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

作者： Sharma, Rishika Banda, Gourinath Indian Institute of Technology Department of Computer Science and Engineering Indore India

ISBN: (纸本)9798331531195

Effective management of semi-perishable food items is essential for minimising waste, maintaining food safety, and supporting sustainability efforts. This paper introduces the Responsibility Index for Sustainability and Efficiency (RISE), a set of modular components designed to seamlessly integrate with existing invoicing systems and enhance their performance by improving transparency, accountability, and waste management practices in retail stores. Semi-perishable food items, such as flour, bread, packaged meals, and beverages made from unprocessed grains, have a shorter shelf life than their raw counterparts, which can be stored for longer periods before processing. Ineffective management of these items often results in wastage and inflates the prices of raw materials due to imbalanced demand. Our solution holds retailers accountable for their role in demand creation and food waste by integrating a Responsibility Index Score, encouraging them to optimise their product offerings and reduce waste. These modules enhance consumption practices, supporting UN Sustainable Development Goals 2 (Zero Hunger) and 12 (Responsible Consumption and Production). They also help build consumer trust and reinforce retail stores' economic and social accountability. The modular approach ensures adaptability and ease of adoption across various invoicing platforms without requiring a complete system overhaul. © 2025 IEEE.

关键词： Sales

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Advancing Software-Defined Vehicles: An End-to-End Framework with Digital Twin Based Attestation for OTA Updates 17

Advancing Software-Defined Vehicles: An End-to-End Framework...

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

作者： Agrawal, Krish Rohan, Jha Luthra, Nishkarsh Sekhar, Pilla Venkata Sharma, Hrishesh Banda, Gourinath Indian Institute of Technology Department of Computer Science and Engineering Indore India

ISBN: (纸本)9798331531195

Software-defined vehicles (SDVs) are an emerging paradigm in the automotive industry where vehicles' functionality, performance, and safety can be enhanced and updated through software, even after production. Unlike traditional vehicles, which rely primarily on physical components, SDVs leverage advanced connectivity, real-time data analytics, and cloud integration to adapt to changing regulations, driver preferences, and environmental conditions. This shift enables vehicles to evolve continuously, responding to new technological advances and customer needs. In this paper, we propose an end-to-end framework to demonstrate and realize the full benefits of SDVs. We incorporate the concept of digital twin (of the vehicle) driven software update authorization to download or update an application on the vehicle. In our framework, attestation refers to the verification of each software update's compatibility and functionality with the vehicle's current ECUs before deployment. Any impending/requested update is first verified for its compatibility with the vehicle's architecture (as per the twin) on the cloud. The idea is via the SDVs' digital twin - since each and every ECU is virtualized - the simulation and testing of the actual hardware setup with the new application software can be done without direct impact on the physical vehicle. Once the application is installed across all relevant virtualized ECUs, the framework confirms compatibility, ensuring smooth deployment and functionality in the real-world vehicle. Post this successful attestation, the installation will be done on the real vehicle corresponding to that digital twin. Through this proposed framework, we intend to ensure the safety of the updation, while the new updates contribute to the functionality and performance improvements. © 2025 IEEE.

关键词： Automotive industry

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A Knowledge Discovery Framework for Maritime Intelligence on Dark Vessels 17

A Knowledge Discovery Framework for Maritime Intelligence on...

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

作者： Jayaswal, Bharat Ghosh, Soumya K. Indian Institute of Technology Department of Computer Science and Engineering Kharagpur India

ISBN: (纸本)9798331531195

Maritime Intelligence is essential for safeguarding maritime interests of coastal nations. Traditionally, vessel movements are tracked using self-reported positions via Automatic Identification System (AIS) transponders. However, vessels engaged in illicit activities often disable their AIS transponders to evade detection, thereby going dark. Satellite-based sensors, such as Synthetic Aperture Radar (SAR) and Visible Infrared Imaging Radiometer Suite (VIIRS) can be used to detect Dark Vessels. However, extraction of actionable insights for maritime intelligence on Dark Vessels is posed with several challenges. The spatial estimation of activities of vessels over various time periods is complicated due to spatial and temporal overlaps in satellite scans. This poster presents our ideas on knowledge discovery framework that integrates spatial analytics methods to gain actionable insights on Dark Vessels. Preliminary results are shown on Global Fishing Watch (GFW) Dataset of Dark Vessels. © 2025 IEEE.

