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Performance *** Comparative Analysis of Multimodal Bilinear Pooling Fusion Approaches for Deep Learning-Based Visual Arabic-Question Answering systems

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computer Modeling in engineering & Sciences 2025年第4期143卷 373-411页

作者： Sarah M.Kamel Mai A.Fadel Lamiaa Elrefaei Shimaa I.Hassan Electrical Engineering Department Faculty of Engineering at ShoubraBenha UniversityCairo11629Egypt Computer Science Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer and Systems Engineering Faculty of Engineering and TechnologyBadr University in Cairo(BUC)Cairo11829Egypt Communication Systems Engineering Department Faculty of EngineeringBenha National UniversityObour11846QalyubiaEgypt

Visual question answering(VQA)is a multimodal task,involving a deep understanding of the image scene and the question’s meaning and capturing the relevant correlations between both modalities to infer the appropriate *** this paper,we propose a VQA system intended to answer yes/no questions about real-world images,in *** support a robust VQA system,we work in two directions:(1)Using deep neural networks to semantically represent the given image and question in a fine-grainedmanner,namely ResNet-152 and Gated Recurrent Units(GRU).(2)Studying the role of the utilizedmultimodal bilinear pooling fusion technique in the *** the model complexity and the overall model *** fusion techniques could significantly increase the model complexity,which seriously limits their applicability for VQA *** far,there is no evidence of how efficient these multimodal bilinear pooling fusion techniques are for VQA systems dedicated to yes/no ***,a comparative analysis is conducted between eight bilinear pooling fusion techniques,in terms of their ability to reduce themodel complexity and improve themodel performance in this case of VQA *** indicate that these multimodal bilinear pooling fusion techniques have improved the VQA model’s performance,until reaching the best performance of 89.25%.Further,experiments have proven that the number of answers in the developed VQA system is a critical factor that *** the effectiveness of these multimodal bilinear pooling techniques in achieving their main objective of reducing the model *** Multimodal Local Perception Bilinear Pooling(MLPB)technique has shown the best balance between the model complexity and its performance,for VQA systems designed to answer yes/no questions.

关键词： Arabic-VQA deep learning-based VQA deep multimodal information fusion multimodal representation learning VQA of yes/no questions VQA model complexity VQA model performance performance-complexity trade-off

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An enhanced framework for smart automated evaluations of answer scripts using NLP and deep learning methods

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Multimedia Tools and Applications 2025年第11期84卷 8491-8513页

作者： G, Mohanraj R.K, Nadesh M, Marimuthu V, Sathiyapriya School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore632014 India School of Computer Science and Engineering Vellore Institute of Technology Chennai600127 India Knowledge Institute of Technology Salem637504 India

The manual process of evaluating answer scripts is strenuous. Evaluators use the answer key to assess the answers in the answer scripts. Advancements in technology and the introduction of new learning paradigms need automation of the evaluation process. This work aims to develop an enhanced novel hybrid framework that can evaluate answer scripts and automatically assign marks for different type of questions based on keywords, grammar, symbols, special keywords, and the given factors. First, the proposed system uses Optical Character Recognition (OCR) to convert image answer scripts into an editable text format. Second, the sentence transformers, the Natural Language Processing (NLP) technique flips the answer script and answers key texts into word embedding vectors. To find similarity measures, these vectors are matched using BERT encoding, spearmanś rank-order correlation, and fuzzy search. At last, the proposed model is trained using Deep Columnar Convolutional Neural Network (DCCNN) in the third step with MINST and Kaggle handwritten mathematical symbols and tested with the segmented mathematical equations to find the similarity. The performance of proposed model is measured using precision, recall, accuracy, and F1-score, and its gives highest accuracy of 93% and 95% when compared to the existing methodologies. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Optical character recognition

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Advanced Scalable Multi-Beam Focusing for Indoor Optical Wireless Networks With IR Radiative Clusters

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2025年 6卷 3624-3643页

作者： Gunathilake, Sharadhi Nirmalathas, Ampalavanapillai Herath, Kosala Premaratne, Malin Monash University Department of Electrical and Computer Systems Engineering ClaytonVIC3800 Australia The University of Melbourne Department of Electrical and Electronic Engineering ParkvilleVIC3010 Australia

