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Innovation and Novelty in engineering and Technology (INNOVA), International Conference on

作者： T. Kavitha Manikandan S P Bhimaraya Patil Anita Patil Dept. of Computer Science and Engineering New Horizon College of engineering Bangalore India School of Engineering & Technology(SoET) CMR University Bengaluru India Dept. of Computer Science Engineering New Horizon College of Engg Bangalore India Dept. of Information Science and Engg City Engineering College Bengaluru India

ISBN: (数字)9798331505134

ISBN: (纸本)9798331505141

This paper present advancements in distributed deep learning, focusing on federated learning, AutoML integration, and beyond. Leveraging the latest developments in machine learning (ML) and artificial intelligence (AI), our research explores novel approaches to enhance the efficiency, scalability, and security of distributed deep learning systems. This paper introduces federated learning techniques to enable collaborative model training across decentralized edge devices, thereby minimizing data privacy concerns and reducing communication over-head. Additionally, Proposed work investigate the integration of automated machine learning (AutoML) capabilities into distributed training pipelines, streamlining the model selection and hyperparameter tuning processes. Furthermore, Paper also explores cutting-edge advancements beyond traditional deep learning paradigms, such as generative adversarial networks (GANs) and reinforcement learning (RL), to unlock new opportunities in distributed AI. Through experimental evaluations and case studies, Paper demonstrate the effectiveness and potential impact of our proposed advancements, paving the way for future breakthroughs in distributed deep learning research.

关键词： Deep learning Training Technological innovation Federated learning Scalability Reinforcement learning Automated machine learning Generative adversarial networks Security Tuning

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RETHINKING MACHINE UNLEARNING FOR LARGE LANGUAGE MODELS

arXiv

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arXiv 2024年

作者： Liu, Sijia Yao, Yuanshun Jia, Jinghan Casper, Stephen Baracaldo, Nathalie Hase, Peter Yao, Yuguang Liu, Chris Yuhao Xu, Xiaojun Li, Hang Varshney, Kush R. Bansal, Mohit Koyejo, Sanmi Liu, Yang Computer Science & Engineering Dept. Michigan State University United States IBM Research United States ByteDance Research United States Computer Science and Artificial Intelligence Laboratory MIT United States Computer Science Dept. University of North Carolina Chapel Hill United States Computer Science & Engineering Dept. University of California Santa Cruz United States Computer Science Dept. Stanford University United States

We explore machine unlearning (MU) in the domain of large language models (LLMs), referred to as LLM unlearning. This initiative aims to eliminate undesirable data influence (e.g., sensitive or illegal information) and the associated model capabilities, while maintaining the integrity of essential knowledge generation and not affecting causally unrelated information. We envision LLM unlearning becoming a pivotal element in the life-cycle management of LLMs, potentially standing as an essential foundation for developing generative AI that is not only safe, secure, and trustworthy, but also resource-efficient without the need of full retraining. We navigate the unlearning landscape in LLMs from conceptual formulation, methodologies, metrics, and applications. In particular, we highlight the often-overlooked aspects of existing LLM unlearning research, e.g., unlearning scope, data-model interaction, and multifaceted efficacy assessment. We also draw connections between LLM unlearning and related areas such as model editing, influence functions, model explanation, adversarial training, and reinforcement learning. Furthermore, we outline an effective assessment framework for LLM unlearning and explore its applications in copyright and privacy safeguards and sociotechnical harm reduction. © 2024, CC BY.

关键词： Adversarial machine learning

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Surface traffic monitoring using OpenCV for various weather conditions through enhanced spatial correlation method

Surface traffic monitoring using OpenCV for various weather ...

