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1st International Conference on Electronics, Communication and Signal Processing, ICECSP 2024

作者： Sen, Madhurima Batra, Ashita Das, Pradip K. Indian Institute of Technology Guwahati Department of Computer Science & Engineering Guwahati India

ISBN: (纸本)9798350364590

Stuttering is a speaking problem affecting normal fluency and flow of speech. Despite research done in this area, stuttering detection and identification is challenging in nature, due to the heterogeneity of audio samples which varies across people who stutter. This paper presents the application of extracting feature representations using pretrained deep learning models i.e. Wav2Vec2.0 (BASE & LARGE) and examines different data sampling methods for effective analysis. We have explored different sampling techniques i.e. oversampling and undersampling and evaluated their performance on traditional classifiers i.e. k-Nearest Neigh-bors (kNN), Support Vector Machines (SVM), Naïve Bayes' Classifier (NBC), Random Forest (RF) where SVM performed the best. With this, we performed a comprehensive layer-wise analysis of Wav2Vec2.0 feature embeddings and observed that middle layers capture more stuttering information. Our results show significant improvements in unweighted average recall (UAR) using the KSoF [1] and Sep-28k [2] datasets. Specifically, we observed a relative increase of 15.5% & 24.4% in UAR for the KSoF and Sep-28k dataset, relative to the results reported in [3] and [4] respectively. © 2024 IEEE.

关键词： Support vector machines

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SFI-Swin: symmetric face inpainting with swin transformer by distinctly learning face components distributions

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Multimedia Tools and Applications 2025年第17期84卷 17581-17595页

作者： Givkashi, MohammadHossein Naderi, MohammadReza Karimi, Nader Shirani, Shahram Samavi, Shadrokh Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan84156-83111 Iran Department of Electrical and Computer Engineering McMaster University HamiltonL8S 4L8 Canada Computer Science Department Seattle University Seattle98122 United States

Image inpainting consists of filling holes or missing parts of an image. Inpainting face images with symmetric characteristics is more challenging than inpainting a natural scene. None of the powerful existing models can fill out the missing parts of an image while considering the symmetry and homogeneity of the picture. Moreover, the metrics that assess a repaired face image quality cannot measure the preservation of symmetry between the rebuilt and existing parts of a face. In this paper, we intend to solve the symmetry problem in the face inpainting task by using multiple discriminators that check each face organ’s reality separately and a transformer-based network. We also propose "symmetry concentration score" as a new metric for measuring the symmetry of a repaired face image. The quantitative and qualitative results show the superiority of our proposed method compared to some of the recently proposed algorithms in terms of the reality, symmetry, and homogeneity of the inpainted parts. The code for the proposed method is available at https://***/mohammadrezanaderi4/SFI-Swin. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep learning

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Advancements in AI-Powered Robotic Cleaning Systems: Autonomous Path Planning, Predictive Maintenance, and Cleanliness Assessment Frameworks 3

Advancements in AI-Powered Robotic Cleaning Systems: Autonom...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Jones Sarah, A. Nandhakumar, D. Kumar, Atchitha Magesh Pavithra, S. Department of Computer Science and Engineering Chennai Institute of Technology Tamilnadu Chennai600069 India

ISBN: (纸本)9798331527549

Artificial Intelligence (AI) is transforming robotic cleaning systems to be more efficient, safe, and adapt in various environments, including public spaces and industrial facilities. The novelty of these systems is the use of advanced sensing and deep learning to identify areas that need cleaning, plan paths, and save resources all while meeting high cleanliness standards required in sensitive areas like hospitals and pharmaceutical facilities. The objective is how recent developments in path planning, dirt detection, and predictive maintenance improve these systems, which reduce costs and perform without human supervision. Though these systems work well, challenges remain in deciding on cleaning paths for complex spaces, correctly detecting various types of dirt, adapting to changing environments, and maintaining to prevent breakdowns. © 2025 IEEE.

关键词： Predictive maintenance

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Blockchain-Powered Financial Service Platform: Enabling P2P Payments, Crowdfunding and Loans 3

Blockchain-Powered Financial Service Platform: Enabling P2P ...

