family of parallel Spiking Neural P Systems (SNPS) and a Guider algorithm. However, its performance is only competitive with modern metaheuristics in low dimensional cases.
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
(2021)
Article
Biochemical Research Methods
Takayuki Kimura, Nobuaki Yasuo, Masakazu Sekijima, Brooke Lustig
Summary: Characterizing RNA-protein interactions is challenging due to difficulty in obtaining relevant structures. Evaluating model structures using statistical potentials is effective but optimization remains problematic. The study used covariance matrix adaptation to successfully identify native docking poses.
JOURNAL OF MOLECULAR GRAPHICS & MODELLING
(2022)
Article
Engineering, Marine
Yoshiki Miyauchi, Atsuo Maki, Naoya Umeda, Dimas M. Rachman, Youhei Akimoto
Summary: This study investigates the system identification on a mathematical model for berthing maneuver. The proposed method optimizes system parameters with a reduced amount of model tests and achieves better agreement with the free-running model test compared to the traditional captive model test. Furthermore, the proposed method requires fewer data amounts.
JOURNAL OF MARINE SCIENCE AND TECHNOLOGY
(2022)
Article
Social Sciences, Interdisciplinary
Joerg Bremer, Sebastian Lehnhoff
Summary: Cartesian genetic programming, a version of genetic programming, has shown excellent performance in solving various use cases. A new algorithmic level decomposition has been introduced for program evolution using a fully distributed multi-agent system. The applicability and effectiveness of this approach have been successfully demonstrated in symbolic regression, n-parity, and classification problems. The use of CMA-ES as a local decision algorithm has extended the original approach, showing superior performance and scrutinizing the distributed modality of local optimization through fitness landscape analysis.
Article
Computer Science, Information Systems
Zhiwei Xu, Kai Zhang, Juanjuan He, Xiaoming Liu
Summary: In this research, a novel membrane-inspired evolutionary framework with a hybrid dynamic membrane structure is proposed to solve multi-objective multi-task optimization problems. The algorithm improves convergence and diversity, and reduces negative information transfer through the information molecule concentration vector.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Suxia Jiang, Yijun Liu, Bowen Xu, Junwei Sun, Yanfeng Wang
Summary: In this study, asynchronous numerical spiking neural (ANSN) P systems are investigated by combining set theory and threshold control strategy. It is proved that ANSN P systems are Turing universal and capable of processing information.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Yue Ruan, Zhiqiang Yuan, Xiling Xue, Zhihao Liu
Summary: The Quantum Approximate Optimization Algorithm (QAOA) is an algorithmic framework derived from the Quantum Adiabatic Algorithm (QAA) for finding approximate solutions to combinatorial optimization problems. This paper proposes and discusses several QAOA-based algorithms for solving combinatorial optimization problems with equality and/or inequality constraints. The encoding method for different types of constraints is formalized, and the effectiveness and efficiency of the proposed scheme are demonstrated through examples and results for well-known NP optimization problems. Compared to previous constraint-encoding methods, this work provides a more generalized framework for finding higher-quality approximate solutions to combinatorial problems with various types of constraints in the context of QAOA.
INFORMATION SCIENCES
(2023)
Article
Environmental Sciences
Hassan Smaoui, Lahcen Zouhri, Sami Kaidi
Summary: This study introduces a new mathematical model for identifying the HDT of a porous medium based on inverse problem-solving techniques. The model is shown to be practical through an integrated optimization algorithm.
Article
Computer Science, Information Systems
Lei Dai, Liming Zhang, Zehua Chen, Weiping Ding
Summary: This paper proposes a novel deterministic multi-EAs method for multimodal optimization problems. By introducing the principle of global optimization and establishing a survival-of-the-fittest strategy, it achieves effective solutions to multimodal optimization problems without the need for random parameters.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Tanmay Kundu, Harish Garg
Summary: Harris Hawks Optimization (HHO) is a swarm optimization technique that mimics the behavior of Harris hawks, but it may get trapped in local search space. An improved hybrid method, ITLHHO, has been developed to significantly outperform other algorithms and become a promising tool for various optimization problems.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Hao Yang, Min Wang, Zhengfei Yu, Hang Zhang, Jinshen Jiang, Yun Zhou
Summary: In this paper, a novel method called CSTTA is proposed for test time adaptation (TTA), which utilizes confidence-based optimization and sample reweighting to better utilize sample information. Extensive experiments demonstrate the effectiveness of the proposed method.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Jin Liu, Ju-Sheng Mi, Dong-Yun Niu
Summary: This article focuses on a novel method for generating a canonical basis for decision implications based on object-induced operators (OE operators). The logic of decision implication based on OE operators is described, and a method for obtaining the canonical basis for decision implications is given. The completeness, nonredundancy, and optimality of the canonical basis are proven. Additionally, a method for generating true premises based on OE operators is proposed.
