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A Solid Waste Management System Using Smart Bins in a Decentralized Manner in Ethereum Blockchain Network for Incentivization 2

A Solid Waste Management System Using Smart Bins in a Decent...

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2nd International Conference on Computational Systems and Communication, ICCSC 2023

作者： Amitha, A.K. Pamba, Raja Varma LBS Institute of Technology for Women Dept.of Computer Science and Engineering Thiruvananthapuram India

ISBN: (纸本)9781665493932

Waste generation in the world is raising in a huge way that the requirement to devise new solutions is crucial. The Solid Waste Management, SWM finds it difficult to handle manually these waste generated day by day, as a result of urbanization. With the advent of the technologies, it is possible to find some really good solutions for the waste segregation, waste collection, tracking, and documenting on the entire process. In this paper, we propose an automated solid waste management system by bringing the IoT and block chain technologies together. Here the data related to the bin is collected and is passed to the blockchain smart contracts that can automate incentives. The system enables waste segregation, which will ease the further procedures in SWM. Smart contract is deployed in the public blockchain Ethereum network platform and the automated reward based on the weight and type of the waste through an API is proposed in this paper. © 2023 IEEE.

关键词： Ethereum

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MAD: A Meta-Learning Approach to Detect Advanced Persistent Threats using Provenance Data in Industrial IoT 40

MAD: A Meta-Learning Approach to Detect Advanced Persistent ...

引用

40th Annual computer Security Applications Conference Workshops, ACSACW 2024

作者： Saha, Bikash Rani, Nanda Shukla, Sandeep Kumar Indian Institute of Technology Kanpur Dept. of Computer Science and Engineering Kanpur India

ISBN: (纸本)9798331532819

The increasing reliance on Industrial Internet of Things (IIoT) systems in critical infrastructure has made these environments prime targets for stealthy and prolonged Advanced Persistent Threats (APTs) that evade traditional security systems. This study introduces MAD (Meta-learning based APT Detection), a novel framework designed to detect APTs in IIoT environments by leveraging provenance data. This data provides a detailed view of system-level interactions, capturing subtle and persistent malicious behaviors. We evaluate MAD on the CICAPT-IIoT dataset, which simulates real-world APT scenarios with severe class imbalance due to the "low and slow"nature of APT attacks. MAD includes two variants: MAD v1, utilizing a multi-layer perceptron (MLP), and MAD v2, employing Model-Agnostic Meta-Learning (MAML) to improve detection of rare attack behaviors. MAD v1 achieves F1-score of 0.91 and outperforms the existing baselines. MAD v2 achieves F1-score of 0.95 surpassess existing baselines as well as MAD v1, which demonstrates its enhanced capability to detect rare APT activities despite the dominance of benign samples. This work combines provenance-based detection with meta-learning to address class imbalance and enhance APT detection in IIoT, demonstrating MAD's potential in tackling emerging IIoT security challenges. © 2024 IEEE.

关键词： Industrial internet of things (IIoT)

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Reconnaissance Detection in Decentralised IoT System using Model-Contrastive Federated Learning 18

Reconnaissance Detection in Decentralised IoT System using M...

引用

18th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2024

作者： Dutta, Rajdeep Kumar Chhetry, Bishal Matam, Rakesh Barbhuiya, Ferdous Ahmed Indian Institute of Information Technology Dept. of Computer Science and Engineering Guwahati India

ISBN: (纸本)9798350391725

The detection of reconnaissance attacks is crucial for safeguarding Internet of Things (IoT) environments, which are inherently more vulnerable and resource-constrained compared to traditional computing systems. Traditional centralised detection methods face significant challenges, such as privacy concerns and limited scalability due to the need to aggregate raw data on a central server. These issues become particularly pronounced in IoT environments where devices are diverse and geographically distributed. To address these challenges, we propose ReconGuard, a novel approach leveraging Model Contrastive Federated Learning (MCFL). ReconGuard enables collaborative training across multiple IoT devices without the necessity of sharing raw data, thereby preserving user privacy and enhancing data security. The approach integrates contrastive learning techniques, which improve the model's ability to discriminate between benign and malicious activities by contrasting positive (similar) and negative (dissimilar) data pairs. Our experimental results demonstrate that the ReconGuard-based detection system provides a scalable and privacy-preserving solution for identifying reconnaissance activities. These activities are often the initial step in more severe cyber threats, such as botnet attacks, which can lead to significant disruption and damage. By effectively detecting reconnaissance activities, ReconGuard enhances the overall cybersecurity framework of IoT environments. This research presents a viable and innovative method for improving cybersecurity in IoT systems, addressing the critical need for scalable, efficient, and privacy-preserving intrusion detection solutions. The use of MCFL in ReconGuard not only mitigates the challenges of data centralisation but also leverages the distributed nature of IoT networks to enhance robustness against cyber threats. © 2024 IEEE.

关键词： Contrastive Learning

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Improving Time Series Forecasting Accuracy with Transformers: A Comprehensive Analysis with Explainability 3

Improving Time Series Forecasting Accuracy with Transformers...

