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Intelligent data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Bosubabu Sambana Kotamsetty Geethika Devi Bandi Rajeswara Reddy Galeti Mohammad Hussain Gownivalla Siddartha Department of Computer Science and Engineering (Data Science) School of Computing Mohan Babu University Tirupati India Department of Computer Science and Systems Engineering Sree Vidyanikethan Engineering College Tirupati India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

The recent development of advanced machine learning methods for hybrid models has greatly addressed the need for the correct prediction of electrical prices. This method combines AlexNet and LSTM algorithms, which are used to introduce a new model with higher accuracy in price forecasting. Despite RNN and ANN being effective, they often fail to deal with forex time sequence data. The traditional methods do not accurately forecast the prices. These traditional methods only focus on demand and price which leads to insufficient analysis of data. To address this issue, using the hybrid approach, which focuses on external variables that also effect the predicted prices. Nevertheless, due to AlexNet's excellent feature extraction and LSTM's learning sequential patterns, the prediction accuracy is vastly increased. The model is built on the past data, which has been supplied with the most significant elements like demand, temperature, sunlight, and rain. For example, the model applies methods, such as minimum-maximum scaling and a time window, to predict the electricity prices of the future. The results show that this hybrid model is good than the standalone ones in terms of accuracy. Although we got our accuracy rating of 97.08, it shows higher accompaniments than remaining models RNN and ANN with accuracies of 96.64 and 96.63 respectively.

关键词： Temperature distribution Accuracy Recurrent neural networks Rain Electricity Predictive models Prediction algorithms data models Forecasting Long short term memory

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Advancing Multi-Party Dialogue Systems with Speaker-ware Contrastive Learning

arXiv

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arXiv 2025年

作者： Hu, Zhongtian Cui, Yiwen Li, Ronghan Zhao, Meng Wang, Lifang School of Computer Science and Engineering Northwestern Polytechnical University China School of Computer Science and Technology Xidian University China School of Artificial Intelligence and Big Data Henan University of Technology China

Dialogue response generation has made significant progress, but most research has focused on dyadic dialogue. In contrast, multi-party dialogues involve more participants, each potentially discussing different topics, making the task more complex. Current methods often rely on graph neural networks to model dialogue context, which helps capture the structural dynamics of multi-party conversations. However, these methods are heavily dependent on intricate graph structures and dataset annotations, and they often overlook the distinct speaking styles of participants. To address these challenges, we propose CMR, a Contrastive learning-based Multi-party dialogue Response generation model. CMR uses self-supervised contrastive learning to better distinguish "who says what." Additionally, by comparing speakers within the same conversation, the model captures differences in speaking styles and thematic transitions. To the best of our knowledge, this is the first approach to apply contrastive learning in multi-party dialogue generation. Experimental results show that CMR significantly outperforms state-of-the-art models in multiparty dialogue response tasks. © 2025, CC BY.

关键词： Self-supervised learning

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A Comparative Performance Analysis of Classification and Segmentation Models on Bangladeshi Pothole dataset

arXiv

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arXiv 2025年

作者： Parsa, Antara Firoz Abdullah, S.M. Talukder, Anika Hasan Kabbya, Md Asif Shahidullah Hasan, Shakib Al Islam, Md Farhadul Noor, Jannatun Department of Computer Science and Engineering School of Data and Sciences BRAC University Dhaka Bangladesh Research Group Department of Computer Science and Engineering United International University Dhaka Bangladesh

The study involves a comprehensive performance analysis of popular classification and segmentation models, applied over a Bangladeshi pothole dataset, being developed by the authors of this research. This custom dataset of 824 samples, collected from the streets of Dhaka and Bogura performs competitively against the existing industrial and custom datasets utilized in the present literature. The dataset was further augmented four-fold for segmentation and ten-fold for classification evaluation. We tested nine classification models (CCT, CNN, INN, Swin Transformer, ConvMixer, VGG16, ResNet50, DenseNet201, and Xception) and four segmentation models (U-Net, ResU-Net, U-Net++, and Attention-Unet) over both the datasets. Among the classification models, lightweight models namely CCT, CNN, INN, Swin Transformer, and ConvMixer were emphasized due to their low computational requirements and faster prediction times. The lightweight models performed respectfully, oftentimes equating to the performance of heavyweight models. In addition, augmentation was found to enhance the performance of all the tested models. The experimental results exhibit that, our dataset performs on par or outperforms the similar classification models utilized in the existing literature, reaching accuracy and f1-scores over 99%. The dataset also performed on par with the existing datasets for segmentation, achieving model Dice Similarity Coefficient up to 67.54% and IoU scores up to 59.39%. © 2025, CC BY-NC-SA.

