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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Pujari, Jambukeshwar Wasim, Javed Tripathi, Aprna Mangalayatan University Department of Computer Engineering and Applications Aligarh20221385 India Mangalayatan University Department of Computer Engineering and Applications Aligarh India Manipal University Department of Data Science Engineering Jaipur India

ISBN: (纸本)9798331512965

In recent years, Geographic Information Systems (GIS) have garnered a significant deal of interest for their ability to detect changes in metropolitan areas. One of the uses of change detection in satellite photographs is in the field of urban planning. Other applications include environmental monitoring and remote surveillance. A number of different approaches have been suggested in recent times for the purpose of automatically identifying alterations in the land cover based on GIS photographs. However, due to poor texture, cluttered context, and considerable object scale disparities, the convolutional neural network (CNN) method is only partially successful in capturing the context from geographic information systems (GISs). This is despite the fact that it remains an effective method for detecting change in urban environments. The article presents the construction of a CNN-based change detection model using the utilization of Dung Beetle Optimization (DBO). The DBO algorithm is used to adjust the loss parameter in order to increase the overall performance. The approach that was proposed makes use of the Onera satellite data set and displays an overall accuracy of 97.50% percent. In light of this, it appears that the strategy that is proposed is both very efficient and effective. © 2025 IEEE.

关键词： Convolutional neural networks

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An effective compressed image authentication scheme based on N-variant AMBTC

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Multimedia Tools and Applications 2024年第2期83卷 3801-3829页

作者： Su, Guo-Dong Chang, Chin-Chen Lin, Chia-Chen School of Big Data and Artificial Intelligence Fujian Polytechnic Normal University Fuzhou350300 China Department of Information Engineering and Computer Science Feng Chia University 100 Wenhwa Road Seatwen Taichung40724 Taiwan Department of Computer Science and Information Engineering National Chin-Yi University of Technology Taichung41170 Taiwan

Image authentication is one technique that provides integrity protection for digital images, making them sensitive to any slight modification. Recently, the detectability of absolute moment block truncation coding (AMBTC) compressed images has been attracting more attention due to its integrity authentication concerns. Thus, this paper proposes an effective N-variant AMBTC based image authentication scheme for AMBTC compressed images. First, a novel AMBTC compression code is derived using a variant AMBTC technique that can provide a better quality of compressed images. Then, each AMBTC compression code is further eliminated into a version requiring fewer bits using the proposed N-variant AMBTC technique, and those vacated N-bits are used to conceal authentication codes, which is generated by hashing the AMBTC compression code and location information. Experimental results confirm that the proposed scheme has superior stability and accuracy in tampering detection while providing better visual quality of the watermarked image. © 2023, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Authentication

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Explainable Artificial Intelligence for Education: A Real Case of a University Subject Switched to Python 1

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16th International Conference on Computational Intelligence in Security for Information Systems, CISIS 2023 and 14th International Conference on EUropean Transnational Education, ICEUTE 2023

作者： Melgar-García, Laura Troncoso-García, Ángela Gutiérrez-Avilés, David Torres, José Francisco Troncoso, Alicia Data Science & Big Data Lab Pablo de Olavide University Seville41013 Spain Department of Computer Science University of Seville Seville Spain

ISBN: (数字)9783031425196

ISBN: (纸本)9783031425189

Explainable artificial intelligence aims to describe an artificial intelligence model and its predictions. In this research work, this technique is applied to a subject of a computer science degree where the programming language changed from Octave to Python. Experiments are performed to analyze the explainability using the SHapley Additive exPlanations algorithm for XGBoost regressor model (for numerical grade prediction) and XGBoost classifier model (for class grade prediction). After the validation and training process, several conclusions are drawn that validate the idea of changing the programming language to a more popular one such as Python. For example, regarding classification problems, the most important feature for the insufficient class in the Octave courses is the practical exam. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Python

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Localization and Local Motion Magnification of Pulsatile Regions in Endoscopic Surgery Videos 1

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30th International Conference on MultiMedia Modeling, MMM 2024

作者： Zheng, Honglei Fan, Wenkang Chen, Yinran Luo, Xiongbiao Department of Computer Science and Technology Xiamen University Xiamen China National Institute for Data Science in Health and Medicine Xiamen University Xiamen China

ISBN: (数字)9783031533112

ISBN: (纸本)9783031533105

Localization of neurovascular bundles or vessels is critical in endoscopic surgery. It still remains challenging to identify neurovascular bundles and vessels due to limited field-of-view and tactile perception loss. This paper presents a new framework for local motion magnification of the pulsatile region in endoscopic surgical videos, which can help surgeons localize these pulsatile regions from complex surgical fields. Our method consists of an autocorrelated dynamic linear model, a pulsatile region localization module, and a local motion magnification module. Specifically, the autocorrelated dynamic linear model and pulsatile region localization module can extract periodic features and position information of pulsatile regions. Then, the local motion magnification module performs local motion magnification, minimizing post-magnification artifacts. Importantly, our method also avoids distorting the surgical instruments during magnification. Experimental validations on both synthetic and clinical surgical videos demonstrate that our method outperforms the existing methods in both qualitative and quantitative assessments. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Endoscopy

