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Evaluating Logical Expressions by Membrane Systems

Chinese Journal of Electronics 2025年第2期23卷 278-283页

作者： Ping Guo Jinfang Ji Haizhu Chen Ran Liu College of Computer Science Chongqing University Chongqing China Chongging Key Laboratory of Software Theory and Technology Chongqing China Department of Software Engineering Chongqing College of Electronic Engineering Chongqing China

Boolean and relational operations, which are defined for solving mathematically logical problems, are always required in computing models. Membrane computing is a kind of distributed parallel computing model. In this paper, we design different membranes for implementing primary Boolean and relational operations respectively. And based on these membranes, a membrane system can be constructed by a present algorithm for evaluating a logical expression. Some examples are given to illustrate how to perform the Boolean, relational operations and evaluate the logical expression correctly in these membrane systems.

关键词： Computational modeling Parallel processing Mathematical models

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DiffFormer: A Differential Spatial-Spectral Transformer for Hyperspectral Image Classification

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 10419-10428页

作者： Ahmad, Muhammad Mazzara, Manuel Distefano, Salvatore Khan, Adil Mehmood Ullo, Silvia Liberata Dhahran31261 Saudi Arabia Innopolis University Institute of Software Development and Engineering Innopolis420500 Russia University of Messina Dipartimento di Matematica e Informatica-MIFT Messina98121 Italy University of Hull School of Computer Science HullHU6 7RX United Kingdom University of Sannio Engineering Department Benevento82100 Italy

Hyperspectral image classification (HSIC) presents significant challenges due to spectral redundancy and spatial discontinuity, both of which can negatively impact classification performance. To mitigate these issues, this work proposes the differential spatial-spectral transformer (DiffFormer), a novel framework designed to enhance feature discrimination and improve classification accuracy. At its core, DiffFormer incorporates a differential multihead self-attention mechanism, which accentuates subtle spectral-spatial variations by applying differential attention across neighboring patches. The architecture integrates spectral-spatial tokenization, utilizing 3-D convolution-based patch embeddings, positional encoding, and a stack of transformer layers augmented with the SwiGLU activation function—a variant of the gated linear unit—to enable efficient and expressive feature extraction. In addition, a token-based classification head ensures robust representation learning, facilitating precise pixelwise labeling. Extensive experiments on benchmark hyperspectral datasets demonstrate that DiffFormer consistently outperforms state-of-the-art methods in classification accuracy, computational efficiency, and generalizability. © 2008-2012 IEEE.

关键词： Image classification

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Arithmetic Expression Evaluations with Membranes

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Chinese Journal of Electronics 2025年第1期23卷 55-60页

作者： Ping Guo Haizhu Chen Hui Zheng College of Computer Science Chongqing University Chongqing China Chongqing Key Laboratory of Software Theory and Technology Chongqing China Department of Software Engineering Chongqing College of Electronic Engineering Chongqing China

Arithmetic operations and expression evaluations are fundamental in computing models. This paper firstly designs arithmetic membranes without priority rules for basic arithmetic operations, and then proposes an algorithm to construct expression P systems based on several of such membranes after designing synchronous and asynchronous transmission strategies among the membranes. For any arithmetic expression, an expression P system can be built to evaluate it effectively. Finally, we discuss different parallelism strategies through which different expression P systems can be built for an arithmetic expression.

关键词： Computational modeling Biological system modeling Parallel processing Biomembranes Compounds Arithmetic

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A Meta-Model to Support Compatibility Testing of Cross-Browser Web Application

A Meta-Model to Support Compatibility Testing of Cross-Brows...

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International Conference on Advancements in Computational sciences (ICACS)

作者： Mishal Muneer Uzair Rasheed Muhammad Majid Hussain Department of Computer Science & IT The University of Lahore Lahore Pakistan Department of Computer and Software Engineering College of Electrical and Mechanical Engineering NUST Islamabad Pakistan

ISBN: (数字)9798331533038

ISBN: (纸本)9798331533045

Ensuring the user interface (UI) compatibility of web applications across diverse client-side configurations, including various operating systems and browsers, is a significant challenge due to the extensive range of possible combinations. Traditional tools often struggle to address this complexity, leading to visual inconsistencies and malfunctions. To improve compatibility testing, we propose a meta-model that initiates the process with a checklist covering critical configurations across different browsers. This checklist is then translated into Interaction Flow Modeling Language (IFML) constructs, enabling a model-driven approach to compatibility assessment. By leveraging this checklist-IFML integration, we systematically generate test cases that target compatibility issues more effectively. Our approach is validated through the Laptop-Web Case Study, where we assess compatibility by comparing checklist items with IFML constructs within the case study’s domain model. The results show that our approach enhances the accuracy and efficiency of compatibility testing, addressing a core gap in current methods. This research introduces a structured, model-driven method for compatibility testing, providing a more reliable framework for identifying and mitigating UI compatibility issues in web applications across varying client-side configurations.

