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2023 International Conference on Sustainable computing and Smart Systems, ICSCSS 2023

作者： Padmanaban, K. Sathiya, A. Jeevitha, K. Rosaline, R. Anto Arockia Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vijayawada India Sri Sairam Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India R.M.K. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Computing Technologies Tamil Nadu Chennai India

ISBN: (纸本)9798350333602

The continuous advancement of computer software, hardware, and artificial intelligence, has allowed for tremendous progress in the development of human-machine communication systems in recent years. The development of such methods provides more accurate results than humans. The core of the human-computer interface is the Natural Language Understanding (NLU) component. The success of Human-Computer Interaction (HCI) and the time it takes to accomplish tasks are both directly affected by the results of this module's semantic comprehension. In this research study, a neural network and HCI-based engine is developed for understanding and interacting with natural languages. data from the appropriate literature is used to comprehend the necessary understanding of HCI. Next, the interactive engine is used to develop a model of HCI based on natural language comprehension using a neural network, and also the model's underlying neural network reasoning structure is assessed. © 2023 IEEE.

关键词： Engines

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Mobile-enabled Prosthetic System with Machine Learning Support

Mobile-enabled Prosthetic System with Machine Learning Suppo...

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2024 IEEE International Conference on E-Health Networking, Application and Services, HealthCom 2024

作者： Williams, Christian Lee, Andrew Tolah, Ahmed Einafshar, Mohammadjavad Massaad, Elie Kiapour, Ali Yu, Chen-Hsiang School of Computing and Data Science Wentworth Institute of Technology BostonMA United States Department of Biomedical Engineering Wentworth Institute of Technology BostonMA United States Department of Materials and Production Aalborg University Aalborg Denmark Massachusetts General Hospital Harvard Medical School BostonMA United States College of Engineering Northeastern University BostonMA United States

ISBN: (纸本)9798350350548

Machine learning (ML) and its applications have expanded over the past few years. ML inevitably makes its way to the world of prosthetics and amputees. Smart prosthetics have been studied and growing recently. Through the collection of data from these devices, results can help the user in numerous ways. On the other hand, mobile devices and applications are widely used. However, how to combine mobile applications and ML to enhance the prosthetic device was less addressed. In this research, we studied ML with an IoT-based prosthetic device paired with a mobile application. The hypothesis was that the ML methods could help evaluate the user’s status. The study results showed that some evaluated ML methods were able to see through the average temperature, humidity and contraction percentage of the people who wear a designed prosthetic device. The results also indicated that the users could tell if the contractions reached a concerning level. © 2024 IEEE.

关键词： Adversarial machine learning

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Multi-Task Learning with LLMs for Implicit Sentiment Analysis: data-level and Task-level Automatic Weight Learning

arXiv

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arXiv 2024年

作者： Lai, Wenna Xie, Haoran Xu, Guandong Li, Qing The Department of Computing Hong Kong Polytechnic University Hong Kong The School of Data Science Lingnan University Hong Kong The School of Computer Science The Data Science Institute University of Technology Sydney SydneyNSW2007 Australia The Education University of Hong Kong Hong Kong

Implicit sentiment analysis (ISA) presents significant challenges due to the absence of salient cue words. Previous methods have struggled with insufficient data and limited reasoning capabilities to infer underlying opinions. Integrating multi-task learning (MTL) with large language models (LLMs) offers the potential to enable models of varying sizes to reliably perceive and recognize genuine opinions in ISA. However, existing MTL approaches are constrained by two sources of uncertainty: data-level uncertainty, arising from hallucination problems in LLM-generated contextual information, and task-level uncertainty, stemming from the varying capacities of models to process contextual information. To handle these uncertainties, we introduce MT-ISA, a novel MTL framework that enhances ISA by leveraging the generation and reasoning capabilities of LLMs through automatic MTL. Specifically, MT-ISA constructs auxiliary tasks using generative LLMs to supplement sentiment elements and incorporates automatic MTL to fully exploit auxiliary data. We introduce data-level and task-level automatic weight learning (AWL), which dynamically identifies relationships and prioritizes more reliable data and critical tasks, enabling models of varying sizes to adaptively learn fine-grained weights based on their reasoning capabilities. We investigate three strategies for data-level AWL, while also introducing homoscedastic uncertainty for task-level AWL. Extensive experiments reveal that models of varying sizes achieve an optimal balance between primary prediction and auxiliary tasks in MT-ISA. This underscores the effectiveness and adaptability of our approach. © 2024, CC BY-NC-SA.