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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Sonawane, Swapnil S. Motwani, Dilip Vidyalankar Institute of Technology Department of Computer Engineering Mumbai India Vidyalankar Institute of Technology Department of Information Technology Mumbai India

ISBN: (纸本)9798350354348

This paper provides an in-depth analysis of a financial services platform that utilizes blockchain technology for peer-to-peer payments, crowdfunding, and loan services. By leveraging blockchain, the platform guarantees security, transparency, and efficiency in all transactions, offering a transformative alternative to traditional financial systems. It establishes a decentralized environment where users can interact and transact directly with one another, removing the need for intermediaries. Peer-to-peer payments allow for quick and effortless fund transfers between users, while crowdfunding supports the collection of capital for various projects or initiatives through numerous small contributions. Moreover, the platform includes loan services, enabling users to borrow funds in a secure and transparent manner, with smart contracts managing the loan process and ensuring compliance with repayment terms. The integration of blockchain technology on our platform aims to democratize financial services, enhance financial inclusion, and build trust within the financial system. © 2024 IEEE.

关键词： Decentralized finance

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An Experimental Analysis of Various Deep Learning Architectures for the Classification of Cognitive Stimuli based EEG Signals 15

An Experimental Analysis of Various Deep Learning Architectu...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Sarkar, Prashant Srinivasan Kanaga, E. Grace Mary Bhuvaneshwari, M. Mathew, Joel Stephen, Caleb Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798331300579

The human brain functions through electrical signals. By measuring these signals, one can monitor brain activity and gain insights into the brain function of the subject. An electroencephalogram (EEG) allows one to monitor brain activity by having the subject wear an array of sensors on their head. This process is frequently used to diagnose medical conditions such as epilepsy. In recent years, there have been efforts to use EEG signals in concert with deep learning to create a brain computer interface (BCI). Such a device would enable the wearer to communicate to a system via brain signals. While such a system would not be so advanced as to enable the translation of complex thoughts, it would enable a user to command a machine to perform a small number of functions. The objective of this paper was to develop and optimize recurrent neural network architectures for use with a brain computer interface. Using EEG data collected from subjects, a variety of neural network models were created to learn from the data. The models that were used were simple recurrent neural networks (RNN), long short-term memory (LSTM), and gated recurrent units (GRU). This paper proposes a novel approach to EEG signal classification, demonstrating the capabilities of recurrent networks which are seldom explored for this purpose. This study produced promising results for recurrent models, obtaining a 91% accuracy with the 4-layer LSTM architecture. This presents a solid foundation for the argument that LSTM and similar architectures are feasible for BCI applications. © Grenze Scientific Society, 2024.

关键词： Electroencephalography

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An ECC-Based Lightweight CPABE Scheme with Attribute Revocation 3rd

An ECC-Based Lightweight CPABE Scheme with Attribute Revoca...

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3rd International Conference on Machine Learning, Internet of Things and Big Data, ICMIB 2023

作者： Chandel, Avinash Debnath, Sourabh Kumar, Jitendra Mohapatra, Ramesh Kumar Department of Computer Science and Engineering National Institute of Technology Odisha Rourkela India

ISBN: (纸本)9789819939312

Data storage on cloud servers has become a common practice across businesses. Data security and accessibility in cloud environments are jeopardized when stored on unreliable cloud servers, making it necessary to convert user information into encrypted text that is difficult to understand and analyze, even if it is compromised. The novel encryption method known as Ciphertext Policy Attribute Based Encryption (CPABE) can be used to provide fine-grained access control and privacy in a cloud environment. To avoid the costly bilinear pairings, several researchers suggested CPABE approaches based on the elliptic curve cryptosystem (ECC). But there is a key-escrow concern with these systems. Hence, in this paper, we propose a multi-authority lightweight CPABE methodology. In addition, time and geographical attributes are also taken into consideration to enhance fine-grained access control. Our proposed solution also solves the issue related to dynamic attribute revocation by relegating repeated and tedious tasks to proxy servers. The detailed performance analysis demonstrates that the suggested method outperforms current schemes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Access control

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Rider Optimization Algorithm Based Optimal Cloud Server Selection in E-Learning

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computer Systems Science & engineering 2023年第2期44卷 1749-1762页

作者： R.Soundhara Raja Pandian C.Christopher Columbus Department of Computer Science and Engineering PSN College of Engineering and TechnologyMelathediyoorTamil Nadu627152India

Currently,e-learning is one of the most prevalent educational methods because of its need in today’s *** classrooms and web-based learning are becoming the new method of teaching *** students experience a lack of access to resources commonly the educational *** remote loca-tions,educational institutions face signiﬁcant challenges in accessing various web-based materials due to bandwidth and network infrastructure *** objective of this study is to demonstrate an optimization and queueing tech-nique for allocating optimal servers and slots for users to access cloud-based e-learning *** proposed method provides the optimization and queue-ing algorithm for multi-server and multi-city constraints and considers where to locate the best *** optimal server selection,the Rider Optimization Algo-rithm(ROA)is utilized.A performance analysis based on time,memory and delay was carried out for the proposed methodology in comparison with the exist-ing *** proposed Rider Optimization Algorithm is compared to Par-ticle Swarm Optimization(PSO),Genetic Algorithm(GA)and Fireﬂy Algorithm(FFA),the proposed method is more suitable and effective because the other three algorithms drop in local optima and are only suitable for small numbers of user *** the proposed method outweighs the conventional techniques by its enhanced performance over them.