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2022 International Mobile and Embedded Technology Conference, MECON 2022

作者： Ghosh, Dipak Bera, Soumokanti Datta, Pushan Kumar Amity University Dept. of Computer Science Engineering Kolkata India Amity University School of Engineering and Technology Kolkata India

ISBN: (纸本)9781665420204

It is a big challenge in the present urban traffic management system to develop a vehicle detection and traffic surveillance monitoring system through video capture using real time data capture model whereby machine learning may be used to recognise and categorise things. The idea is to analyse the motion and approach of cars acquired from CCTV cameras footage installed on the roads in different weather conditions and create an interactive software which should be able to gather and update important statistics like vehicle type classification and counts, lane usage details, for better regulation and control with ease. The core of this working model is to enhance vehicle detection and counting module which is implemented by analysing consecutive frames of video using frame differencing technique. The government has tried the best to ensure swift movement by implementing the broad usage of intelligent traffic controller system design. According to the WHO, around 2 million road traffic accidents result in deaths, with some nations devoting 2% of their GDP to road traffic accidents. Opportunities in the intelligent transport system market is growing at a global compound annual growth rate (CAGR) from 2019 to 2025 as an effective tool for road safety. © 2022 IEEE.

关键词： Information management

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IoT-Based Smart Control and Protection System for Home Appliances 25

IoT-Based Smart Control and Protection System for Home Appli...

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25th International Conference on computer and Information Technology, ICCIT 2022

作者： Ibne Joha, Md. Shafiul Islam, Md. Ahamed, Shamim Khulna University of Engineering & Technology Dept. of Electrical and Electronic Engineering Khulna9203 Bangladesh Khulna University of Engineering & Technology Dept. of Computer Science and Engineering Khulna9203 Bangladesh

ISBN: (纸本)9798350346022

Internet connectivity has become an essential aspect of the 21st century. Internet of Things (IoT) has embedded devices, including various sensors, software, and appliances. IoT-based intelligent home automation system offers the automated monitoring, operation, and control of home appliances via the internet. This paper presents an IoT-based, intelligent control and protection system for home appliances using NodeMCU and Blynk app. System control and operation via the app are independent of the Wi-Fi network's nature. It eliminates additional coding to connect the system to any Wi-Fi network rather than home Wi-Fi. Moreover, it automatically detects and protects home appliances from damage against overheating, overloading, gas leakage, and fire hazards. Furthermore, the dimmer circuit offers fan speed and light intensity control. The system also provides a time-scheduling option and voice command control via Google Assistant. Above all, it saves energy and enhances human comfort. © 2022 IEEE.

关键词： Domestic appliances

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Implementing Secure Key Management and Distribution in Cloud Environment 2

Implementing Secure Key Management and Distribution in Cloud...

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2nd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2023

作者： Lamsal, Aman Paul, Aniket Buchade, Amar Nalavade, Jagannath Bidwe, Ranjeet MIT Art Design and Technology University Dept. of Computer Science and Engineering Pune India Vishwakarma Institute of Information Technology Pune Savitribai Phule Pune University Pune India Department of Computer Engineering Pune India

ISBN: (纸本)9798350333763

Key management is considered as an essential aspect of securing cloud-based systems and data. As more organizations move their operations to cloud, the need for secure and efficient key management solutions becomes increasingly important. En-suring security of cloud computing is essential as many users often store their sensitive information with the cloud providers. Cloud service providers offer Key Management Services (KMS) to manage cryptographic keys for their customers, but there are still several challenges related to key management in cloud environments, including key storage, access control, and key rotation. Our research project aims to address these challenges by developing a secure and efficient key management solution for cloud-based systems. We propose a system that uses a combination of various cloud services and encryption algorithms to generate, store, and manage cryptographic keys securely. Our system provides granular access controls, and strong encryption to ensure the confidentiality and integrity of the keys. We believe that our proposed solution will help cloud service providers and organizations to manage their cryptographic keys more efficiently and securely, reducing the risk of unauthorized access and data breaches. This research paper provides a method to effectively manage, store and share the secret key between the parties using the concept of Threshold Cryptography. © 2023 IEEE.