Optical wireless networks emerge as a promising solution to the ever-growing data demand for user-centric indoor applications. This work demonstrates a novel approach to advance multi-beam radiation patterns in indoor optical wireless networks by utilizing a cluster-based optical aperture comprising IR radiative elements. Spatially distributed IR clusters permit a non-uniform spherical wave model to focus the radiation in the near-field regime. By executing sub-clusters within main clusters and assigning them to groups for phase delay compensation, we ensure the generation of independent narrow beams focused on each receiver simultaneously. To mitigate the grating lobe formation, we incorporate a dual-carrier framework that introduces an effective wavelength for the system. Based on this theoretical model, we examine multi-beam focusing with a systematic arrangement of clusters on a planar ceiling. It follows a phased array within a phased array structure and incorporates a sub-cluster segmentation algorithm. We suggest optimizing cluster excitation based on receiver positions to enhance power efficiency and safety. This involves selecting the optimal clusters from a uniform array by solving a multiobjective non-convex binary optimization problem, aiming to maximize receiver intensity, minimize intensity variations, and reduce side lobes level. Instead of stochastic algorithms, we adopt a sparse relaxation-based weighted sum method that convexifies the binary space with L1 norm regularization compensating for convexity. The Transformed problem is solved deterministically via Nelder-Mead simplex without gradients. Simulated results confirm a better multi-beam focusing pattern, effectively balancing three objectives. Our findings pave the way for sustainable indoor optical wireless networks in next-generation communication. © 2020 IEEE.

关键词： Multiobjective optimization

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GTE: learning code AST representation efficiently and effectively

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Science China(Information Sciences) 2025年第3期68卷 393-394页

作者： Yihao QIN Shangwen WANG Bo LIN Kang YANG Xiaoguang MAO College of Computer Science and Technology National University of Defense Technology Key Laboratory of Software Engineering for Complex Systems National University of Defense Technology

With the development of deep learning in recent years, code representation learning techniques have become the foundation of many software engineering tasks such as program classification [1] and defect detection. Earlier approaches treat the code as token sequences and use CNN, RNN, and the Transformer models to learn code representations.

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Coordinated Jamming and Poisoning Attack Detection and Mitigation in Wireless Federated Learning Networks

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2025年 6卷 3745-3759页

作者： Barkatsa, Sofia Diamanti, Maria Charatsaris, Panagiotis Voikos, Stefanos Tsiropoulou, Eirini Eleni Papavassiliou, Symeon Institute of Communication and Computer Systems School of Electrical and Computer Engineering National Technical University of Athens Zografou15780 Greece Arizona State University School of Electrical Computer and Energy Engineering Performance and Resource Optimization in Networks –PROTON Lab TempeAZ85287 United States

Wireless Federated Learning (FL) is a distributed Artificial Intelligence (AI) framework, enabling decision-making at the network edge where data are generated. However, wireless transmissions of model updates from edge nodes to the coordinating server are vulnerable to jamming, alongside the inherent risk of poisoning the learning process. In this paper, we tackle the problem of coordinated jamming and poisoning attacks in wireless FL networks, where malicious edge nodes disrupt transmissions of legitimate local model updates to the cloud server while injecting poisoned model updates to manipulate the global model. To this end, we introduce two complementary mechanisms operating alternately. First, a robust global model aggregation algorithm is developed to address poisoning attacks by weighting edge nodes’ local model updates using a novel contribution index. The calculation of the index is inspired by the Shapley value, but it offers polynomial complexity compared to existing methods. Subsequently, a distributed power control solution for jamming attack mitigation in the uplink of the FL network is introduced based on Bayesian games with incomplete information. Both legitimate and malicious nodes aim to successfully transmit their model parameters, minimizing transmission power and time to the server, while having probabilistic knowledge about the malicious behavior of the other nodes in the game. The proposed unified approach and each individual mechanism are assessed via modeling and simulation, verifying their effectiveness in mitigating both attacks while achieving a good tradeoff between global model accuracy and consumed time and energy compared to state-of-the-art approaches. © 2020 IEEE.

