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12th International Conference on Internet of Everything, Microwave, Embedded, Communication and Networks, IEMECON 2024

作者： Natarajan, Krishnaraj Mathur, Abeer Bhargava, Kshitiz Singh, Manvendra Tejpal, Moulik Department of Database Systems School of Computer Science & Engineering Vellore Institute of Technology Vellore India Department of Software Systems School of Computer Science & Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350387315

Several newly developed techniques and tools for manipulating images, audio, and videos have been introduced as an outcome of the recent and rapid breakthroughs in AI, machine learning, and deep learning. While most applications for these techniques or tools are in the fields of entertainment and education, some individuals with unlawful intent have also been benefited from them. These individuals use such techniques for various purposes, including the spread of misleading information and unnecessary propaganda, the incitement of political instability, hate and unrest, as well as for purposes of torture and blackmail. These high-quality and convincing manipulated images, audio, or videos are commonly referred to as ‘Deepfakes’. Since then, various solutions to the problems raised by Deepfakes have been proposed in academic studies. This literature review contains relevant publications that offered a variety of approaches to give an updated summary of the research activities in different types of Deepfake attacks, their detection, and countermeasures. It also assesses the effectiveness of the detection capabilities of different techniques with various datasets and algorithms applied in Deepfake detection, while also outlining the various benefits and drawbacks of various methodologies. © 2024 IEEE.

关键词： Deep learning

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Inhibiting liquid permeation into silicone rubber by superhydrophobic coating for prolonged service life

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High Voltage 2023年第5期8卷 986-996页

作者： Shuming Liu Ying Zhou Zhou Zuo Shuqi Liu Qian Wang Zhong Chen Xidong Liang State Key Laboratory of Control and Simulation of Power System and Generation Equipment Department of Electrical EngineeringTsinghua UniversityBeijingChina School of Material Science and Engineering Nanyang Technological UniversitySingaporeSingapore

Liquid permeation into silicone rubber(SR)may lead to the degradation or complete failure of the composite *** to inhibit liquid permeation,two kinds of superhydrophobic coatings are applied to the SR *** liquid permeation properties of SR with different types of liquids are investigated,and the Langmuir diffusion model is applied to analyse the experimental *** results show that the composite superhydrophobic coating can effectively inhibit liquid permeation by reducing the contact area between the surface and liquid,and the stable structure of the composite coating can maintain the performance after the liquid permeation and ***,the nano‐particle coating without the polymer binder experiences severe performance degradation during the immersion test,and it cannot restrain the liquid permeation anymore after it loses the *** a validation of the effective protection,the insulation resistivity change was *** resistivity decreases sharply for the pristine SR after water permeation;however,the decrease is much smaller for the coated *** current study not only provides new insight into the liquid permeation properties of SR with different surface wettability but also suggests an effective measure to prevent liquid permeation into SR.

关键词： coating permeation liquid

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Machine Learning Approaches to Securing IoT Networks: A Comprehensive Review of Recent Advances 2

Machine Learning Approaches to Securing IoT Networks: A Comp...

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2nd IEEE International Conference on Advancements and Key Challenges in Green Energy and Computing, AKGEC 2024

作者： Saini, Harnit Prasad, Sanjeev Kumar Galgotias University School of Computer Science and Engineering Greater Noida India Ajay Kumar Garg Engineering College Computer Science and Engineering Department Ghaziabad India

ISBN: (纸本)9798350390179

The IoT is a network of physical objects embedded with sensors and software, connecting and exchanging data over the internet. With over 7 billion connected devices, experts predict growth to 10 billion by 2020 and 22 billion by 2025. Machine learning ML technologies are advancing quickly, with new algorithms and approaches emerging regularly. In this paper, a review is carried out that helps provide insights into their effectiveness in the current landscape. Different ML techniques are categorized and evaluated based on how they address diverse challenges. Reviewing current ML techniques helps in understanding their strengths and limitations in combating novel attack vectors. Later on, a comparative analysis of these techniques is conducted based on performance metrics. A comprehensive review identifies gaps in current research and practice, highlighting areas where ML techniques might need further development or improvement. © 2024 IEEE.

关键词： Adversarial machine learning

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CONTESTS: a Framework for Consistency Testing of Span Probabilities in Language Models

CONTESTS: a Framework for Consistency Testing of Span Probab...