关键词： Access control

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BERT-D2: Drug-Drug Interaction Extraction using BERT

BERT-D2: Drug-Drug Interaction Extraction using BERT

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2022 International Conference for Advancement in Technology, ICONAT 2022

作者： Datta, Tanmoy Tapos Shill, Pintu Chandra Al Nazi, Zabir Khulna University of Engineering Technology Dept. of Computer Science and Engineering Khulna Bangladesh Brainekt AI Lab Dhaka Bangladesh

ISBN: (纸本)9781665425773

When one drug interacts with another, it is known as a drug-drug interaction. This could change how one or both drugs work in the body, or induce unforeseen adverse effects. The performance of a combination can be effectively degraded or improved by mixing different medications. In some cases, it can adversely affect the patient's health. So, it's of great importance and time demanding to classify the interaction of drugs. Drug-drug interaction is amongst the top challenging and far-reaching applications of natural language processing. In this work, we have presented drug-drug interaction using the BERT (Bidirectional Encoder Representations from Transformers) model. The previous state of the art performance on the DDI Extraction 2013 corpus was using different variations of convolutional neural networks and LSTMs. In order to validate our proposed model, well-known benchmark data set are used and our BERT-based classification has achieved a much higher score than previous methods at 90.69% accuracy and 81.97% f1-score. © 2022 IEEE.

关键词： Extraction

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Blockchain-Enhanced Framework for Secure Third-Party Vendor Risk Management and Vigilant Security Controls

Blockchain-Enhanced Framework for Secure Third-Party Vendor ...

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2024 IEEE International Conference on Big data, Bigdata 2024

作者： Gupta, Deepti Elluri, Lavanya Jain, Avi Moni, Shafika Showkat Aslan, Omer Texas A&m University - Central Texas Dept. of Computer Information Systems TX United States College of Business University of Tampa FL United States Embry-Riddle Aeronautical University Dept. of Electrical Engineering and Computer Science FL United States Bandirma Onyedi Eylül University Department of Software Engineering Bandirma Turkey

ISBN: (纸本)9798350362480

In an era of heightened digital interconnectedness, businesses increasingly rely on third-party vendors to enhance their operational capabilities. However, this growing dependency introduces significant security risks, making it crucial to develop a robust framework to mitigate potential vulnerabilities. This paper proposes a comprehensive secure framework for managing third-party vendor risk, integrating blockchain technology to ensure transparency, traceability, and immutability in vendor assessments and interactions. By leveraging blockchain, the framework enhances the integrity of vendor security audits, ensuring that vendor assessments remain up-to-date and tamperproof. This proposed framework leverages smart contracts to reduce human error while ensuring real-time monitoring of compliance and security controls. By evaluating critical security controls - such as data encryption, access control mechanisms, multi-factor authentication, and zero-trust architecture - this approach strengthens an organization's defense against emerging cyber threats. Additionally, continuous monitoring enabled by blockchain ensures the immutability and transparency of vendor compliance processes. In this paper, a case study on iHealth's transition to AWS Cloud demonstrates the practical implementation of the framework, showing a significant reduction in vulnerabilities and marked improvement in incident response times. Through the adoption of this blockchain-enabled approach, organizations can mitigate vendor risks, streamline compliance, and enhance their overall security posture. Our findings highlight the importance of employing blockchain to enforce security controls and maintain compliance with healthcare regulations such as HIPAA. In this paper, we present a comprehensive set of security controls and demonstrate how blockchain technology enhances their effectiveness, ensuring greater transparency, accountability, and automation in vendor assessments. By reducing human error, e

关键词： Smart contract

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Communication-Avoiding Recursive Aggregation

Communication-Avoiding Recursive Aggregation

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IEEE International Conference on Cluster Computing

作者： Yihao Sun Sidharth Kumar Thomas Gilray Kristopher Micinski Dept. Electrical Engineering and Computer Science Syracuse University Syracuse USA Dept. Computer Science University of Illinois at Chicago Chicago USA Dept. Computer Science University of Alabama at Birmingham Birmingham USA Dept. of Electrical Engineering and Computer Science Syracuse University Syracuse USA

