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1st IEEE International Conference on Emerging Research in Computational science, ICERCS 2023

作者： Dinesh Babu, V. Geetha, S.K. Aneeshkumar, M. Revathi, S. Arulprakash Arulprakash, P. Karpagam Institute of Technology Department of Information Technology Coimbatore India Department of Computer Science and Engineering Coimbatore India Arjun Collge of Technology Department of Artificial Intelligence and Data Science Thamaraikulam India Erode Sengunthar Engineering College Department of Computer Science and Engineering Erode India Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350359756

The Digital Twin (DT) is primarily used to digitally represent a physical object, process, or service. Simply put, DT is a clone representation of a physical object in the real world, such as wind farms, objects like a building or even an entire city. The key uses of technology are to collaborate, train, and simulate processes or products. The results of DT vary greatly depending on the sector and application, but a few key advantages include reducing costs and risks before investing in real-world resources and helping to enhance products or processes over their entire life cycles. The integration of the digital Twin with so many various industries, including manufacturing, healthcare, and the growth of urban and rural areas, has enhanced the prominence of this emerging technology. This paper's major goal is to identify ongoing initiatives by numerous engineering fields to apply digital twin technology for sustainable development. © 2023 IEEE.

关键词： Blockchain

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Seer: Accelerating Block chain Transaction Execution by Fine-Grained Branch Prediction 51st

Seer: Accelerating Block chain Transaction Execution by Fine...

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51st International Conference on Very Large data Bases, VLDB 2025

作者： Zhang, Shijie Cheng, Ru Liu, Xinpeng Xiao, Jiang Jin, Hai Li, Bo National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong

Increasingly popular decentralized applications (dApps) with complex application logic incur significant overhead for executing smart contract transactions, which greatly limits public block chain performance. Pre-executing transactions off the critical path can mitigate substantial I/O and computation costs during execution. However, pre-execution does not yield any state transitions, rendering the system state inconsistent with actual execution. This inconsistency can lead to deviations in pre-execution paths when processing smart contracts with multiple state-related branches, thus diminishing pre-execution effectiveness. In this paper, we develop Seer, a novel public block chain execution engine that incorporates fine-grained branch prediction to fully exploit pre-execution effectiveness. Seerpredicts state-related branches using a two-level prediction approach, reducing inconsistent execution paths more efficiently than executing all possible branches. To enable effective reuse of pre execution results, Seer employs checkpoint-based fast-path execution, enhancing transaction execution for both successful and unsuccessful predictions. Evaluations with realistic block chain workloads demonstrate that Seer delivers an average of 27.7× transaction-level speedup and an overall 20.6× speedup in the execution phase over vanilla Ethereum, outperforming existing block chain execution acceleration solutions. © 2025, VLDB Endowment, All rights reserved.

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Advancing Anomaly Detection in Time Series data: A Knowledge Distillation Approach with LSTM Model

Advancing Anomaly Detection in Time Series Data: A Knowledge...

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2023 Innovations in Intelligent Systems and Applications Conference, ASYU 2023

作者： Kilinc, Sena Camlidere, Bora Yildiz, Ekrem Guler, Ali Kerem Alsan, Huseyin Fuat Arsan, Taner Kadir Has University Department of Computer Engineering Istanbul Turkey Turknet Department of Data Science Istanbul Turkey

ISBN: (纸本)9798350306590

This paper focuses on enhancing anomaly detection in time series data using deep learning techniques. Particularly, it investigates the integration of knowledge distillation with LSTM-based models for improved precision, efficiency, and interpretability. The study outlines objectives such as dataset preprocessing, developing a novel LSTM-knowledge distillation framework, incorporating Grafana, InfluxDB, Flask API with Docker, performance assessment, and practical implications. Results highlight the efficacy of knowledge distillation in enhancing student model performance. The proposed approach enhances anomaly detection, offering a viable solution for real-world applications. © 2023 IEEE.