关键词： Classification (of information)

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Enhancing PAN Card Security with OCR Based Information Extraction and Face Recognition for Reliable Identity Verification

Enhancing PAN Card Security with OCR Based Information Extra...

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data Science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： D N Harshavardhan Reddy Malapati Sai Sravanth Reddy Panyam Sudha Dandapani Computer Science and Engineering With Data Science Sathyabama Institute of Science and Technology Chennai India Computer Science and Engineering School of Computing Sathyabama Institute of Science and Technology Chennai India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

Due to the potential for impersonation, the Indian PAN card is of great importance and thus, it should be encrypted with certain methods. The paper proposes a system that deals for achieving greater security of PAN card by applying a combination of OCR based information extraction and face recognition system as a means of securing identity verification. A set of OCR algorithms is first entrusted with the task to accurately read and translate the text found on the PAN card which includes the, date of birth, name and the PAN number, along with other features. The digitized and translated data is easier to store and retrieve, which makes the verification process quicker. Furthermore, a deep learning face recognition module is added in a bid to match the face of the PAN card holder with the face on the card. The proposed system is based on the architecture of the convolutional neural network (CNN) which has been shown to perform well in systems that need to compare in different amounts of light and at different angles making it reduce both the false acceptance and the false rejection rates. This article emphasizes the need for cutting-edge technologies to be implemented in identity verification systems, as identity fraud can be avoided through the use of computerized Optical character recognition technology (OCR) combined with face recognition technology that provides advanced scanning capabilities advanced scanning capabilities that assist in safeguarding the PAN card.

关键词： Privacy Translation Face recognition Optical character recognition Information retrieval Fraud Convolutional neural networks Reliability Protection Next generation networking

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Can xLLMs Understand the Structure of Dialog? Exploring Multilingual Response Generation in Complex Scenarios

arXiv

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arXiv 2025年

Multilingual research has garnered increasing attention, especially in the domain of dialogue systems. The rapid advancements in large language models (LLMs) have fueled the demand for high-performing multilingual models. However, two major challenges persist: the scarcity of high-quality multilingual datasets and the limited complexity of existing datasets in capturing realistic dialogue scenarios. To address these gaps, we introduce XMP, a high-quality parallel Multilingual(X) dataset sourced from Multi-party Podcast dialogues1. Each sample in the dataset features at least three participants discussing a wide range of topics, including society, culture, politics, and entertainment. Through extensive experiments, we uncover significant limitations in previously recognized multilingual capabilities of LLMs when applied to such complex dialogue scenarios. For instance, the widely accepted multilingual complementary ability of LLMs is notably impacted. By conducting further experiments, we explore the mechanisms of LLMs in multilingual environments from multiple perspectives, shedding new light on their performance in real-world, diverse conversational contexts. © 2025, CC BY.

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A secure quantum homomorphic encryption ciphertext retrieval scheme

引用

Soft Computing 2025年第3期29卷 1497-1509页

作者： Cheng, Zhen-Wen Chen, Xiu-Bo Xu, Gang Chang, Yan Miao, Li-Hua Yang, Yi-Xian Wang, Ya-Lan School of Data and Computer Science Shandong Women’s University Jinan250300 China Information Security Center State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China School of Information Science and Technology North China University of Technology Beijing100144 China School of Cybersecurity Chengdu University of Information Technology Chengdu610225 China Huawei Technologies Co. Ltd Shenzhen518129 China Department of Computer Science Faculty of Engineering and Physical Sciences University of Surrey Surrey GuildfordGU2 7XH United Kingdom