关键词： Optimization queuing slot selection server selection rider optimization algorithm

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Enhancement in Loop Prevention Protocols: Dynamic Per-VLAN Multi-shortest Path Bridging (DV-MPB)

Enhancement in Loop Prevention Protocols: Dynamic Per-VLAN M...

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International Conference on Smart Systems and Wireless Communication, SSWC 2024

作者： Nagabhushanam, N. Abhishek Kumar, P. Pramod Chaitanya, D. Chandavarkar, B.R. Department of Computer Science and Engineering National Institute of Technology Karnataka Mangalore Surathkal India

ISBN: (纸本)9789819613472

Loops in a network pose significant challenges in modern communication systems, leading to congestion, performance degradation, and potential system failures. Additionally, loops can cause broadcast storms, impacting the network performance. While traditional loop prevention mechanisms like the Spanning Tree Protocol (STP) have been effective, the evolving complexity of network environments demands more efficient solutions. This paper provides a comprehensive review of loop prevention protocols, including STP, Rapid Spanning Tree Protocol (RSTP), Multiple Spanning Tree Protocol (MSTP), Shortest Path Bridging (SPB), and Per-VLAN Spanning Tree (PVST). Further, this paper introduces Dynamic VLAN - Multi-Shortest Path Bridging (DV-MPB) as a potential solution to enhance loop prevention efficiency. DV-MPB combines the strengths of SPB and PVST to achieve dynamic traffic optimization and reduce convergence time in loop prevention protocols. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： HTTP

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Efficient Collision-Free Motion Planning in Distributed Multi-Robot Systems 17

Efficient Collision-Free Motion Planning in Distributed Mult...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Sharma, Om Chakraborty, Nilotpal India Institute of Information Technology Department of Computer Science and Engineering Assam Guwahati India

ISBN: (纸本)9798331531195

In distributed multi-robot systems, ensuring collision-free motion planning is a complex challenge, especially in dynamic environments where multiple robots are operating simultaneously. Traditional path-planning algorithms are effective for individual robot navigation but struggle when dealing with the unpredictable movements of other robots in the system. To overcome these limitations, this research focuses on advanced motion planning techniques that ensure safe and efficient robot coordination. We model the overall motion planning problem into a two-dimensional grid and our objective is to minimize both the time and the energy taken by the robots to reach their destinations from their respective sources. We enhance standard path-finding algorithms such as A∗ search, along with Deep Reinforcement Learning (DRL) with suitable modifications to adapt to real-time changes. DRL allows robots to learn from their environment and make optimal decisions, improving their ability to avoid collisions and navigate efficiently in uncertain conditions. By integrating these algorithms and conducting a comparative analysis, we propose a framework that enables robots to dynamically adjust their paths, ensuring collision-free movement and optimal coordination. © 2025 IEEE.

关键词： Robot programming

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CNLP-NITS-PP at GenAI Detection Task 3: Cross-Domain Machine-Generated Text Detection Using DistilBERT Techniques 1

CNLP-NITS-PP at GenAI Detection Task 3: Cross-Domain Machine...

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1st Workshop on GenAI Content Detection, GenAIDetect 2025

作者： Teja, L.D.M.S. Sai Yadagiri, Annepaka Vardhan, M. Srikar Pakray, Partha Department of Computer Science & Engineering National Institute of Technology Assam Silchar788010 India

ISBN: (纸本)9798891762053

This paper presents a Cross-domain Machine-Generated Text Detection model developed for the COLING 2025 Workshop on Detecting AI-generated Content (DAIGenC). As large language models evolve, detecting machine-generated text becomes increasingly challenging, particularly in contexts like misinformation and academic integrity. While current detectors perform well on unseen data, they remain vulnerable to adversarial strategies, including paraphrasing, homoglyphs, misspellings, synonyms, whitespace manipulations, etc. We introduce a framework to address these adversarial tactics designed to bypass detection systems by adversarial training. Our team DistilBERT-NITS detector placed 7th in the Non-Adversarial Attacks category, and Adversarial-submission-3 achieved 17th in the Adversarial Attacks category. © 2025 International Conference on Computational Linguistics.

关键词： Adversarial machine learning

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