关键词： Federated learning

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Reliable Task Offloading for 6G-Based IoT Applications

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computers, Materials & Continua 2025年第2期82卷 2255-2274页

作者： Usman Mahmood Malik Muhammad Awais Javed Ahmad Naseem Alvi Mohammed Alkhathami Department of Computer Software Engineering National University of Science and Technology(NUST)Islamabad44000Pakistan Department of Electrical and Computer Engineering COMSATS University IslamabadIslamabad45550Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia

Fog computing is a key enabling technology of 6G systems as it provides quick and reliable computing,and data storage services which are required for several 6G *** Intelligence(AI)algorithms will be an integral part of 6G systems and efficient task offloading techniques using fog computing will improve their performance and *** this paper,the focus is on the scenario of Partial Offloading of a Task to Multiple Helpers(POMH)in which larger tasks are divided into smaller subtasks and processed in parallel,hence expediting task ***,using POMH presents challenges such as breaking tasks into subtasks and scaling these subtasks based on many interdependent factors to ensure that all subtasks of a task finish simultaneously,preventing resource ***,applying matching theory to POMH scenarios results in dynamic preference profiles of helping devices due to changing subtask sizes,resulting in a difficult-to-solve,externalities *** paper introduces a novel many-to-one matching-based algorithm,designed to address the externalities problem and optimize resource allocation within POMH ***,we propose a new time-efficient preference profiling technique that further enhances time optimization in POMH *** performance of the proposed technique is thoroughly evaluated in comparison to alternate baseline schemes,revealing many advantages of the proposed *** simulation findings indisputably show that the proposed matching-based offloading technique outperforms existing methodologies in the literature,yielding a remarkable 52 reduction in task latency,particularly under high workloads.

关键词： 6G IoT task offloading fog computing

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Edge-aware Feature Aggregation Network for Polyp Segmentation

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Machine Intelligence Research 2025年第1期22卷 101-116页

作者： Tao Zhou Yizhe Zhang Geng Chen Yi Zhou Ye Wu Deng-Ping Fan PCA Lab Key Laboratory of Intelligent Perception and Systems for High-dimensional Information of Ministry of EducationSchool of Computer Science and EngineeringNanjing University of Science and TechnologyNanjing210094China School of Computer Science and Engineering Northwestern Polytechnical University(NPU)Xi’an710129China School of Computer Science and Engineering Southeast UniversityNanjing211189China Computer Vision Lab ETH ZürichZürich8092Switzerland

Precise polyp segmentation is vital for the early diagnosis and prevention of colorectal cancer(CRC)in clinical ***,due to scale variation and blurry polyp boundaries,it is still a challenging task to achieve satisfactory segmentation performance with different scales and *** this study,we present a novel edge-aware feature aggregation network(EFA-Net)for polyp segmentation,which can fully make use of cross-level and multi-scale features to enhance the performance of polyp ***,we first present an edge-aware guidance module(EGM)to combine the low-level features with the high-level features to learn an edge-enhanced feature,which is incorporated into each decoder unit using a layer-by-layer ***,a scale-aware convolution module(SCM)is proposed to learn scale-aware features by using dilated convolutions with different ratios,in order to effectively deal with scale ***,a cross-level fusion module(CFM)is proposed to effectively integrate the cross-level features,which can exploit the local and global contextual ***,the outputs of CFMs are adaptively weighted by using the learned edge-aware feature,which are then used to produce multiple side-out segmentation *** results on five widely adopted colonoscopy datasets show that our EFA-Net outperforms state-of-the-art polyp segmentation methods in terms of generalization and *** implementation code and segmentation maps will be publicly at https://***/taozh2017/EFANet.