关键词： Distillation

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Secure and Low-Complexity Medical Image Exchange Based on Compressive Sensing and LSB Audio Steganography

Informatica (Slovenia)

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Informatica (Slovenia) 2023年第6期47卷 65-74页

作者： Al-Chaab, Wafaa Abduljabbar, Zaid Ameen Abood, Enas Wahab Nyangaresi, Vincent Omollo Mohammed, Hussein M. Ma, Junchao Department of Mathematics College of Science University of Basrah Basrah61004 Iraq Department of Computer Science College of Education for Pure Sciences University of Basrah Basrah61004 Iraq Technical Computer Engineering Department Al-Kunooze University College Basrah61001 Iraq Huazhong University of Science and Technology Shenzhen Institute Shenzhen430074 China Jaramogi Oginga Odinga University of Science & Technology Bondo40601 Kenya Department of Computer Science Shatt Al-Arab University College Basra61001 Iraq College of Big Data and Internet Shenzhen Technology University Shenzhen518118 China

Patients’ data constitutes the vast majority of information exchanged over the Internet. The magnitude of maintaining its security and privacy directly correlates to the importance and privacy of the patients themselves. The manipulation of this data negatively impacts the patient's life and treatment, thus the researchers were inclined to find solutions for this confidentiality challenge by concealing the data to prevent unauthorized access. The process of data concealment is more complex when dealing with large amounts of data. Often, the proportions of the medical image are sizable, which creates challenges when compressing data while maintaining high accuracy. In this paper, a system for securing and compressing medical images based on the Compressive Sensing (CS) principle is presented. The medical image is divided into 8×4 sub-matrices that are multiplied by a 3×8 sensor matrix consisting of Gaussian random numbers. The proposed solution reduces the image's original size by about 30% and conceals it as a random distribution inside an audio (wav) file for more security, using the LSB technique for low complexity. For reconstruction, the sensed image is multiplied by the pseudo-inverse of Moore-Penrose. The statistical analysis proved the efficacy of the system in compression and recovery with reduced cost and time consumption, combined with reduced distortion of the cover file;it was also judged to be increasingly efficient compared to previous research. © 2023 Slovene Society Informatika. All rights reserved.

关键词： Compressed sensing

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Optimization of Path Planning for Automated Vehicles Using SLAM Algorithm 1st

Optimization of Path Planning for Automated Vehicles Using S...

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1st International Conference on data Engineering and Machine Intelligence, ICDEMI 2023

作者： Prinslin, L. Selvi, R. Revathi, B. Yuvabharathi, E. Sharmila, L. Department of Computer Science and Engineering Agni College of Technology Chennai India Department of Artificial Intelligence and Data Science Dhanalakshmi College of Engineering Chennai India Department of Information Technology Jeppiaar Institute of Technology Chennai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (纸本)9789819776153

SLAM (simultaneous localization and mapping) is a technique for automated vehicles that enables simultaneous map building and vehicle localization. The vehicle is able to map out uncharted terrain owing to SLAM algorithms. Engineers use the map information to carry out tasks such as path planning and obstacle avoidance. An autonomous machine needs the ability to plan the motion of a high-level command. For example, if a task is given to explore a particular area, the bot has to plan accordingly to complete the required task. To achieve this level of accomplishment, the path works on an algorithm that solves SLAM and path planning problems. The machine is designed in such a way that it will work as a grass cutter will complete the task in the given frame. The grass-cutting machine creates its own path in the frame and detects obstacle coming in the path. In this way, the SLAM robot is utilized in the application of agricultural field. In the current work, we have designed a 3-D model of a SLAM robot and its various components like the LIDAR Camera, Jetson Nano, Lithium-Ion Battery, Motor, and Body design machine. This model is capable of performing grass-cutting operations using a cutting blade and collection of waste in a sack attached to the machine. This machine works on automation based on path planning algorithms using ROS, this eliminates human interference. Various kinds of research papers were reviewed to compare different kinds of mechanisms and various components were analyzed for the final model *** check and verify that the author names are correctly recognized and presented in the correct sequence order, which is [given name, middle name/initial, family name] for the author(s). In addition, please verify that the name(s) and respective affiliation(s) shown on the metadata page are valid and make any necessary amendments if ***, have edited the affiliation of the last author. © The Author(s), under exclusive license to Springer Nat