关键词： Multi-task learning

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Enhanced Long Short Term Memory for Early Alzheimer's Disease Prediction

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Intelligent Automation & Soft computing 2023年第2期35卷 1277-1293页

作者： M.Vinoth Kumar M.Prakash M.Naresh Kumar H.Abdul Shabeer Department of Information Science and Engineering Dayananda Sagar Academy of Technology&ManagementKanakapura Main Road560082BangaloreIndia Department of Data Science and Business Systems School of Computing SRM Institute of Science and TechnologyKattankulathurChengalpattu603203TamilnaduIndia Department of ECE Vardhaman College of EngineeringHyderabad501218India IBM India Pvt Ltd. DLF IT ParkChennai600125India

The most noteworthy neurodegenerative disorder nationwide is appar-ently the Alzheimer's disease(AD)which ha no proven viable treatment till date and despite the clinical trials showing the potential of preclinical therapy,a sen-sitive method for evaluating the AD has to be developed *** to the correla-tions between ocular and brain tissue,the eye(retinal blood vessels)has been investigated for predicting the ***,en enhanced method named Enhanced Long Short Term Memory(E-LSTM)has been proposed in this work which aims atﬁnding the severity of AD from ocular ***ﬁnd the level of disease severity,the new layer named precise layer was introduced in E-LSTM which will help the doctors to provide the apt treatments for the patients *** avoid the problem of overﬁtting,a dropout has been added to *** the existing work,boundary detection of retinal layers was found to be inaccurate during the seg-mentation process of Optical Coherence Tomography(OCT)image and to over-come this issue;Particle Swarm Optimization(PSO)has been *** the best of our understanding,this is theﬁrst paper to use Particle Swarm *** compared with the existing works,the proposed work is found to be per-forming better in terms of F1 Score,Precision,Recall,training loss,and segmen-tation accuracy and it is found that the prediction accuracy was increased to 10%higher than the existing systems.

关键词： Alzheimer's disease enhanced LSTM particle swarm optimization OCT image boundary detection

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AI-Enhanced Public Safety Lighting for Smart Sustainable Cities Using Cloud computing

AI-Enhanced Public Safety Lighting for Smart Sustainable Cit...

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

作者： Tidke, Bharat Pragadeeswaran, S. Lakshmi, R. Poornima Bhuvaneswari, G. Manikandan, G. Murugan, S. University Nagpur Campus Pune India School of Computing Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Chennai Institute of Technology Department of Computer Science and Engineering Chennai India Saveetha Engineering college Department of Computer Science and Engineering Thandalam Tamil Nadu Chennai India Rmk Engineering College Department of Artificial Intelligence and Data Science RSM Nagar Kavaraipettai Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331509828

This paper presents a novel method that combines cloud computing infrastructure and Reinforcement Learning (RL) algorithms to improve public safety lighting in smart cities. By dynamically adjusting lighting levels in response to real-time sensor inputs, the proposed system aims to maximize visibility while minimizing energy consumption. The cloud-based framework's RL agents continuously interact with the environment and human activity patterns to learn the best lighting policies. The system utilizes data from multiple sensors, including ambient light and motion detectors, to adjust real-time to daylight levels, weather, and pedestrian traffic variations. By integrating RL, the lighting system can adapt to changing urban dynamics and safety requirements over time, all while reducing energy waste. Compared to conventional static lighting systems, case studies and simulations show how the system can significantly improve visibility and response times while enhancing safety in various urban settings. RL algorithms can be easily deployed and managed throughout extensive smart city networks due to cloud computing network scalability and adaptability. This study advances sustainable urban infrastructure by showcasing the effectiveness of RL-based artificial intelligence (AI) solutions in public safety lighting optimization, thereby laying the groundwork for future smarter and safer cities. © 2025 IEEE.