关键词： Radar imaging

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mmWave Based User Identification using Gait Signatures 17

mmWave Based User Identification using Gait Signatures

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

作者： Das, Deepraj Mandal, Soumik Golande, Ashutosh Sen, Argha Chakraborty, Sandip Indian Institute of Technology Kharagpur Department of Computer Science and Engineering India

ISBN: (纸本)9798331531195

In this work, we present milliGait, a user identification using gait patterns captured by millimeter wave (mmWave) radar technology. milliGait takes into account the unique movement signatures of individuals to enable privacy secured accurate user identification. Using a Commercial-Off-the-self (COTS) mmWave radar we have collected mmWave features, particularly the range information and range-doppler profile features, extracted from the raw I/Q samples. To categorize people according to their leg movement patterns, we developed a Convolutional Neural Network (CNN) model using the mmWave features. The two features namely the range information and the range-doppler profile is fed into two concurrent CNN models (1D and 2D CNN models respectively). Finally a fully-connecyed classifier is employed to identify the subjects. We have evaluated milliGait with 3 different subjects with an authentication accuracy of over 80%. © 2025 IEEE.

关键词： Convolutional neural networks

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SlidEar: Exploring eSense in-Ear Wearable for Voice-Assisted Smart Slide Supervision 17

SlidEar: Exploring eSense in-Ear Wearable for Voice-Assisted...

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

作者： Mondal, Susmita Zingre, Sameeran Ravishankar Chakraborty, Suchetana Indian Institute of Technology Department of Computer Science and Engineering Jodhpur India

ISBN: (纸本)9798331531195

In corporate settings, conferences, or classrooms, an orator relies on manual slide transitions, which can disrupt their presentation flow. Presentation devices can be inaccessible due to the physical limitations of individuals, reducing engagement with the audience. Traditionally used remote-controlled wireless presenters are hand-held devices that usually require a compatible connector slot for the presentation device and partially engage one hand of the orator. To ease the hand-free presentation of slides, we introduce SlidEar, a voice-assisted smart slide supervision successfully tested on eSense in-ear wearables. SlidEar leverages voice recognition fusion with wearable sensors to facilitate seamless slide operations. It allows real-time feedback on natural language commands during the presentation of slides. Compared with conventional methods, SlidEar enhances engagement and accessibility through hand-free in-ear placement and helps the orator focus on delivering their content without any disruption or distraction. The evaluation shows promising results that improve the presentation experience profusion. SlidEar significantly reduces errors and can be widely adopted in diverse presentation scenarios. © 2025 IEEE.

关键词： Speech recognition

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OSINT at GenAI Detection Task 1: Multilingual MGT Detection: Leveraging Cross-Lingual Adaptation for Robust LLMs Text Identification 1

OSINT at GenAI Detection Task 1: Multilingual MGT Detection:...

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1st Workshop on GenAI Content Detection, GenAIDetect 2025

作者： Agrahari, Shifali Singh, Sanasam Ranbir Department of Computer Science and Engineering Indian Institute of Technology Guwahati India

ISBN: (纸本)9798891762053

Detecting AI-generated text has become increasingly prominent. This paper presents our solution for the DAIGenC Task 1 Subtask 2, where we address the challenge of distinguishing human-authored text from machine-generated content, especially in multilingual contexts. We introduce Multi-Task Detection (MLDet), a model that leverages Cross-Lingual Adaptation and Model Generalization strategies for Multilingual Machine-Generated Text (MGT) detection. By combining language-specific embeddings with fusion techniques, MLDet creates a unified, language-agnostic feature representation, enhancing its ability to generalize across diverse languages and models. Our approach demonstrates strong performance, achieving macro and micro F1 scores of 0.7067 and 0.7187, respectively, and ranking 15th in the competition1. We also evaluate our model across datasets generated by different distinct models in many languages, showcasing its robustness in multilingual and cross-model scenarios. © 2025 International Conference on Computational Linguistics.

关键词： Computational linguistics

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Uncertainty-Guided Metric Learning Without Labels

Uncertainty-Guided Metric Learning Without Labels

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2025 IEEE/CVF Winter Conference on Applications of computer Vision, WACV 2025

作者： Devalraju, Dhanunjaya Varma Sekhar, C Chandra Indian Institute of Technology Madras Department of Computer Science and Engineering India

ISBN: (纸本)9798331510831

Unsupervised metric learning aims to learn the discriminative representations by grouping similar examples in the absence of labels. Many unsupervised metric learning algorithms combine clustering-based pseudo-label generation with embedding fine-tuning. However, pseudo-labels can be unreliable and noisy. This could affect metric learning and degrade the quality of the learned representations. In this work, we propose an approach to reduce the negative effect of label noise on learning discriminative embeddings by using context and prediction uncertainty. In particular, we refine the pseudo-labels by aggregating information from neighbors. We propose a function to weigh the pairs, leveraging their prediction confidence and uncertainty. We modify the metric learning loss function to incorporate this weight. Experimental results demonstrate the effectiveness of our proposed method on standard datasets for metric learning. © 2025 IEEE.