关键词： SLAM robotics

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From Efficiency to Innovation: LLM 5.0 and the Future of Industry

From Efficiency to Innovation: LLM 5.0 and the Future of Ind...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Gantayat, Pradosh Kumar Kaur, Tejinder Majhi, Madhusmita Jena, Uttam Kumar Das, Swatismita Soni Department of Artificial Intelligence and Data Science Telangana Hyderabad501203 India Department of Mmictbm Mullana Haryana Ambala133207 India Department of Computer Science and Engineering Telangana Hyderabad501203 India Raajdhani Engineering College Biju Patnaik University of Technology Department of Computer Science and Engineering Odisha Bhubaneswar India Raajdhani Engineering College Bhubaneswar India

ISBN: (纸本)9798331509828

Industry 5.0 is a new way of thinking that is consistent with the ideas of Industry 4.0 but places greater emphasis on sustainability, sustainability, and human-centricity. Unlike Industry 4.0, which emphasizes the efficient integration of advanced technologies such as the Internet, AI, robots, and automation, Industry 5.0 seeks to balance human-machine collaboration to create more inclusive, meaningful, and sustainable systems. In Industry 5.0, LLM has a bigger role than automating similar activities. LLMs make sectors more resilient, adaptable and aligned with social values by fostering innovation, improving human-centred processes and promoting sustainable practices. Industry 5.0 will create a revolutionary force in the global industrial landscape thanks to a combination of emerging technologies that will further enhance the synergies between human creativity and machine intelligence. © 2025 IEEE.

关键词： Human-AI Collaboration Industrial Efficiency Industry 5.0 Innovation LLM 5.0

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Robust EEG-based Emotion Recognition Using an Inception and Two-sided Perturbation Model 46

Robust EEG-based Emotion Recognition Using an Inception and ...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Sartipi, Shadi Cetin, Mujdat University of Rochester Department of Electrical and Computer Engineering Rochester United States University of Rochester Goergen Institute for Data Science Department of Electrical and Computer Engineering Rochester United States

ISBN: (纸本)9798350371499

Automated emotion recognition using electroencephalogram (EEG) signals has gained substantial attention. Although deep learning approaches exhibit strong performance, they often suffer from vulnerabilities to various perturbations, like environmental noise and adversarial attacks. In this paper, we propose an Inception feature generator and two-sided perturbation (INC-TSP) approach to enhance emotion recognition in brain-computer interfaces. INC-TSP integrates the Inception module for EEG data analysis and employs two-sided perturbation (TSP) as a defensive mechanism against input perturbations. TSP introduces worst-case perturbations to the model's weights and inputs, reinforcing the model's elasticity against adversarial attacks. The proposed approach addresses the challenge of maintaining accurate emotion recognition in the presence of input uncertainties. We validate INC-TSP in a subject-independent three-class emotion recognition scenario, demonstrating robust performance. © 2024 IEEE.

关键词： Emotion Recognition

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Opticurve: an optimized informer-curvelet framework for enhanced hyperspectral image segmentation and classification

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International Journal of Information Technology (Singapore) 2025年 1-16页

作者： Shaw, Kailash Wou Onn, Choo Li, Baihua Department of Computer Science and Engineering (AIML) Vishwakarma Institute of Technology Maharashtra Pune 411037 India Faculty of Data Science and Information Technology INTI International University Persiaran Perdana BBN Putra Nilai Negeri Sembilan Nilai 71800 Malaysia Department of Computer Science Loughborough University Loughborough LE11 3TU United Kingdom