KNOWLEDGE-BASED SYSTEMS
(2024)
Review
Computer Science, Artificial Intelligence
Kun Bu, Yuanchao Liu, Xiaolong Ju
Summary: This paper discusses the importance of sentiment analysis and pre-trained models in natural language processing, and explores the application of prompt learning. The research shows that prompt learning is more suitable for sentiment analysis tasks and can achieve good performance.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Xiangjun Cai, Dagang Li
Summary: This paper presents a new decomposition mechanism based on learned decomposition mapping. By using a neural network to learn the relationship between original time series and decomposed results, the repetitive computation overhead during rolling decomposition is relieved. Additionally, extended mapping and partial decomposition methods are proposed to alleviate boundary effects on prediction performance. Comparative studies demonstrate that the proposed method outperforms existing RDEMs in terms of operation speed and prediction accuracy.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Xu Wu, Yang Liu, Jie Tian, Yuanpeng Li
Summary: This paper proposes a blockchain-based privacy-preserving trust management architecture, which adopts federated learning to train task-specific trust models and utilizes differential privacy to protect device privacy. In addition, a game theory-based incentive mechanism and a parallel consensus protocol are proposed to improve the accuracy of trust computing and the efficiency of consensus.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Zaiyang Yu, Prayag Tiwari, Luyang Hou, Lusi Li, Weijun Li, Limin Jiang, Xin Ning
Summary: This study introduces a 3D view-based approach that effectively handles occlusions and leverages the geometric information of 3D objects. The proposed method achieves state-of-the-art results on occluded ReID tasks and exhibits competitive performance on holistic ReID tasks.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Yongliang Shi, Runyi Yang, Zirui Wu, Pengfei Li, Caiyun Liu, Hao Zhao, Guyue Zhou
Summary: Neural implicit representations have gained attention due to their expressive, continuous, and compact properties. However, there is still a lack of research on city-scale continual implicit dense mapping based on sparse LiDAR input. In this study, a city-scale continual neural mapping system with a panoptic representation is developed, incorporating environment-level and instance-level modeling. A tailored three-layer sampling strategy and category-specific prior are proposed to address the challenges of representing geometric information in city-scale space and achieving high fidelity mapping of instances under incomplete observation.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Ruihan Hu, Zhi-Ri Tang, Rui Yang, Zhongjie Wang
Summary: Mesh data is crucial for 3D computer vision applications worldwide, but traditional deep learning frameworks have struggled with handling meshes. This paper proposes MDSSN, a simple mesh computation framework that models triangle meshes and represents their shape using face-based and edge-based Riemannian graphs. The framework incorporates end-to-end operators inspired by traditional deep learning frameworks, and includes dedicated modules for addressing challenges in mesh classification and segmentation tasks. Experimental results demonstrate that MDSSN outperforms other state-of-the-art approaches.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Buliao Huang, Yunhui Zhu, Muhammad Usman, Huanhuan Chen
Summary: This paper proposes a novel semi-supervised conditional normalizing flow (SSCFlow) algorithm that combines unsupervised imputation and supervised classification. By estimating the conditional distribution of incomplete instances, SSCFlow facilitates imputation and classification simultaneously, addressing the issue of separated tasks ignoring data distribution and label information in traditional methods.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Deeksha Varshney, Asif Ekbal, Erik Cambria
Summary: This paper focuses on the neural-based interactive dialogue system that aims to engage and retain humans in long-lasting conversations. It proposes a new neural generative model that combines step-wise co-attention, self-attention-based transformer network, and an emotion classifier to control emotion and knowledge transfer during response generation. The results from quantitative, qualitative, and human evaluation show that the proposed models can generate natural and coherent sentences, capturing essential facts with significant improvement over emotional content.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Junchen Ye, Weimiao Li, Zhixin Zhang, Tongyu Zhu, Leilei Sun, Bowen Du
Summary: Modeling multivariate time series has long been a topic of interest for scholars in various fields. This paper introduces MvTS, an open library based on Pytorch, which provides a unified framework for implementing and evaluating these models. Extensive experiments on public datasets demonstrate the effectiveness and universality of the models reproduced by MvTS.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Reham R. Mostafa, Ahmed M. Khedr, Zaher Al Aghbari, Imad Afyouni, Ibrahim Kamel, Naveed Ahmed
Summary: Feature selection is crucial in classification procedures, but it faces challenges in high-dimensional datasets. To overcome these challenges, this study proposes an Adaptive Hybrid-Mutated Differential Evolution method that incorporates the mechanics of the Spider Wasp Optimization algorithm and the concept of Enhanced Solution Quality. Experimental results demonstrate the effectiveness of the method in terms of accuracy and convergence speed, and it outperforms contemporary cutting-edge algorithms.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Ti Xiang, Pin Lv, Liguo Sun, Yipu Yang, Jiuwu Hao
Summary: This paper introduces a Track Classification Model (TCM) based on marine radar, which can effectively recognize and classify shipping tracks. By using a feature extraction network with multi-feature fusion and a dataset production method to address missing labels, the classification accuracy is improved, resulting in successful engineering application in real scenarios.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Zhihao Zhang, Yuan Zuo, Chenghua Lin, Junjie Wu
Summary: This paper proposes a novel unsupervised context-aware quality phrase mining framework called LMPhrase, which is built upon large pre-trained language models. The framework mines quality phrases as silver labels using a parameter-free probing technique on the pre-trained language model BERT, and formalizes the phrase tagging task as a sequence generation problem by fine-tuning on the Sequence to-Sequence pre-trained language model BART. The results of extensive experiments show that LMPhrase consistently outperforms existing competitors in two different granularity phrase mining tasks.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Kemal Buyukkaya, M. Ozan Karsavuran, Cevdet Aykanat
Summary: The study aims to investigate the hybrid parallelization of the Stochastic Gradient Descent (SGD) algorithm for solving the matrix completion problem on a high-performance computing platform. A hybrid parallel decentralized SGD framework with asynchronous inter-process communication and a novel flexible partitioning scheme is proposed to achieve scalability up to hundreds of processors. Experimental results on real-world benchmark datasets show that the proposed algorithm achieves 6x higher throughput on sparse datasets compared to the state-of-the-art, while achieving comparable throughput on relatively dense datasets.
KNOWLEDGE-BASED SYSTEMS
(2024)