引用

3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Joseph, Susmera Jo, Ashly Ann Raj, Ebin Deni Indian Institute of Information Technology Dept. of Computer Science Engineering Kottayam India

ISBN: (纸本)9798350369083

Time series forecasting is crucial in numerous sectors, including healthcare, energy, and finance. Transformer models, initially designed for natural language processing applications, demonstrate potential in capturing temporal dependencies within sequential data. In this paper, the application of transformer models in time-series forecasting is explored, and their performance and interpretability are investigated. Transparent models build trust and facilitate decision-making, especially in industries with ethical guidelines. While inherently interpretable time-series Transformers exist, there is a gap in research regarding post hoc methods, which is an area ripe for exploration. Understanding and comparing complex Transformer models for time series forecasting can benefit from advancements in explainability, ensuring accurate and comprehensible predictions. In comparison to five other transformer-based models and classical models, the Temporal Fusion Transformer (TFT) model outperformed them in short-term forecasting. Further enhancement of the model performance was achieved through a meticulous analysis of the Encoder-Decoder's importance, which contributed significantly to refining the forecasting accuracy. The vanilla transformer was explained using permutation feature importance. The model stability was analyzed using perturbation methods. © 2024 IEEE.

关键词： Spatio-temporal data

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Advancements in Flood Mapping Methodologies: A Comprehensive Review 6

Advancements in Flood Mapping Methodologies: A Comprehensive...

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6th IEEE International Conference on Recent Advances in Intelligent Computational Systems, RAICS 2024

作者： Chandran, Divya V Anitha, J. Chandran, Divya V Vijayakumar, Amrutha Raveendran, Anjana Karunya Institute of Technology and Sciences Dept.of Computer Science Engineering Coimbatore India Adi Shankara Institute of Engineering and Technology Dept. of Electronics and Communication Engineering Kalady India

ISBN: (纸本)9798350381689

This review aims to comprehensively assess the recent advancements in flood mapping methodologies and technologies. By analyzing diverse techniques and approaches employed in flood mapping, this review seeks to present an in-depth overview of the current landscape of this field. The primary focus is on evaluating the strengths and limitations inherent in various flood mapping methods. Special attention will be given to exploring the integration of remote sensing technologies and geographic information systems (GIS) in flood mapping. Additionally, the review will delve into the transformative impact of machine learning and artificial intelligence on enhancing the precision and efficiency of flood mapping processes. Recent developments in flood mapping have significantly elevated the accuracy and reliability of the techniques employed. The integration of AI technologies with remote sensing methods has notably contributed to this advancement. Conventional approaches to flood mapping have traditionally relied on multiple methodologies to comprehend potential flood impacts. However, these methods often prove laborious, resource-intensive, and may lack access to real-time data, posing limitations for precise flood mapping and prediction. These identified limitations underscore the critical need for the development of more robust flood mapping techniques. Addressing these challenges is essential for enhancing flood risk assessment, improving management strategies, and ensuring better preparedness for future flood events. © 2024 IEEE.

关键词： Floods

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Deep Learning-Based Flood Detection System Using Semantic Segmentation 7

Deep Learning-Based Flood Detection System Using Semantic Se...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Chandran, Divya V Anitha, J. Anusree, A.R. Nair, Gowri Karunya Institute of Technology and Sciences Dept.of Computer Science Engineering Coimbatore India Adi Shankara Institute of Engineering and Technology Dept. Electronics and Communication Engineering Ernakulam India

ISBN: (纸本)9798350372816

Floods cause immense damage worldwide, resulting in significant financial losses and loss of life. Existing flood detection methods often face limitations, including delays and errors due to manual data interpretation and limited coverage. In this study, we propose a novel flood detection system that leverages deep learning techniques for semantic segmentation. Our system employs a U-Net architecture to analyze aerial and satellite images, identifying flood-affected areas at a pixel level. Trained on a diverse dataset of labeled flood images from various regions, our model is both robust and adaptable. Notably, our system achieves an Intersection over Union (IoU) score of 0.85, surpassing traditional methods in accuracy. This advanced system offers the potential to provide real-time flood information, enhancing disaster response and management. With real-time flood mapping, emergency services can better understand the disaster's scope and plan rescue operations more effectively. Improved emergency response and resource management become possible with timely, precise data on flooded areas, allowing for informed decisions regarding evacuation, relief distribution, and infrastructure maintenance. Future work will focus on integrating additional data sources, such as weather conditions and water flow rates, to further enhance system performance. © 2024 IEEE.