关键词： Visualization Translation Flow production systems Computational modeling Operating systems User interfaces Reliability engineering Browsers Complexity theory Testing

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Introduction to the Special Issue on Formal Methods and Models for System Design

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ACM Transactions on Embedded Computing Systems 2025年第3期24卷 1-3页

作者： Jens Brandt Indranil Saha Lijun Zhang Engineering and Computer Science Hochschule Niederrhein Krefeld Germany Department of Computer Science and Engineering Indian Institute of Technology Kanpur Kanpur India State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China

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Efficient Fingerprint Database Organization for Optimized Recognition

Efficient Fingerprint Database Organization for Optimized Re...

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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： Jeffrey Jeeva Dhas Adhip Koshy Nishit Dua D. Ajitha Department of Software Systems School of Computer Science and Engineering Vellore Institute of Technology Vellore Tamil Nadu India Department of Software Systems School of Computer Science and Engineering Vellore Institute of Technology Vellore Vellore India

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

A popular biometric identification method, renowned for its dependability and individuality in personal identification, is fingerprint recognition. This article presents an efficient fingerprint identification system that significantly reduces processing times for 100,000 fingerprints from 8 minutes to less than 5 seconds by utilizing multithreading, ANSI 378 templates, and Source AFIS. The system overcomes the time-consuming image-to-template conversion procedure and increases overall storage efficiency by utilizing parallel processing and preformatted templates (ANSI/ISO). The fingerprint matching system incorporates Directional Field, CNN models for identifying fingerprint types, and Finger Code methods to classify fingerprints, improving precision and efficiency.

关键词： Technological innovation Multithreading Law enforcement System performance Organizations Machine learning Fingerprint recognition Biometric identification Real-time systems Informatics

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Deep domain adaptation by joint distribution neural matching

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Neural Computing and Applications 2025年 1-21页

作者： Hong, Zijie Chen, Sentao Wen, Lisheng Yang, Xiaowei School of Software Engineering South China University of Technology Guangzhou China Department of Computer Science Shantou University Shantou China

Domain adaptation aims to learn a prediction model that performs well in a target domain with unlabeled data by harnessing the distributionally different labeled data from a source domain. In this paper, we design a simple yet powerful neural network approach named joint distribution neural matching (JOINT) to address the challenge of joint distribution difference/mismatch in domain adaptation. Our JOINT approach matches the source joint distribution and the target one in the activation space of a neural network by minimizing an estimate of the Jensen–Shannon (JS) divergence between the two joint distributions. The estimated JS divergence is derived in an explicit form, which frees our approach from solving a minimax adversarial problem when matching the distributions. Experiments on four representative large-scale visual recognition datasets show that our JOINT approach outperforms the state-of-the-art under both unsupervised and semi-supervised domain adaptation settings. Our code will be available at https://***/TitanRisen/JOINT-CODE .

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Evaluating Feature Prioritization in a Reduced Feature Set Using Explainable AI for Intracranial Aneurysm Classification

Evaluating Feature Prioritization in a Reduced Feature Set U...

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International Conference on Advancements in Computational sciences (ICACS)

作者： Iram Abdullah Ali Javed Department of Computer Science Air University Kamra Pakistan Department of Software Engineering University of Engineering & Technology Taxila Pakistan

ISBN: (数字)9798331533038

ISBN: (纸本)9798331533045

The timely management of an unruptured aneurysm can prevent hemorrhage. Multiple anatomical, morphological, and hemodynamic features associated with an aneurysm can predict the risk of rupture and classify the nature of the aneurysm as either ruptured or unruptured. However, considering all features may create ambiguities in the model due to inter-dependencies, such as the correlation between aneurysm height and width to aneurysm area. In computer-aided medical diagnostics, focusing on relevant features that diagnose the disease is more crucial than using an abundance of features that may not contribute to the diagnosis and may cause unnecessary complexity. The use of independent or sparsely correlated features improves the accuracy of the classifier. The proposed research analyzes the interdependency of features by calculating the correlation between features. Based on the analyses, the high correlation threshold is determined through rigorous experimentation. From the highly correlated features, one feature is selected. Consequently, the reduced feature set is recommended. The reduced feature set performs better with simpler classifiers. Instead of choosing a classifier that struggles with a broad range of features, an appropriately optimized feature set can enhance performance and improve accuracy. The proposed approach is tested on a publicly available dataset of aneurysms, leading to an improvement in classification accuracy from 82.6% to 87%, due to the use of an appropriate feature set. Moreover, the best model was evaluated using two different explainable AI frameworks to assess the contribution of each feature in prediction. However, the conflicting feature prioritization in each explainable framework suggests that these models prioritize different features for the same trained model, leading to ambiguity. This inconsistency indicates that the explainable models lack generalization and fail to consistently analyze feature importance.