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Provably Secure Efficient Key-Exchange Protocol for Intelligent Supply Line Surveillance in Smart Grids

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IEEE Internet of Things Journal 2025年第12期12卷 21784-21792页

作者： Ayub, Muhammad Faizan Li, Xiong Mahmood, Khalid Alenazi, Mohammed J. F. Das, Ashok Kumar Wang, Guijuan University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh12311 Saudi Arabia International Institute of Information Technology Hyderabad Center for Security Theory and Algorithmic Research Hyderabad500032 India Korea University College of Informatics Department of Computer Science and Engineering Seoul02841 Korea Republic of Key Laboratory of Computing Power Network and Information Security Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Ministry of Education Shandong Computer Science Center Jinan250353 China

Intelligent supply line surveillance is critical for modern smart grids (SGs). Smart sensors and gateway nodes are strategically deployed along supply lines to achieve intelligent surveillance. They collect data continuously and transmit it to the control centre in real-time. It enables real-time monitoring, fault detection, and efficient energy management across distribution networks. This advanced surveillance system ensures continuous monitoring of supply lines, detecting anomalies, and optimizing operations to maintain the stability and reliability of the SG. However, the reliance of all participating nodes on public communication channels to transmit supply line surveillance data exposes these systems to critical cyber attacks. These cyber attacks include impersonation, physical tampering, ephemeral secret leakage (ESL), and desynchronization attacks. To address these issues, existing key-exchange protocols often fail to ensure robust security while imposing high computation and communication overheads, limiting their practicality for resource-constrained environments. Therefore, we propose a secure and efficient key exchange and tamper-resistant authentication protocol using elliptic curve cryptography (ECC) and physical unclonable functions (PUFs). The PUF mechanism provides robust resistance against physical tampering and cloning attacks, ensuring enhanced physical security for supply line devices. We validate the security robustness of the devised protocol using the random or real (ROR) model. Furthermore, informal security analysis demonstrates the protocol’s robustness in rigorously resisting various attacks, including physical tampering, impersonation, ESL and desynchronization. Moreover, we determine the performance evaluation that reveals the protocol’s superior efficiency compared to competing protocols, achieving significant reductions of 28.64% in computation overhead and 9.96% in communication overhead. © 2014 IEEE.

关键词： Authentication Protocol

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ABLE: Personalized Disability Support with Politeness and Empathy Integration

ABLE: Personalized Disability Support with Politeness and Em...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Mishra, Kshitij Burja, Manisha Ekbal, Asif Department of Computer Science and Engineering Indian Institute of Technology Patna India School of AI and Data Science Indian Institute of Technology Jodhpur India

ISBN: (纸本)9798891761643

In today's dynamic world, providing inclusive and personalized support for individuals with physical disabilities is imperative. With diverse needs and preferences, tailored assistance according to user personas is crucial. In this paper, we introduce ABLE (Adaptive, Bespoke, Listen and Empathetic), a Conversational Support System for Physical Disabilities. By tracking user personas, including gender, age, and personality traits based on the OCEAN model, ABLE ensures that support interactions are uniquely tailored to each user's characteristics and preferences. Moreover, integrating politeness and empathy levels in responses enhances user satisfaction and engagement, fostering a supportive and respectful environment. The development of ABLE involves compiling a comprehensive conversational dataset enriched with user profile annotations. Leveraging reinforcement learning techniques and diverse reward mechanisms, ABLE trains a model to generate responses aligned with individual user profiles while maintaining appropriate levels of politeness and empathy. Based on rigorous empirical analysis encompassing automatic and human evaluation metrics based on persona-consistency, politeness accuracy, empathy accuracy, perplexity, and conversation coherence, the efficacy of ABLE is assessed. Our findings underscore ABLE's success in delivering tailored support to individuals grappling with physical disabilities. To the best of our knowledge, this is the very first attempt towards building a user's persona-oriented physical disability support system. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Anatomization of Neural Networks based models for Semantic Analysis of Tabular dataset 9

Anatomization of Neural Networks based models for Semantic A...

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Bharath, B. Sai Bollineni, Jahnavi Mandala, Sandeep Preetham Ganguly, Tania Amudha, J. Shukla, Nikhil Joseph, Raj Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science And Engineering Bengaluru India Intellectyx Data Science Pvt Ltd India

ISBN: (纸本)9798350394474

The Paper focuses on analysing neural network models that are used for semantically classifying tabular customer datasets. Additionally, we propose a custom neural network architecture to analyze tabular datasets and enable the model to extract and comprehend the underlying semantics within the data. The research focuses on three distinct neural network models: CharCNN, Bi-LSTM, and CNN+Bi-LSTM. Through comprehensive evaluation based on accuracy, and confidence scores of the models' performance, we determined that Bi-LSTM proved to be the best fit for this approach and dataset. The findings suggest that the custom CHAR-CNN model can be effective in classifying tabular data and can potentially be applied on various datasets considering both computational time and accuracy. This research contributes to the advancement in the field of semantic analysis for tabular dataset, opening avenues for further research on how to handle different kinds of Tabular datasets and enhancement of semantic models in NLP. © 2024 IEEE.