In a recent paper (Gong et al. Quantum Inf Process 19:3, 2020), a novel ciphertext retrieval scheme based on the Grover algorithm and quantum homomorphic encryption was presented. In this scheme, when the server performs the operation of marking the solution on the user’s encrypted state in the Grover iteration, it needs to remove many gate-errors generated in the homomorphic evaluation of the T gate. And the server could judge this specific solution from the quantum circuit of marking the solution. It makes this scheme unable to achieve the low-cost and secure ciphertext retrieval. Therefore, we improve the Gong et al.’s scheme and propose a secure quantum homomorphic encryption ciphertext retrieval scheme. In our scheme, the trusted third party is introduced to cooperate with the server to execute the Grover algorithm. In each Grover iteration, the trusted third party can quickly mark the solution on the plaintext state, encrypt the marked state, and transmit it to the server. Then the server performs the remaining operations of this Grover iteration on the encrypted state. The trusted third party finally decrypts the iterated state. This cooperative approach ensures that the number of auxiliary qubits required and extra quantum gates executed in our scheme are lower than the Gong et al.’s scheme. By analyzing the security of our scheme, we confirm that the server and the trusted third party will not be informed of this solution. Thus, our scheme realizes the secure ciphertext retrieval with low computational overhead. We utilize IBM’s Qiskit framework to simulate our scheme, and the experimental result shows that our scheme is correct. It is worth noting that the low-cost and secure ciphertext retrieval will play a crucial role in modern information security and privacy protection. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Ciphertext

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Parameter-Efficient Image Classification with Convolutional Spiking Neural Network via Fast Sigmoid Surrogate Gradient Descent

Parameter-Efficient Image Classification with Convolutional ...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Joy Datta Fabliha Afaf Sarwar Rawhatur Rabbi Puja Saha Md. Golam Rabiul Alam Jia Uddin Aniqua Nusrat Zereen Department of Computer Science & Engineering School of Data & Sciences Brac University Dhaka Bangladesh Department of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh AI and Big Data Department Endicott College Woosong University Daejeon South Korea

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Spiking Neural Networks (SNNs) offer several advantages over traditional Artificial Neural Networks (ANNs), especially in terms of biologically inspired event-driven mechanisms and energy efficiency. This paper aims to demonstrate parameter efficiency of Convolutional Spiking Neural Networks (CSNNs) by training extremely small CSNNs and comparing those with conventional Convolutional Neural Networks (CNNs) of same parameter count and depth, as well as against state-of-the-art SNN implementations. Through experiments conducted on multiple small-scale CSNN models, we observed that CSNNs are able to achieve exceptional parameter efficiency and performance with the help of a fast sigmoid surrogate gradient descent, proving an astonishing balance between accuracy and compactness. The findings fulfill the promise that CSNNs hold as a feasible solution for resource-constrained and low-power applications at the same time, as it demonstrates the biological plausibility of learning in such complex machine learning tasks.

关键词： Training Image recognition Biological system modeling Neurons Spiking neural networks Machine learning computer architecture Energy efficiency Convolutional neural networks Image classification

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Channel-wise attention based binarization for CNN Architectures

Channel-wise attention based binarization for CNN Architectu...

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Emerging Systems and Intelligent Computing (ESIC), International Conference on

作者： Ipsita Paul Mainak Bandyopadhyay Satya Ranjan Dash Tanjim Taharat Aurpa School of Computer Engineering KIIT D.U. Bhubaneswar India School of Computer Applications KIIT D.U. Bhubaneswar India Department of Data Science Engineering BSMR Digital University Bangladesh