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

作者： Wagner, Eitan Slavutsky, Yuli Abend, Omri School of Computer Science and Engineering Department of Statistics and Data Science Hebrew University of Jerusalem Israel

ISBN: (纸本)9798891761643

Although language model scores are often treated as probabilities, their reliability as probability estimators has mainly been studied through calibration, overlooking other aspects. In particular, it is unclear whether language models produce the same value for different ways of assigning joint probabilities to word spans. Our work introduces a novel framework, ConTestS (Consistency Testing over Spans), involving statistical tests to assess score consistency across interchangeable completion and conditioning orders. We conduct experiments on post-release real and synthetic data to eliminate training effects. Our findings reveal that both Masked Language Models (MLMs) and autoregressive models exhibit inconsistent predictions, with autoregressive models showing larger discrepancies. Larger MLMs tend to produce more consistent predictions, while autoregressive models show the opposite trend. Moreover, for both model types, prediction entropies offer insights into the true word span likelihood and therefore can aid in selecting optimal decoding strategies. The inconsistencies revealed by our analysis, as well their connection to prediction entropies and differences between model types, can serve as useful guides for future research on addressing these limitations. © 2024 Association for Computational Linguistics.

关键词： Prediction models

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***: Learning-based Governance for Decentralized Finance (DeFi)

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IEEE Transactions on Services Computing 2025年第03期18卷 1278-1292页

作者： Xu, Jiahua Feng, Yebo Perez, Daniel Livshits, Benjamin University College London United Kingdom Exponential Science Foundation United Kingdom Nanyang Technological University School of Computer Science and Engineering Singapore Imperial College London Computer Science Department United Kingdom Imperial College London United Kingdom

Decentralized finance (DeFi) is an integral component of the blockchain ecosystem, enabling a range of financial activities through smart-contract-based protocols. Traditional DeFi governance typically involves manual parameter adjustments by protocol teams or token holder votes, and is thus prone to human bias and financial risks, undermining the system's integrity and security. While existing efforts aim to establish more adaptive parameter adjustment schemes, there remains a need for a governance model that is both more efficient and resilient to significant market manipulations. In this paper, we introduce "***", a learning-based governance framework that employs a deep Qnetwork (DQN) reinforcement learning (RL) strategy to perform semi-automated, data-driven parameter adjustments. We create a DeFi environment with an encoded action-state space akin to the Aave lending protocol for simulation and testing purposes, where *** has demonstrated the capability to retain funds that would have otherwise been lost to price oracle attacks. In tests with real-world data, *** outperforms the benchmark approaches by at least 14% and the static baseline model by tenfold, in terms of the preset performance metric–protocol profitability. Overall, the comprehensive evaluations confirm that *** is more efficient and effective than traditional governance methods, thereby enhancing the security, profitability, and ultimately, the sustainability of DeFi protocols. © 2025 IEEE. All rights reserved.

关键词： Decentralized finance

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Biomedical signal processing for health monitoring applications: a review

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International Journal of Applied systemic Studies 2023年第1期10卷 44-69页

作者： Sonawani, Shilpa Patil, Kailas Natarajan, Prabhu School of Computer Engineering and Technology Dr. Vishwanath Karad MIT World Peace University India Department of Computer Engineering Vishwakarma University India Department of Computer Science DigiPen Institute of Technology Singapore

Biomedical health monitoring systems are evolving rapidly and using non-invasive and cost effective sensors. These systems can monitor physiological parameters of the body to monitor health conditions and provide feedback. These new generation systems can use advance technologies to build intelligent systems for health monitoring and timely diseases detection and diagnosis. Biomedical signal processing and analysis of the patterns in the signals plays important role in building efficient systems, life sciences and research and is rapidly expanding in this domain. This review paper is significant as no existing review paper is giving complete information on biomedical signal processing phases as a whole. It makes four contributions;first, it gives activity flow for developing biomedical signal processing systems. Second, reviews various recent applications researched and kinds of low cost, non-invasive sensors used for biomedical health monitoring. Third, categorisation and enlisting of signal processing techniques like signal sampling, segmentation, filtering, feature extraction, dimensionality reduction and machine learning techniques in healthcare domain. Fourth, it reviews challenges in using signal processing techniques and gives future perspectives which can be helpful for the researchers to use advance techniques to build intelligent systems for reliable decision making. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Cost effectiveness

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AI LipReader-Transcribing Speech from Lip Movements 6

AI LipReader-Transcribing Speech from Lip Movements

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6th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Ajitha, D. Dutta, Disha Saha, Falguni Giri, Parus Kant, Rohan School of Computer Science and Engineering Vellore Institute of Technology Vellore Department of Software Systems Vellore India School of Computer Science and Engineering Vellore Institute of Technology Department of Database Systems Vellore India School of Computer Science and Engineering Vellore Institute of Technology Vellore Department of Information Security Vellore India

