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2025 IEEE International Students' Conference on Electrical, Electronics and computer sci.nce, SCEECS 2025

作者： Pimple, Jagdish Sharma, Avinash Madhyanchal Professional University Department of Computer Sci. & Engineering Bhopal India

ISBN: (纸本)9798331529833

Medical Cyber-Physical Systems (MCPS) are essential in modern healthcare, enabling real-time patient monitoring, diagnosis, and treatment. However, their integration of digital and physical elements exposes them to cyber-attacks, threatening both patient safety and data integrity. This paper shows a novel cybersecurity framework that combines blockchain technology and deep learning-based intrusion detection to protect MCPS. The proposed model uses a deep DNN-based intrusion detection system (IDS) to categorize medical data as either normal or malicious. Blockchain technology is incorporated to ensure secure, tamper-proof, and decentralized storage of patient information. To improve detection accuracy and reduce false positives, feature fusion techniques, processed through convolutional neural networks (CNNs), are applied. Ethereum-based blockchain ensures that medical data remains confidential and resistant to unauthorized alterations. The system's effectiveness is validated using a healthcare dataset, showing a marked improvement in detection accuracy, precision, and recall over traditional methods. This integrated solution strengthens the safety and reliability of MCPS, protecting patient data while maintaining efficient data shared between medical devices and healthcare networks. Future research will focus on real-time applications and scaling the model for larger healthcare systems. © 2025 IEEE.

关键词： Cybersecurity

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Enhanced Reversible Data Hiding in Encrypted Images by Pixel Shifting 16

Enhanced Reversible Data Hiding in Encrypted Images by Pixel...

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2024 16th International Conference on Graphics and Image Processing, ICGIP 2024

作者： Yang, Ching-Nung Lu, Yu-Quan Liu, Shu-Yu Xiong, Lizhi Dept. of Computer Sci. and Inf. Engineering National Dong Hwa University Hualien Taiwan School of Computer and Software Nanjing University of Inf. Sci. & Technology Nanjing China

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

Reversible data hiding (RDH) is one special type of data hiding, and is widely used for many intended applications. Moreover, to protect the cover image, RDH in encrypted image (RDHEI) is accordingly proposed. With the reversibility and hiding data in encrypted image, RDHEI has potential utility in fields such as military and medical image transmission, and forensic analysis. Recently, Panchikkil et al. propose a RDHEI by using pixel swapping (RDHE-PSW) to swap some pixels in a block. The pixel swapping does not change pixel values, and thus Panchikkil et al.’s RDHEI-PSW can retain entropy and histogram. The smoothness of a block is calculated to extract the hidden bits and meantime recover the cover image. Panchikkil et al.’s RDHEI-PSW only hides two secret bits in every block. Recently, Kim et al. use pixel shifting (PSH), a circular right shift on a block, instead of pixel swapping to propose a RDHEI-PSH. In this paper, we still adopt PSH approach but use it in a different way (a modified PSH) to propose an enhanced RDHEI-PSH with a higher embedding capacity than Kim et al.’s RDHEI-PSH. Our enhanced RDHEI-PSH double the patterns on a block, on which we can embed more secret bits. Experimental results demonstrate that our enhanced RDHEI-PSH surpasses Panchikkil et al.’s RDHEI-PSW and Kim et al.’s RDHEI-PSH. © 2025 SPIE.

关键词： Military photography

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Fake Review Detection using Random Forest Classifier

Fake Review Detection using Random Forest Classifier

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2025 IEEE International Students' Conference on Electrical, Electronics and computer sci.nce, SCEECS 2025

作者： Mane, Kishor Dongre, Snehlata Madankar, Mangala G H Raisoni College of Engineering Department of Computer Sci. & Engg. Nagpur India

ISBN: (纸本)9798331529833

In recent years, online review plays crucial role in the modern consumer experience to provide the valuable insights into the performance and quality of the products and services. Most of the consumers take a decision about purchase of online product based on the reviews. In the view of customer all the reviews are genuine. But nowadays many of the businesses manipulated the reviews of their products by inserting the fake reviews to misleads the consumer to buy wrong product. There is a need of the effective mechanism which can identify the fake reviews and increases the trustworthiness of the online reviews. Many researchers working on the different techniques available in different domains such as Machine learning, NLP, Opinion Mining, etc. In this paper the fake reviews are identified using the random forest classifier along with different challenges identified for improvement. The amazon reviews dataset has been tested for 90000 reviews to achieves accuracy up to 79.47%. © 2025 IEEE.