关键词： Sustainable city

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A Survey of Integrating Generative Artificial Intelligence and 6G Mobile Services: Architectures, Solutions, Technologies and Outlooks

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IEEE Transactions on Cognitive Communications and Networking 2025年第3期11卷 1334-1356页

作者： Liu, Yi-Jing Du, Hongyang Xu, Xinyi Zhang, Ruichen Feng, Gang Cao, Bin Niyato, Dusit Kim, Dong In Jamalipour, Abbas Letaief, Khaled B. Tafazolli, Rahim University of Electronic Science and Technology of China National Key Lab on Wireless Communications Chengdu China University of Hong Kong Department of Electrical and Electronic Engineering Pok Fu Lam Hong Kong Nanyang Technological University College of Computing and Data Science Singapore Singapore Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing China Sungkyunkwan University Department of Electrical and Computer Engineering Suwon Korea Republic of University of Sydney School of Electrical and Computer Engineering Sydney Australia Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Hong Kong University of Surrey Institute for Communication Systems School of Computer Science and Electronic Engineering United Kingdom

Generative artificial intelligence (GenAI) is rapidly driving a new phase of artificial intelligence revolution, marked by various applications such as ChatGPT, Sora and DeepSeek. With powerful capabilities in content generation, personalization and user interaction, GenAI can drive significant enhancements in various mobile services to satisfy individual user preferences. Meanwhile, mobile services offer substantial support for accessibility, functionality and intelligence of GenAI applications. However, current research on integrating GenAI and mobile services remains nascent, lacking necessary theoretical and technical support. In this survey, we break down a new vision to guide research on integrating GenAI and mobile services, aiming to improve 6G intelligence levels and enable 6G mobile networks to support the integration of GenAI and mobile services effectively. We begin by examining the necessity of integrating GenAI with 6G mobile services and exploring potential learning architectures of GenAI models within mobile networks. Next, we analyze potential solutions from two perspectives: how 6G mobile networks empower GenAI and how GenAI enables mobile services. We subsequently explore a promising use case, along with essential features and techniques, to facilitate the integration of GenAI and mobile services. Finally, we discuss future research directions, aiming at realizing intelligent 6G networks. © 2015 IEEE.

关键词： Generative adversarial networks

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Evolutionary Neural Architecture Search for 3D Point Cloud Analysis

arXiv

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arXiv 2024年

作者： Yang, Yisheng Du, Guodong Toa, Chean Khim Tang, Ho-Kin Goh, Sim Kuan School of Computing and Data Science Xiamen University Malaysia Sepang Malaysia School of Science Harbin Institute of Technology Shenzhen China School of Electrical Engineering and Artificial Intelligence Xiamen University Malaysia

Neural architecture search (NAS) automates neural network design by using optimization algorithms to navigate architecture spaces, reducing the burden of manual architecture design. While NAS has achieved success, applying it to emerging domains, such as analyzing unstructured 3D point clouds, remains underexplored due to the data lying in non-Euclidean spaces, unlike images. This paper presents Success-History-based Self-adaptive Differential Evolution with a Joint Point Interaction Dimension Search (SHSADE-PIDS), an evolutionary NAS framework that encodes discrete deep neural network architectures to continuous spaces and performs searches in the continuous spaces for efficient point cloud neural architectures. Comprehensive experiments on challenging 3D segmentation and classification benchmarks demonstrate SHSADE-PIDS’s capabilities. It discovered highly efficient architectures with higher accuracy, significantly advancing prior NAS techniques. For segmentation on SemanticKITTI, SHSADE-PIDS attained 64.51% mean IoU using only 0.55M parameters and 4.5GMACs, reducing overhead by over 22-26X versus other top methods. For ModelNet40 classification, it achieved 93.4% accuracy with just 1.31M parameters, surpassing larger models. SHSADE-PIDS provided valuable insights into bridging evolutionary algorithms with neural architecture optimization, particularly for emerging frontiers like point cloud learning. © 2024, CC BY.

关键词： Cloud computing architecture

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Exact space-depth trade-offs in multicontrolled Toffoli decomposition

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Physical Review A 2025年第5期111卷 052611-052611页

作者： Suman Dutta Siyi Wang Anubhab Baksi Anupam Chattopadhyay Subhamoy Maitra College of Computing & Data Science Nanyang Technological University Singapore Applied Statistics Unit Indian Statistical Institute Kolkata West Bengal India School of Physical & Mathematical Sciences Nanyang Technological University Singapore