Hyperspectral image (HSI) classification is crucial for applications in climate action, land use analysis, disaster risk reduction, and informed decision-making, given the complex spatial and spectral variations inherent in HSI data. Traditional methods struggle with accurately capturing these variations, necessitating more advanced techniques. This work introduces an Optimized Linformer-Curvelet (OptiCurve) Framework that integrates CNN-based feature extraction, Curvelet Transform for spatial detail capture, and Linformer for efficient feature representation. By combining these techniques, the model enhances HSI segmentation and classification, supporting improved outcomes in critical areas like environmental monitoring and disaster response. The framework is validated on four standard HSI datasets-Indian Pines, Pavia University, Kennedy Space Center, and Houston University-showing significant performance improvements over existing methods. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Climate action Disaster risk reduction Feature extraction Hyperspectral image classification Land use Linformer-Curvelet transform

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A Case Study on Context-Aware Neural Machine Translation with Multi-Task Learning 25

A Case Study on Context-Aware Neural Machine Translation wit...

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25th Annual Conference of the European Association for Machine Translation, EAMT 2024

作者： Appicharla, Ramakrishna Gain, Baban Pal, Santanu Ekbal, Asif Bhattacharyya, Pushpak Department of Computer Science and Engineering Indian Institute of Technology Patna India Wipro AI Lab45 London United Kingdom School of AI and Data Science Indian Institute of Technology Jodhpur India Department of Computer Science and Engineering Indian Institute of Technology Bombay India

ISBN: (纸本)9781068690709

In document-level neural machine translation (DocNMT), multi-encoder approaches are common in encoding context and source sentences. Recent studies (Li et al., 2020) have shown that the context encoder generates noise and makes the model robust to the choice of context. This paper further investigates this observation by explicitly modelling context encoding through multi-task learning (MTL) to make the model sensitive to the choice of context. We conduct experiments on cascade MTL architecture, which consists of one encoder and two decoders. Generation of the source from the context is considered an auxiliary task, and generation of the target from the source is the main task. We experimented with German-English language pairs on News, TED, and Europarl corpora. Evaluation results show that the proposed MTL approach performs better than concatenation-based and multi-encoder DocNMT models in low-resource settings and is sensitive to the choice of context. However, we observe that the MTL models are failing to generate the source from the context. These observations align with the previous studies, and this might suggest that the available document-level parallel corpora are not context-aware, and a robust sentence-level model can outperform the context-aware models. © 2024 The authors, © 2024 European Association for Machine Translation.

关键词： Neural machine translation

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IMF: Interactive Multimodal Fusion Model for Link Prediction 23

IMF: Interactive Multimodal Fusion Model for Link Prediction

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32nd ACM World Wide Web Conference, WWW 2023

作者： Li, Xinhang Zhao, Xiangyu Xu, Jiaxing Zhang, Yong Xing, Chunxiao Department of Computer Science and Technology Tsinghua University Beijing China School of Data Science City University of Hong Kong Hong Kong School of Computer Science and Engineering Nanyang Technological University Singapore

ISBN: (纸本)9781450394161

Link prediction aims to identify potential missing triples in knowledge graphs. To get better results, some recent studies have introduced multimodal information to link prediction. However, these methods utilize multimodal information separately and neglect the complicated interaction between different modalities. In this paper, we aim at better modeling the inter-modality information and thus introduce a novel Interactive Multimodal Fusion (IMF) model to integrate knowledge from different modalities. To this end, we propose a two-stage multimodal fusion framework to preserve modality-specific knowledge as well as take advantage of the complementarity between different modalities. Instead of directly projecting different modalities into a unified space, our multimodal fusion module limits the representations of different modalities independent while leverages bilinear pooling for fusion and incorporates contrastive learning as additional constraints. Furthermore, the decision fusion module delivers the learned weighted average over the predictions of all modalities to better incorporate the complementarity of different modalities. Our approach has been demonstrated to be effective through empirical evaluations on several real-world datasets. The implementation code is available online at https://***/HestiaSky/IMF-Pytorch. © 2023 Owner/Author.

关键词： Knowledge graph

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