关键词： Accuracy Correlation Explainable AI Computational modeling Redundancy Training data Aneurysm Predictive models Medical diagnosis Random forests

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Optimizing Heart-Kidney Interaction for Cancer Detection through Physiological Process Simulation as a Decision Support System

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IEEE Access 2025年 13卷 79169-79179页

作者： Jaddi, Najmeh Sadat Abdullah, Salwanj Othnman, Zalinda Yahya, Yazrina Nazri, Mohd Zakree Ahmad Goh, Say Leng Alvankarian, Fatemeh Alvankarian, Jafar Iranian eUniversity Faculty of Computer Engineering Tehran Iran Faculty of Information Science and Technology Center for Artificial Intelligence Technology Selangor Bangi43600 Malaysia Center for Software Technology and Management Faculty of Information Science and Technology UniversitiKebangsaan Malaysia Selangor Bangi43600 Malaysia Universiti Malaysia Sabah KampusAntarabangsa Labuan Optimisation and Analytics Research Group Faculty of Computing and Informatics Labuan87000 Malaysia University of Maryland Department of Electrical & Computer Engineering College ParkMD20742 United States Tehran University Science and Technology Park RayanMiniFab Tehran Iran

Decision support system (DSS) is a computer-based tool used to improve decision-making capabilities for any organization, using analysis of the available *** heart-kidney (HK) model proposed in this paper as a DSS simulates the physiological processes of the heart and kidneys in the human body to support cancer detection. The first part of the HK algorithm is inspired by the functions of the heart, where blood molecules (solutions) are sent to the heart, simulating the processes of oxygenation and the pumping of oxygenated blood to the kidneys as a local search. The solutions generated by the heart are then fed into the kidney-inspired algorithm (KA), which performs filtration, reabsorption, secretion, and excretion. The heart component enhances the KA's local search capability, guiding the algorithm toward optimal solutions. The HK model is evaluated on eight test functions, with results compared to those of the heart algorithm and KA alone. Additionally, the HK algorithm's effectiveness as a decision support system is tested on benchmark classification and time series prediction problems, with statistical analysis against existing methods in the literature. Finally, the HK algorithm is applied to cancer detection from microRNA data, demonstrating its potential in optimizing decision-making processes in healthcare. © 2013 IEEE.

关键词： Diseases

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Ownership Verification for Federated Recommendation

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ACM Transactions on Information Systems 2025年第3期43卷 1-27页

作者： Enyue Yang Weike Pan Lixin Fan Hanlin Gu Zhitao Li Qiang Yang Zhong Ming College of Computer Science and Software Engineering Shenzhen University Shenzhen China AI Department WeBank Shenzhen China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong China and AI Department WeBank Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen Technology University Shenzhen China and Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen China

Most federated learning-based recommender systems allow clients to access a well-trained high-quality model locally, which provides adversaries with the opportunity to infringe the legitimate copyright of the model. In response, we study an emerging and important problem, i.e., copyright protection of a federated recommendation model, which has not yet been addressed in the community of federated learning or recommender systems. We propose the first backdoor-based ownership verification scheme for federated recommendation (OVFR), which enables the server to claim its ownership for a given suspicious recommendation model. First, we propose to generate a trigger set tailored to recommendation scenarios. In particular, we generate some fake users and items, and then construct a set of fake users with fake interaction records as a trigger set. Moreover, we ensure that the distribution of the popularity of the fake items follows a long-tailed distribution for the effectiveness of the incorporated watermarking. To provide robustness assurance, we propose two different hybrid strategies to make the embeddings of the fake items similar to those of the real items. Second, we focus on effectively learning from a trigger set for recommendation scenarios. In particular, we design an MSE loss function and a contrastive loss function for incorporating the backdoor-based watermarking into the item embeddings, since the item embeddings are often more valuable and easier to be accessed than other parameters of a federated recommendation model. We then design a contrastive loss function to reduce the risk of the fake items being detected. Extensive experiments on three public datasets show the effectiveness of our OVFR in terms of ownership verification, model performance, and robustness.

关键词： Federated recommendation Ownership verification

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