In this paper we consider the optimized implementation of multicontrolled Toffoli decomposition using the Clifford+T gate set. While there are several recent works in this direction, here we explicitly quantify the trade-off (with concrete formulas) between the Toffoli depth (this means the depth using the classical 2-controlled Toffoli gate) of the n-controlled Toffoli decomposition and the number of clean ancilla qubits. Additionally, we achieve a reduced Toffoli (consequently T) depth, which is an extension of the technique introduced by Khattar and Gidney [T. Khattar and C. Gidney, arXiv:2407.17966]. In terms of a negative result, we first show that by using such conditionally clean ancilla techniques, the Toffoli depth can never achieve exactly ⌈log2n⌉, though it remains of the same order. This highlights the limitation of the techniques exploiting conditionally clean ancillas [J. Nie et al., arXiv:2402.05053; T. Khattar and C. Gidney, arXiv:2407.17966]. Then we prove that, in a more general setup, the T depth in the Clifford+T decomposition, via Toffoli gates, is lower bounded by ⌈log2n⌉, and this bound is achieved following the complete binary tree structure. Since the (2-controlled) Toffoli gate can further be decomposed using Clifford+T gate set, various methodologies are also explored in this regard for trade-off-related implications.

关键词： Numerical methods

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Single Bridge Formation in Self-Organizing Particle Systems

arXiv

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arXiv 2024年

作者： Oh, Shunhao Briones, Joseph L. Calvert, Jacob Egan, Noah Randall, Dana Richa, Andréa W. School of Computer Science Georgia Institute of Technology AtlantaGA United States School of Computing and Augmented Intelligence Arizona State University TempeAZ United States Institute for Data Engineering and Science Georgia Institute of Technology United States

Local interactions of uncoordinated individuals produce the collective behaviors of many biological systems, inspiring much of the current research in programmable matter. A striking example is the spontaneous assembly of fire ants into "bridges" comprising their own bodies to traverse obstacles and reach sources of food. Experiments and simulations suggest that, remarkably, these ants always form one bridge-instead of multiple, competing bridges-despite a lack of central coordination. We argue that the reliable formation of a single bridge does not require sophistication on behalf of the individuals by provably reproducing this behavior in a self-organizing particle system. We show that the formation of a single bridge by the particles is a statistical inevitability of their preferences to move in a particular direction, such as toward a food source, and their preference for more neighbors. Two parameters, η and β, reflect the strengths of these preferences and determine the Gibbs stationary measure of the corresponding particle system's Markov chain dynamics. We show that a single bridge almost certainly forms when η and β are sufficiently large. Our proof introduces an auxiliary Markov chain, called an "occupancy chain," that captures only the significant, global changes to the system. Through the occupancy chain, we abstract away information about the motion of individual particles, but we gain a more direct means of analyzing their collective behavior. Such abstractions provide a promising new direction for understanding many other systems of programmable matter. Copyright © 2024, The Authors. All rights reserved.

关键词： Markov chains

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DOMAIN ADVERSARIAL TRAINING ON CONDITIONAL VARIATIONAL AUTO-ENCODER FOR CONTROLLABLE MUSIC GENERATION 23

DOMAIN ADVERSARIAL TRAINING ON CONDITIONAL VARIATIONAL AUTO-...

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23rd International Society for Music Information Retrieval Conference, ISMIR 2022

作者： Zhao, Jingwei Xia, Gus Wang, Ye School of Computing NUS Singapore Institute of Data Science NUS Singapore Music X Lab. NYU Shanghai China Integrative Sciences and Engineering Programme NUS Graduate School Singapore MBZUAI

ISBN: (纸本)9781732729926

The variational auto-encoder has become a leading framework for symbolic music generation, and a popular research direction is to study how to effectively control the generation process. A straightforward way is to control a model using different conditions during inference. However, in music practice, conditions are usually sequential (rather than simple categorical labels), involving rich information that overlaps with the learned representation. Consequently, the decoder gets confused about whether to ªlisten toºthe latent representation or the condition, and sometimes just ignores the condition. To solve this problem, we leverage domain adversarial training to disentangle the representation from condition cues for better control. Specifically, we propose a condition corruption objective that uses the representation to denoise a corrupted condition. Minimized by a discriminator and maximized by the VAE encoder, this objective adversarially induces a condition-invariant representation. In this paper, we focus on the task of melody harmonization1 to illustrate our idea, while our methodology can be generalized to other controllable generative tasks. Demos and experiments show that our methodology facilitates not only condition-invariant representation learning but also higher-quality controllability compared to baselines. © J. Zhao, G. Xia, and Y. Wang.

关键词： Adversarial machine learning

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