ISBN: (数字)9798331522100

ISBN: (纸本)9798331522117

Attention mechanisms are known to effectively increase the performance of a model by focusing on specific sections of the input while executing a task. Squeeze and Excitation module (SE-Net) and Efficient channel attention module (ECA-Net) are well-known Channel Attention networks that only involve a few parameters to achieve a clear performance gain. In order to further improve the model’s accuracy, Binarized Neural Networks (BNNs) can be introduced in the same. BNNs are deep learning models that use binarized values for activation functions and training weights, instead of fully precised values that leads to much faster computation, occupying less memory and power. Motivated by this, a method of binarization of the convolutional network is proposed in this paper that can enhance the overall model accuracy. In our work, we focused on evaluating the performance of our binarized attention module over the pre-existing ones. Thus, the objective of this work is to design, implement, and evaluate efficient neural network models that balance accuracy and computational efficiency for image classification tasks on datasets with differing complexity levels, such as CIFAR-10 and CIFAR-100. By exploring various backbone architectures (e.g., MobileNetV2 and ResNet50), attention mechanisms (e.g., ECANet and SENet), and binarization techniques, this study aims to understand the impact of these components on model performance and resource requirements. Ultimately, the goal is to develop optimized models that achieve high accuracy with minimal computational overhead, making them suitable for deployment in resource-constrained environments.

关键词： Deep learning Training Attention mechanisms Accuracy Computational modeling Neural networks Transfer learning Convolutional neural networks Image classification Residual neural networks

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GCAT: Gated Convolutional Attention Transformer for Efficient Image Super-Resolution

GCAT: Gated Convolutional Attention Transformer for Efficien...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhijian Wu Kaiyi Feng Dingjiang Huang School of Data Science and Engineering East China Normal University Shanghai China School of Control and Computer Engineering North China Electric Power University Hebei China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Recently, Transformer-based methods have achieved impressive performance in many computer vision tasks (e.g., image super-resolution (SR)) due to the advantages of long-range modeling. However, the computational cost requirement renders these methods unsuitable on resource-constrain devices, especially for image SR tasks involving high-resolution images. In this paper, we propose a concise and effective Gated Convolutional Attention Unit (GCAU) that uses cheap convolutional operations. Specifically, GCAU consists of Convolutional Transposed Attention (CTA) and Locally-enhanced Gating (LeG) in parallel. The former allows for efficient modeling of the global relational interactions by calculating cross-covariance across channels dimension, while the latter controls the information flow from the former directing the network to focus on more refined image attributes. Without bells and whistles, we present a simple SR Transformer GCAT by cascading the GCAUs. Extensive experimental results demonstrate that our GCAT achieves state-of-the-art performance among the existing efficient SR methods with significantly less complexity. Especially, GCAT is on average 5× faster than SwinIR-light with comparable performance.

关键词： Legged locomotion Performance evaluation computer vision Convolution Computational modeling Superresolution Logic gates Transformers Speech processing Signal resolution

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A Secure and Efficient Privacy data Aggregation Mechanism 18th

A Secure and Efficient Privacy Data Aggregation Mechanism

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18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Ma, Wenshuo Liu, Xinru Yu, Kan Luo, Chuanwen Wang, Guopeng Liu, Xiaowu School of Artificial Intelligence and Big Data Zibo Vocational Institute Zibo China Weifang Engineering Vocational College Weifang China School of Computer Science and Engineering Macau University of Science and Technology Taipa China The Key Laboratory of Universal Wireless Communications Ministry of Education Beijing University of Posts and Telecommunications Beijing China School of Information Science and Technology Beijing Forestry University Beijing China School of Computer Science Qufu Normal University Rizhao China Zhejiang Ocean University Zhoushan China The Open University of China Beijing China

ISBN: (纸本)9783031714665

data aggregation (DA) plays an important role in conserving the limited resources and extending the network lifetime of Wireless Sensor Networks (WSNs). Privacy preservation has garnered considerable attention and is considered one of the most viable schemes to address issues such as communication eavesdropping, information leakage, and unauthorized access in WSNs employing DA. In this paper, we propose an improved Secure Privacy-preserving data Aggregation (iSECPDA) mechanism. It dynamically selects cluster heads through an enhanced Stable Election Protocol (SEP), which considers the residual energy of sensor nodes to enhance network lifetime. In our proposed SEPPDA, we integrate interference information into the sensing data and ensure data transmission privacy through a proposed slicing mechanism. Theoretical analysis and simulation experiments demonstrate that iSECPDA exhibits significant performance improvements in terms of communication overhead and privacy preservation levels. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： data aggregation

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