关键词： Semantic Segmentation

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Digital Twin-based Flight Dynamics Simulation

Digital Twin-based Flight Dynamics Simulation

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2024 IEEE Conference on engineering Informatics, ICEI 2024

作者： Talluri, Geetam Konjengbam, Anand Indian Institute of Technology Dharwad Dept of Electrical Electronics & Communication Engineering Dharwad India Indian Institute of Technology Dharwad Computer Science & Engineering Dharwad India

ISBN: (纸本)9798331505776

Unmanned Aerial Vehicle (UAV) systems face significant challenges during critical flight phases, such as post-liftoff stabilization and mid-flight disturbance response. This paper presents a Virtual Reality (VR) based simulation and control system integrated with a digital twin to enhance the understanding of UAV dynamics. The system uses real-time data from the UAV's Onboard computer (OBC) to create a dynamic 3D environment where users can interact with flight parameters such as position, rotational dynamics, and vibration amplitude. This approach enables the analysis of UAV performance and stability under changing conditions by allowing real-time adjustments. This immersive system supports research, training, and operational optimization of aerospace systems. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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WorkoutNet: A Deep Learning Model for the Recognition of Workout Actions from Still Images 3

WorkoutNet: A Deep Learning Model for the Recognition of Wor...

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3rd IEEE International Conference on Intelligent Technologies, CONIT 2023

作者： Dey, Arnab Dutta, Arpita Biswas, Samit Indian Institute of Engineering Science and Technology Dept. of Computer Science and Technology Howrah India

ISBN: (纸本)9798350338607

Workouts or Exercises are some repetitive physical activities essential for maintaining good health. Workouts make us relax and happy, give us more energy, lower the risk of chronic disease, and rejuvenate our bodies and minds. Proper technique and posture are essential to get the most out of any workout;the same workout may lead to injury if performed incorrectly. This paper presents a method to identify a person's workout action from realistic still images. In contrast to video-based action recognition, the challenge is more difficult for still photographs because the Spatio-temporal information is not present in images. This study proposes a deep learning-based model named WorkoutNet to recognize workout actions. The method achieved remarkable results on the Workout Action Image dataset, consisting of 2796 images prepared by us and on the publicly available Exercise image dataset. The suggested approach predicts different workout actions and classifies them into ten categories with high accuracy. The proposed WorkoutNet model attains 92.75% validation accuracy in classifying the workout action images, succeeded by the XceptionNet, VGG-19, DenseNet121, InceptionV3, MobileNet and VGG-16. Further, the classification performance has been evaluated using the F1-Measure and Confusion Matrix. The suggested model can be utilized in real-life to identify workout action images and assist humans doing workouts without a trainer or guide in understanding the right way. © 2023 IEEE.

关键词： Deep learning

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Augmented Reality based WayFinder System in Libraries 2

Augmented Reality based WayFinder System in Libraries

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2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023

作者： Aiswarya, R. Sreeram, V.K. Anchana Jinesh, K. Sneha, V.P. Menon, Hema P. Dept.of Computer Science and Engineering Sreepathy Institute of Mnagement and Technology Vavanoor Kerala Palakkad India

ISBN: (纸本)9798350340235

Advancement in technology can be utilized to ease workloads in many scenarios. In libraries, the search for a particular book may take a lot of time, and memorizing the position of each book is not an easy task and may result in reducing a person's interest in visiting the library. Usage of Augmented Reality, helps in visualizing the plots of the library. After combining with GPS, the system will be able to suggest the path to desired book in minimum required time. In libraries, there can be multiple paths to reach a particular book. The proposed system compares the multiple paths to find the best way with minimum distance to reach the desired book. The developed Augmented Reality wayfinding system revolutionizes the library experience by overlaying directional information or real environments through a smartphone camera. Scanning a provided QR code in the library helps the users to access the interface of the system and search for the required book. Innovation of such a system not only simplifies the search for books but also enables customers to check book availability and stock levels via a database. It also suggests alternatives, if the specific book is unavailable. By engaging such on-screen bots overall user experience can be enhanced and workload can be minimalized. © 2023 IEEE.

关键词： Augmented Reality (AR) Global Positioning System (GPS) Navigation Unity

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TL-CL: Task And Language Incremental Continual Learning

TL-CL: Task And Language Incremental Continual Learning

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

作者： Satapara, Shrey Srijith, P.K. Dept. of Artificial Intelligence Indian Institute of Technology Hyderabad Hyderabad India Dept. of Computer Science and Engineering Indian Institute of Technology Hyderabad Hyderabad India

ISBN: (纸本)9798891761643

This paper introduces and investigates the problem of Task and Language Incremental Continual Learning (TLCL), wherein a multilingual model is systematically updated to accommodate new tasks in previously learned languages or new languages for established tasks. This significant yet previously unexplored area holds substantial practical relevance as it mirrors the dynamic requirements of real-world applications. We benchmark a representative set of continual learning (CL) algorithms for TLCL. Furthermore, we propose Task and Language-Specific Adapters (TLSA), an adapter-based parameter-efficient fine-tuning strategy. TLSA facilitates cross-lingual and cross-task transfer and outperforms other parameter-efficient finetuning techniques. Crucially, TLSA reduces parameter growth stemming from saving adapters to linear complexity from polynomial complexity as it was with parameter isolation-based adapter tuning. We conducted experiments on several NLP tasks arising across several languages. We observed that TLSA outperforms all other parameter-efficient approaches without requiring access to historical data for replay. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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