关键词： Colorectal cancer polyp segmentation edge-aware guidance module scale-aware convolution module cross-level fusion module

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Author profiling from Romanized Urdu text using transfer learning models

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Neural Computing and Applications 2025年第6期37卷 4455-4470页

作者： Ali, Abid khan, Muhammad Sohail Khan, Muhammad Amin Khan, Sajid Ullah Khan, Faheem Department of Computer Software Engineering University of Engineering & Technology Mardan KPK Mardan23200 Pakistan Islamabad H-11/4 Islamabad44000 Pakistan Department of Information Systems College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia Department of Computer Engineering Gachon University Seongnam-Si Korea Republic of

This research concentrates on author profiling using transfer learning models for classifying age and gender. The investigation encompassed a diverse set of transfer learning techniques, including Roberta, BERT, ALBERT, Distil BERT, Distil Roberta, ELECTRA, and XLNet. Through meticulous evaluation using metrics such as the Matthews Correlation Coefficient, Accuracy, Precision, Recall, and F1 Score, the study examined the efficacy of these models. The curated dataset was divided for gender and age tasks, resulting in robust gender prediction with the XLNet model and age prediction with the BERT model. Notably, the XLNet model achieved the highest MCC (0.7946), Accuracy (0.8957), Precision (0.8992), Recall (0.8957), and F1 Score (0.8958) values in gender classification, while the BERT model excelled in age prediction with an MCC of (0.7338), Accuracy of (0.8220), Precision of (0.8324), Recall of (0.8220), and F1 Score of (0.8243). Visualized outcomes provide valuable insights into the model’s performance nuances, paving the way for their practical implementation. This research offers novel contributions to author profiling tasks, bridging the gap between theory and real-world applications. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Contrastive Learning

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Distributed Attention-Enabled Multi-Agent Reinforcement Learning Based Frequency Regulation of Power systems

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IEEE Transactions on Power systems 2025年第3期40卷 2427-2437页

作者： Zhao, Yunzheng Liu, Tao Hill, David J. University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong Hong Kong Monash University Department of Electrical and Computer Systems Engineering ClaytonVIC3800 Australia

This paper develops a new distributed attention-enabled multi-agent reinforcement learning method for frequency regulation of power systems. Specifically, the controller of each generator is modelled as an agent, and the reward and observation are designed based on the characteristics of power systems. All the agents learn their own control policies in the offline training phase and generate frequency control signals in the online execution phase. The target of the proposed algorithm is to conduct both offline training and online frequency control in a distributed way. To achieve this goal, two distributed information-sharing mechanisms are proposed based on the different global information to be discovered. First, a consensus-based reward-sharing mechanism is designed to estimate the globally averaged reward. Second, a distributed observation-sharing scheme is developed to discover the global observation information. Furthermore, the attention strategy is embedded in the observation-sharing scheme to help agents adaptively adjust the importance of observations from different neighbors. With these two mechanisms, a new distributed attention-enabled proximal policy optimization (DAPPO) based method is proposed to achieve model-free frequency control. Simulation results on the IEEE 39-bus system and the NPCC 140-bus system demonstrate that the proposed DAPPO achieves stable offline training and effective online frequency control. © 1969-2012 IEEE.

关键词： Reinforcement learning

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Memory Complexity of Estimating Entropy and Mutual Information

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IEEE Transactions on Information Theory 2025年第5期71卷 3334-3349页

作者： Berg, Tomer Ordentlich, Or Shayevitz, Ofer Tel Aviv University Department of Electrical Engineering—Systems Tel Aviv-Yafo69978 Israel Hebrew University of Jerusalem School of Computer Science and Engineering Jerusalem91904 Israel

We observe an infinite sequence of independent identically distributed random variables X1, X2, . . . drawn from an unknown distribution p over [n], and our goal is to estimate the entropy H(p) = − E[log p(X)] within an Ε-additive *** that end, at each time point we are allowed to update a finite-state machine with S states, using a possibly randomized but time-invariant rule, where each state of the machine is assigned an entropy estimate. Our goal is to characterize the minimax memory complexity S∗ of this problem, which is the minimal number of states for which the estimation task is feasible with probability at least 1 − δ asymptotically, uniformly in ***, we show that there exist universal constants C1and C2 such that S∗ (Formula presented) for Ε not too small, andS∗ (Formula presented) for Ε not too large. The upper bound is proved using approximate counting to estimate the logarithm of p, and a finite memory bias estimation machine to estimate the expectation operation. The lower bound is proved via a reductionof entropy estimation to uniformity testing. We also apply these results to derive bounds on the memory complexity of mutual information estimation. © 1963-2012 IEEE.

关键词： Entropy Estimation Complexity theory Memory management Mutual information Lower bound Additives Upper bound Testing Automata

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