ISBN: (纸本)9798350306613

Lip Reading AI is a discipline that is rapidly changing and has numerous applications in security, accessibility and human-computer interaction. This paper proposes a model which combines Convolutional Neural Networks (CNNs) to capture spatial capabilities, Long Short-Term Memory (LSTM) networks to examine temporal dependencies, and an adaptive interest mechanism. Meticulous preprocessing of the MIRACL VC-l dataset addressing challenges including one of a kind lip moves and occlusions accompanied with the aid of transitioning this study effortlessly to LRS2 dataset to complement lexemic versatility is one of its key function. The effects verify its robustness throughout unique datasets with superior overall performance towards cutting-edge techniques. Ablation checks suggest the crucial significance of every element in phrases of improving lip analyzing accuracy. Our proposed model version additionally suggests flexibility in restricted and naturalistic language situations. © 2024 IEEE.

关键词： Attention Mechanism Convolutional Neural Networks (CNNs) Deep Learning Lip Reading Long Short-Term Memory (LSTM) LRS2 Dataset MIRACLVC-J Dataset

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Computational efficiency in sports talent identification – a systematic review

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International Journal of Applied Decision sciences 2023年第3期16卷 358-384页

作者： Khan, Naveed Jeelani Ahamad, Gulfam Naseem, Mohd Sohail, Shahab Saquib Department of Computer Sciences Baba Ghulam Shah Badshah University Rajouri India Department of Computer Science and Engineering School of Engineering Sciences and Technology Jamia Hamdard New Delhi India

The selection of talent for sports has always been of great concern. The research interest in the domain of computational decision-making for sports talent identification is on an increasing curve. The conventional approaches are being modelled into the scientific models using various analytical and mathematical computational techniques. This paper reviews some of the talent identification models and aims to project the current perspective of the computational techniques being employed in sports talent identification (TiD). Articles from a timeframe of 1995–2020 were systematically selected in accordance with the PRISMA guidelines. We remain focused on the computational methodology being employed in the TiD models. The review delivers the findings and highlights some of the inherent issues that are not being addressed by the existing TiD models. © 2023 Inderscience Enterprises Ltd.. All rights reserved.

关键词： Sports

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A Federated Learning Approach Towards a Privacy-Preserving Technique for Brain Tumor Classification 2nd

A Federated Learning Approach Towards a Privacy-Preserving T...

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2nd International Conference on Data science and Network engineering, ICDSNE 2024

作者： De, Anurag Pal, Gautam Shyam, Karnam Tej, Kalakanti Pawan School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India Computer Science and Engineering Department TIT Narsingarh Tripura Agartala India

ISBN: (纸本)9789819783359

The brain is one of the main organs in the human body. It controls almost all the actions of a human being. Any problem with the brain can lead to even fatal consequences. One of the most dangerous problems faced by the human brain is a brain tumor. If it is not treated properly at early stages, it may lead to severe conditions. However, finding a brain tumor is a very challenging task as the brain structure is very complex. Also, a lot of image data is required to train the model to detect brain tumors. However, the hospitals are not ready to share patient data to train the model. Hence, an approach to train the model by seeing the actual dataset or simply using decentralized data from multiple clients needs to be followed. So, a federated learning methodology is utilized to train the model. The trained federated learning model has achieved a training accuracy of 91% and a test accuracy of 88%. The federated learning and transfer learning approach stands out in preserving privacy, optimizing model performance, and leading to good collaboration among healthcare entities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Improved AI-Generated Multitrack Music with Variational Autoencoders (VAE) for Harmonious Balance Compared to Recurrent Neural Network for Coherence 4

Improved AI-Generated Multitrack Music with Variational Auto...

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4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Naveen Kumar, R. Surendran, R. Sumathy, K. Saveetha Institution of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Saveetha Institution of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science Engineering Chennai India

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364729

This paper, therefore, proposes the application of the VAEs in enhancing the coherence and the alignment of the multiple track music AI-sync'd above. In our case, we want to take advantage of the feature of VAEs in modelling the complex data distribution. This will help create models that are indeed synthetic in the interconnectivity of the musical aspects. As for the evaluation, our method is designed for the assessment of the methodology of music generation, thus we compare the suggested approach with the baselines, namely, Variational Auto Encoders and Recurrent Neural Networks. The studies that have been discussed above help in a number of ways to AI music generation from the given VAE instrument through an understanding of a capacity to create aesthetically appealing and intricate music. The two high-level algorithmic methodologies were employed on 20 samples but from LPD dataset hence arriving at a total of 40 samples. The first priority used was CAO with reference to the structural integration and suitability of individual musical tracks. The findings that have been established in this paper reveal that a new model which has been developed known as VAE is a better model of synthesizing logical multitrack music compared to the RNN. © 2024 IEEE.

关键词： Recurrent neural networks

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