关键词： Neural network models

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Towards property-preserving JPEG encryption with structured permutation and adaptive group differentiation

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Visual computer 2024年第9期40卷 6421-6447页

作者： Su, Guo-Dong Chang, Ching-Chun Lin, Chia-Chen Chang, Chin-Chen School of Big Data and Artificial Intelligence Fujian Polytechnic Normal University Fuzhou350300 China Department of Information Engineering and Computer Science Feng Chia University Taichung40724 Taiwan Fujian Province University Fuzhou China Department of Computer Science and Information Engineering National Chin-Yi University of Technology Taichung41170 Taiwan

The security and privacy of digital images are a major concern in cyberspace. JPEG is the most widely used image compression standard and yet there are problems with format compatibility and file size preservation in most of the modern encryption schemes for JPEG images. To address these problems, we propose a novel properties-preserving encryption scheme that obfuscates JPEG images by scrambling AC and DC coefficients. The key observation is that an innovative structured permutation has a remarkable effect on enciphering the coefficients and simultaneously prevents value overflow as well as size increment. On this basis, we first leverage the row-wise reordering and reversing technique to disorganize the DC coefficients. Afterward, a series of groups of consecutive DC differential values are produced using adaptive group differentiation and each of them is independently permuted inside. Finally, we perform block-wise ACCs permutation and intra-block ACCs shuffling to further confuse the outline of the original image. Moreover, invariant features regarding the coefficients are incorporated in the key generation process to resist various cryptanalytic attacks. Experimental results demonstrate a significant improvement in terms of format compatibility and file size preservation, and verify the security against various attacks. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023.

关键词： Cryptography

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Distributionally Robust Reinforcement Learning with Interactive data Collection: Fundamental Hardness and Near-Optimal Algorithms 38

Distributionally Robust Reinforcement Learning with Interact...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Lu, Miao Zhong, Han Zhang, Tong Blanchet, Jose Department of Management Science and Engineering Stanford University United States Center for Data Science Peking University China Department of Computer Science University of Illinois Urbana-Champaign United States

Distributionally robust reinforcement learning (DRRL), often framed as a robust Markov decision process (RMDP), seeks to find a robust policy that achieves good performance under the worst-case scenario among all environments within a prespecified uncertainty set centered around the training environment. Unlike previous work, which relies on a generative model or a pre-collected offline dataset enjoying good coverage of the deployment environment, we tackle robust RL via interactive data collection, where the learner interacts with the training environment only and refines the policy through trial and error. In this robust RL paradigm, two main challenges emerge: managing the distributional robustness while striking a balance between exploration and exploitation during data collection. Initially, we establish that sample-efficient learning without additional assumptions is unattainable owing to the curse of support shift;i.e., the potential disjointedness of the distributional supports between training and testing environments. To circumvent such a hardness result, we introduce the vanishing minimal value assumption to RMDPs with a total-variation distance robust set, postulating that the minimal value of the optimal robust value function is zero. Such an assumption effectively eliminates the support shift issue for RMDPs with a TV distance robust set, and we present an algorithm with a provable sample complexity guarantee. Our work makes the initial step to uncovering the inherent difficulty of robust RL via interactive data collection and sufficient conditions for sample-efficient algorithms with sharp sample complexity. © 2024 Neural information processing systems foundation. All rights reserved.

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ENERGY EFFICIENT SECURITY PROTOCOL FOR HEALTHCARE INTERNET OF THINGS

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Telecommunications and Radio Engineering (English translation of Elektrosvyaz and Radiotekhnika) 2024年第12期83卷 65-78页

作者： Babu, Ravi Mahesh Malluvalasa, S.N. Lakshmi Devi, M. Kalpana Karthik, Sagirala Satya Rakesh, M. Prasad, Satamraju Krishna 522508 India Department of Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Green Fields Andhra Pradesh Vaddeswaram522302 India Department of Electronics and Communication Engineering Department of Computer Science and Engineering Newton’s Institute of Engineering Guntur Andhra Pradesh Macherla India

The Energy Efficient Security for Internet of Things (EESP-IoT) protocol aims to achieve a harmonious equilibrium between energy preservation, security, and network optimization within the ever-changing domain of the internet of things (IoT). EESP-IoT coordinates the actions of IoT devices where each device independently optimizes its energy usage throughout the network to ensure stability and equity. Adaptive techniques such as power optimization, alternating between sleep and wake modes, and energy harvesting reduce energy inefficiency, while assuring security by using lightweight specifically designed cryptographic techniques. By including energy and security considerations, the protocol achieves both efficiency and resilience against a range of security threats. EESP-IoT exhibited exceptional energy conservation skills, while maintaining comparable levels of throughput and latency. The protocol underwent testing in a simulated environment to assess its ability to handle a large number of devices and its effectiveness in various network circumstances. The findings confirmed that EESP-IoT is capable of maintaining a high degree of network efficiency and strong security procedures, while considerably prolonging the lifespan of IoT devices. © 2024 by Begell House, Inc.

关键词： Energy utilization

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