Recursive aggregation has been of considerable interest due to its unifying a wide range of deductive-analytic workloads, including social-media mining and graph analytics. For example, Single-Source Shortest Paths (SSSP), Connected Components (CC), and PageRank may all be expressed via recursive aggregates. Implementing recursive aggregation has posed a serious algorithmic challenge, with state-of-the-art work identifying sufficient conditions (e.g., pre-mappability) under which implementations may push aggregation within recursion, avoiding the serious materialization overhead inherent to traditional reachability-based methods (e.g., datalog).State-of-the-art implementations of engines supporting recursive aggregates focus on large unified machines, due to the challenges posed by mixing semi-naïve evaluation with distribution. In this work, we present an approach to implementing recursive aggregates on high-performance clusters which avoids the communication overhead inhibiting current-generation distributed systems to scale recursive aggregates to extremely high process counts. Our approach leverages the observation that aggregators form functional dependencies, allowing us to implement recursive aggregates via a high-parallel local aggregation to ensure maximal throughput. Additionally, we present a dynamic join planning mechanism, which customizes join order per-iteration based on dynamic relation sizes. We implemented our approach in PARALAGG, a library which allows the declarative implementation of queries which utilize recursive aggregates and executes them using our MPI-based runtime. We evaluate PARALAGG on a large unified node and leadership-class supercomputers, demonstrating scalability up to 16,384 processes.

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Did COVID-19 Pandemic Influence the Ether Cryptocurrency Market? Perception and Analysis

Did COVID-19 Pandemic Influence the Ether Cryptocurrency Mar...

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2022 International Conference on Breakthrough in Heuristics and Reciprocation of Advanced Technologies, BHARAT 2022

作者： Mithillesh Kumar, P. Jeyanthi, R. Deepa, K. Amrita Vishwa Vidyapeetham Amrita School of Engineering Dept. of Computer Science and Engg. Bengaluru India

ISBN: (数字)9781665436250

ISBN: (纸本)9781665436250

In recent days, DeFi tokens have gained popularity as an investment option in the pandemic period and has gained a significant amount of investment. Cryptocurrency trading is a type of DeFi that has gained a lot of attention in the global market. The value of such currencies is increasing on a daily basis and peaked during the pandemic. One of these significant cryptocurrencies is the ether cryptocurrency, which ranks second only to the bitcoin cryptocurrency in terms of the value of a single coin. The ARIMA model will be used to forecast the price of ether. In this paper, an hourly forecast and a short term period forecast are performed. The forecast clearly shows that the ARIMA model performed better on log transformed data than on the original data. It's also evident that COVID-19 pandemic has also aided the growth of ethereum when compared to previous year. © 2022 IEEE.

关键词： Ethers

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Towards Energy-Efficient Scheduling of UAV-Enabled Mobile Edge Computing Systems

Towards Energy-Efficient Scheduling of UAV-Enabled Mobile Ed...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Yuan, Haitao Wang, Meijia Bi, Jing Zhang, Jia School of Automation Science and Electrical Engineering Beihang University Beijing China Beijing University of Technology Faculty of Information Technology Beijing China Lyle School of Engineering Southern Methodist University Dept. of Computer Science DallasTX United States

ISBN: (纸本)9798350337020

Current mobile edge computing (MEC) owns cloud resources at the network edge, which enables low-latency mobile services. In addition to fixed MEC servers, MEC proxy servers with certain mobility and limited computing, e.g., flying unmanned aerial vehicles (UAVs), and vehicles, have emerged as competitors in providing services. In this work, aiming at a task offloading problem of a UAV-assisted MEC system, a hybrid network environment with multiple mobile devices (MDs) and multiple UAVs is established. A constrained mixed integer nonlinear program of the UAV-assisted hybrid cloud-edge system is formulated. A novel hybrid metaheuristic algorithm called Genetic Simulated annealing-based Particle Swarm Optimization (GSPSO) is presented to solve the program. Then, a task offloading and resource scheduling method is designed to intelligently minimize the total energy consumption of the hybrid system. Simulation results verify superiority of GSPSO over its three benchmark algorithms, thus demonstrating the proposed method significantly improves the energy efficiency of the UAV-enabled hybrid system. © 2023 IEEE.

关键词： computation offloading mobile edge computing particle swarm optimization simulated annealing UAVs

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