关键词： Adversarial machine learning

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Hybridization of DNA Computing and Chaotic System for Ensuring the Randomness and Integrity of Image Data 4

Hybridization of DNA Computing and Chaotic System for Ensuri...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Dhakal, Madhav Shakya, Subarna Tribhuvan University Central Department of Computer Sci. & Information Technology Kathmandu Nepal Pulchowk Campus Tribhuvan University Department of Electronics and Computer Engineering Institute of Engineering Kathmandu Nepal

ISBN: (纸本)9798331523923

In this paper, the concept of DNA computing and 3-D chaotic system is implemented to maintain the security of grayscale image while transmitting through the publicly available network. Here, random selection technique for sequencing of DNA bases with secret key generated through chaotic sequence encryption technique is purposed. Generation of permutation box for diffusion of image pixels carried out on the basis of chaotic system, which create more randomness and difficult to retrieve the secret key and maintain confidentiality from the intruder. System parameter and initial values of chaotic system work as the private key. In this proposed concept, first, plain image is divided in to various block of 512 bit size and padding is necessary if the last block does not contain the block of 512 sizes and then data obtained from the blocks are XOR with the key value obtained through chaotic system. After that, results obtained from this operation are transformed in to the cipher image with using the concept of Central Dogma of Molecular Biology. Experimental result was thoroughly examined using various techniques including histogram analysis, chi-square test, information entropy assessment, differential attack and the result shows that the proposed concept is secure and maintain the randomness, confidentiality and integrity of image data. © 2025 IEEE.

关键词： Gene encoding

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Enhancing Cybersecurity in Medical Cyber-Physical Systems Using Blockchain and Deep Learning

Enhancing Cybersecurity in Medical Cyber-Physical Systems Us...

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IEEE Students' Conference on Electrical, Electronics and computer sci.nce (SCEECS)

作者： Jagdish Pimple Avinash Sharma Department of Computer Sci. & Engineering Madhyanchal Professional University Bhopal

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

Medical Cyber-Physical Systems (MCPS) are essential in modern healthcare, enabling real-time patient monitoring, diagnosis, and treatment. However, their integration of digital and physical elements exposes them to cyber-attacks, threatening both patient safety and data integrity. This paper shows a novel cybersecurity framework that combines blockchain technology and deep learning-based intrusion detection to protect MCPS. The proposed model uses a deep DNN-based intrusion detection system (IDS) to categorize medical data as either normal or malicious. Blockchain technology is incorporated to ensure secure, tamper-proof, and decentralized storage of patient information. To improve detection accuracy and reduce false positives, feature fusion techniques, processed through convolutional neural networks (CNNs), are applied. Ethereum-based blockchain ensures that medical data remains confidential and resistant to unauthorized alterations. The system’s effectiveness is validated using a healthcare dataset, showing a marked improvement in detection accuracy, precision, and recall over traditional methods. This integrated solution strengthens the safety and reliability of MCPS, protecting patient data while maintaining efficient data shared between medical devices and healthcare networks. Future research will focus on real-time applications and scaling the model for larger healthcare systems.

关键词： Accuracy Intrusion detection Medical services Cyber-physical systems Feature extraction Real-time systems Blockchains Safety Convolutional neural networks Medical diagnostic imaging

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Leveraging Language-based Representations for Better Solving Symbol-related Problems with Large Language Models 31

Leveraging Language-based Representations for Better Solving...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Wang, Yile Cheng, Sijie Sun, Zixin Li, Peng Liu, Yang College of Computer Science and Software Engineering Shenzhen University Shenzhen China Dept. of Comp. Sci. & Tech. Institute for AI Tsinghua University Beijing China Tsinghua University Beijing China Jiangsu Collaborative Innovation Center for Language Competence Jiangsu China

ISBN: (纸本)9798891761964

Symbols such as numerical sequences, chemical formulas, and table delimiters exist widely, playing important roles in symbol-related tasks such as abstract reasoning, chemical property prediction, and tabular question-answering. Compared to tasks based on natural language expressions, large language models (LLMs) have limitations in understanding and reasoning on symbol-based representations, making it difficult for them to handle symbol-related problems. In this paper, we propose symbol-to-language (S2L), a method that converts symbol-based representations to language-based representations, providing valuable information for language models during reasoning. We found that, for both closed-source and open-source LLMs, the capability to solve symbol-related problems can be largely enhanced by incorporating such language-based representations. For example, by employing S2L for GPT-4, there can be substantial improvements of +21.9% and +9.5% accuracy for 1D-ARC and Dyck language tasks, respectively. There are also consistent improvements in other six general symbol-related tasks such as table understanding and Tweet analysis. We release the GPT logs in https://***/THUNLP-MT/symbol2language. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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FER-Former: Multimodal Transformer for Facial Expression Recognition