关键词： Clustering algorithms

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Enhancing Predictive Capabilities for Cyber Physical Systems Through Supervised Learning

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第16期49卷 77-86页

作者： Dhanalakshmi, B. Tamije Selvy, P. Department of Computer Science and Engineering Dr.N.G. P Institute of technology India Department of Computer Science and Engineering Hindusthan College of Engineering and Technology India

The rapid advancement and proliferation of Cyber-Physical Systems (CPS) have led to an exponential increase in the volume of data generated continuously. Efficient classification of this streaming data is crucial for predicting system behaviors and enabling proactive decision-making. This research aims to extract actionable knowledge from the continuous data streams of CPS and predict their behavior using advanced supervised learning algorithms. The predictions facilitate timely interventions and necessary actions within the interconnected physical network. The background of this work lies in the intersection of CPS, machine learning, and data stream mining. Traditional batch processing methods are inadequate for real-time analysis of CPS data due to their inherent latency and computational inefficiency. This research employs state-of-the-art techniques for real-time data processing, including incremental learning, sliding window models, and ensemble methods tailored for streaming data. Our approach differs from existing works by focusing on a comprehensive framework that integrates real-time data ingestion, preprocessing, feature extraction, and model updating in a seamless pipeline. Unlike previous studies that often rely on static datasets and offline analysis, our method ensures continuous learning and adaptation to evolving data patterns. Comparative analysis with existing techniques demonstrates superior performance in terms of accuracy, latency, and scalability. Specifically, our models achieved an average classification accuracy of 92%, with a precision of 90%, recall of 89%, and an F1 score of 89.5%. These metrics indicate significant improvements over traditional batch processing methods, which typically lag in responsiveness and adaptability. This research provides a robust and efficient solution for the real-time classification of streaming data from CPS, enhancing the system's ability to predict behaviors and take necessary actions promptly. © 2025 Slov

关键词： Self-supervised learning

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Learning Non-cooperative Driving Practices and its Impact on Road Traffic Dynamics 17

Learning Non-cooperative Driving Practices and its Impact on...

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

作者： Osho Chakraborty, Suchetana Indian Institute of Technology Department of Computer Science and Engineering Jodhpur India

ISBN: (纸本)9798331531195

Cooperative driving that prioritizes global safety and efficiency over self-interest is crucial in ensuring smooth traffic flow and enhanced road safety. However, experiencing noncooperative driving is common under heterogeneous and chaotic on-road traffic conditions. While non-cooperativeness is often context-dependent and assessed during direct vehicle interactions, it can be inferred through longitudinal analysis of driving patterns from vehicular kinematics data. This behavior not only adversely affects the vehicle itself but also has a cascading impact on nearby vehicles, ultimately disrupting overall traffic flow. In this study, we quantify a vehicle's non-cooperativeness using kinematic data and further model its cascading effects on nearby cooperative vehicles. Experimental analysis was conducted using the SUMO traffic simulator and the TraCI interface to assess the impact of non-cooperativeness on overall traffic dynamics. This research is among the first to examine the cascading effects of a vehicle's non-cooperativeness on its cooperative nearby vehicles. © 2025 IEEE.

关键词： Motor transportation

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TruChain: A Blockchain-based Access Control to Improve the Security of Smart Water Grid Systems 17

TruChain: A Blockchain-based Access Control to Improve the S...

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

作者： Mondal, Susmita Chakraborty, Suchetana Indian Institute of Technology Department of Computer Science and Engineering Jodhpur India

ISBN: (纸本)9798331531195

Water is a significant resource in day-to-day life, and it usually requires technological association for comprehensive management. Smart Water Grids (SWG) typically use cyberphysical systems (CPS) to monitor several parameters associated with water quality and distribution, leveraging the possibility of performing remote operations. For robust architecture, remote operations rely on application-specific sensors, communication networks, edge control, and cloud computation. Needless to say, the centralized control of edge and cloud infrastructure is prone to attacks and failures, such as unauthorized access or single-point-of-failure. We propose TruChain, a Blockchain-based access control mechanism to eliminate the probability of access takeover and aggregate quorum-based validation on sensory data for transparent provenance reliability. We also provide access revocation to strengthen security by restricting access to sensitive data stored in a privately distributed cloud. We believe our solution has empirical decentralization and a verifiable authorization method by maintaining a tamper-evident, auditable trail of interactive events, building trust without third-party intervention. © 2025 IEEE.

关键词： Cloud security

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