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IEEE Transactions on Multimedia 2025年 27卷 2412-2422页

作者： Li, Yande Wang, Mingjie Gong, Minglun Lu, Yonggang Liu, Li Lanzhou University School of Information Science and Engineering Lanzhou730000 China University of Guelph School of Computer Science GuelphONN1G 2W1 Canada University of Alberta Department of Electrical & Computer Engineering EdmontonABT6G 1H9 Canada Zhejiang Sci-Tech University School of Science Hangzhou310018 China Chongqing University School of Big Data and Software Engineering Chongqing401331 China

The ever-increasing demands for intuitive interactions in virtual reality have led to surging interests in facial expression recognition (FER). There are however several issues commonly seen in existing methods, including narrow receptive fields and homogenous supervisory signals. To address these issues, we propose in this paper a novel multimodal supervision-steering transformer for facial expression recognition in the wild, referred to as FER-former. Specifically, to address the limitation of narrow receptive fields, a hybrid feature extraction pipeline is designed by cascading both prevailing CNNs and transformers. To deal with the issue of homogenous supervisory signals, a heterogeneous domain-steering supervision module is proposed to incorporate text-space semantic correlations to enhance image features, based on the similarity between image and text features. Additionally, a FER-specific transformer encoder is introduced to characterize conventional one-hot label-focusing and CLIP-based text-oriented tokens in parallel for final classification. Based on the collaboration of multifarious token heads, global receptive fields with multimodal semantic cues are captured, delivering superb learning capability. Extensive experiments on popular benchmarks demonstrate the superiority of the proposed FER-former over the existing state-of-the-art methods. © 1999-2012 IEEE.

关键词： Semantics

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Fake Review Detection using Random Forest Classifier

Fake Review Detection using Random Forest Classifier

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IEEE Students' Conference on Electrical, Electronics and computer sci.nce (SCEECS)

作者： Kishor Mane Snehlata Dongre Mangala Madankar Department of Computer Sci. & Engg. G H Raisoni College of Engineering Nagpur India

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

关键词： Deep learning computer sci.nce Sentiment analysis Accuracy Machine learning algorithms Reviews Classification algorithms Electronic commerce Fake news Random forests

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Hybridization of DNA Computing and Chaotic System for Ensuring the Randomness and Integrity of Image Data

Hybridization of DNA Computing and Chaotic System for Ensuri...

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： Madhav Dhakal Subarna Shakya Central Department of Computer Sci. & Information Technology Tribhuvan University Kathmandu Nepal Department of Electronics and Computer Engineering Institute of Engineering Pulchowk Campus Tribhuvan University Kathmandu Nepal

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

关键词： Histograms Ciphers Sequential analysis Sentiment analysis Three-dimensional displays Gray-scale Encryption Molecular biology Standards DNA computing

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Perspective Transition of Large Language Models for Solving Subjective Tasks

arXiv

引用

arXiv 2025年

作者： Wang, Xiaolong Zhang, Yuanchi Wang, Ziyue Xu, Yuzhuang Luo, Fuwen Wang, Yile Li, Peng Liu, Yang Dept. of Comp. Sci. & Tech. Institute for AI Tsinghua University Beijing China Tsinghua University Beijing China Gansu China Harbin Institute of Technology Harbin China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

Large language models (LLMs) have revolutionized the field of natural language processing, enabling remarkable progress in various tasks. Different from objective tasks such as commonsense reasoning and arithmetic question-answering, the performance of LLMs on subjective tasks is still limited, where the perspective on the specific problem plays crucial roles for better interpreting the context and giving proper response. For example, in certain scenarios, LLMs may perform better when answering from an expert role perspective, potentially eliciting their relevant domain knowledge. In contrast, in some scenarios, LLMs may provide more accurate responses when answering from a third-person standpoint, enabling a more comprehensive understanding of the problem and potentially mitigating inherent biases. In this paper, we propose Reasoning through Perspective Transition (RPT), a method based on in-context learning that enables LLMs to dynamically select among direct, role, and third-person perspectives for the best way to solve corresponding subjective problem. Through extensive experiments on totally 12 subjective tasks by using both closed-source and open-source LLMs including GPT-4, GPT-3.5, Llama-3, and Qwen-2, our method outperforms widely used single fixed perspective based methods such as chain-of-thought prompting and expert prompting, highlights the intricate ways that LLMs can adapt their perspectives to provide nuanced and contextually appropriate responses for different problems. © 2025, CC BY.

关键词： Open systems

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