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IEEE Transactions on Intelligent Vehicles 2024年 1-13页

作者： Lyu, Guanghao Peng, Zhouhua Wang, Dan Wang, Jun School of Marine Electrical Engineering Dalian Maritime University Dalian China Department of Computer Science and the School of Data Science City University of Hong Kong Hong Kong

This paper addresses the safety-certified motion planning and containment control of under-actuated autonomous surface vehicles subject to model uncertainties, external disturbances, and input constraints in the presence of stationary and moving obstacles. A three-level modular control architecture is proposed with a trajectory generation module at its planning level, an adaptive guidance module at its guidance level, and a kinetic control module at its control level. Specifically, at the planning level, a safety-certified containment trajectory generator is designed to generate safe trajectories over a rolling time window to achieve containment formation and collision avoidance with neighboring ASVs, stationary obstacles, and moving obstacles via dynamic control barrier functions and twotimescale neurodynamic optimization models. At the guidance level, an adaptive line-of-sight guidance law is developed based on a finite-time predictor to estimate unknown sideslip angles and generate guidance commands. At the control level, an optimal control law is designed based on finite-time neural predictors and control Lyapunov functions for the autonomous surface vehicle with input constraints to follow the desired guidance commands. The effectiveness and characteristics of the proposed method are demonstrated via simulations and hardware-in-theloop experiments for cooperative exploration. IEEE

关键词： Optimization

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Plant Fettle Detector: Unifying Fruit Quality Tracking and Plant Disease Detection through Advanced Image Analysis 3

Plant Fettle Detector: Unifying Fruit Quality Tracking and P...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Nair, Sruthi Bajpai, Khushi Fadnavis, Sakshi Jain, Shruti Sheikh, Zeenat Shri Ramdeobaba College of Engineering and Management Department of Computer Science and Engineering - Data Science Nagpur44013 India

ISBN: (纸本)9798350381931

India being an agricultural country, food quality tracking is a major challenge faced by common farmers across the country. This research presents an innovative integration of Convolutional Neural Networks (CNNs) to address key challenges in agriculture - fruit quality tracking and plant disease detection. The unified system comprises the Fruit Quality Tracker and the Plant Disease Detector, leveraging advanced image analysis for precision agriculture. The Fruit Quality Tracker uses computer vision to assess ripeness and defects in real-time, empowering farmers with informed decisions. Simultaneously, the Plant Disease Detector swiftly identifies and classifies diseases, mitigating economic losses. The research explores architectural nuances, development methodologies, and deployment on Google Cloud Platform. Testing shows high accuracy rates of 98.5% for fruit quality and 98.83% for plant disease detection. The system's transformative potential in agriculture is highlighted, bridging the gap between technology and sustainable food production systems. © 2024 IEEE.

关键词： Image analysis

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Roughness Analysis of Stainless Steel Bearing Using computer Vision 6

Roughness Analysis of Stainless Steel Bearing Using Computer...

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6th International Conference on Electrical, Control and Instrumentation engineering, ICECIE 2024

作者： Das, Shounak Tarafdar, Arundhati Mondal, Prabir Computer Science and Engineering Jadavpur University Kolkata India School of Computer Science Data Science cluster UPES University Dehradun India Computer Science and Engineering National Institute of Technology Patna India

ISBN: (纸本)9798350380040

Current technology utilizes a sophisticated standard room measurement system to analyze the average roughness of Stainless Steel Bearing Surface. However, this process relies on random sampling at infrequent intervals, resulting in inefficiency and inadequate analysis of a large number of samples. To address this issue, we propose a solution using computer vision via laser scanning. The scanning process produces an amplified speckle pattern of the ring surface. This pattern is then analyzed to verify the number of edges that cross a predefined threshold and exhibit symmetrical patterns, particularly on smoothed ring surfaces. Remarkably, this system operates with a turnaround time of less than a second. Moreover, unlike the standard room setup, this technology can be seamlessly integrated directly into production/manufacturing units. © 2024 IEEE.

关键词： Speckle

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Unsupervised social network embedding via adaptive specific mappings

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Frontiers of computer science 2024年第3期18卷 61-71页

作者： Youming GE Cong HUANG Yubao LIU Sen ZHANG Weiyang KONG School of Computer Science and Engineering Sun Yat-Sen UniversityGuangzhou 510275China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou 510006China

In this paper,we address the problem of unsuperised social network embedding,which aims to embed network nodes,including node attributes,into a latent low dimensional *** recent methods,the fusion mechanism of node attributes and network structure has been proposed for the problem and achieved impressive prediction ***,the non-linear property of node attributes and network structure is not efficiently fused in existing methods,which is potentially helpful in learning a better network *** this end,in this paper,we propose a novel model called ASM(Adaptive Specific Mapping)based on encoder-decoder *** encoder,we use the kernel mapping to capture the non-linear property of both node attributes and network *** particular,we adopt two feature mapping functions,namely an untrainable function for node attributes and a trainable function for network *** the mapping functions,we obtain the low dimensional feature vectors for node attributes and network structure,***,we design an attention layer to combine the learning of both feature vectors and adaptively learn the node *** encoder,we adopt the component of reconstruction for the training process of learning node attributes and network *** conducted a set of experiments on seven real-world social network *** experimental results verify the effectiveness and efficiency of our method in comparison with state-of-the-art baselines.

关键词： network embedding specific kernel mapping attention mechanism

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A Review on Blockchain Technology in Health Care Systems 6th

A Review on Blockchain Technology in Health Care Systems

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6th International Conference on Communications and Cyber-Physical engineering, ICCCE 2023

作者： Rajendar, Janga Kishore Kumar, Botcha Verma, Toran Lavanya, Mangalampalli Sasha Sai Lakshmi Department of Computer Science Engineering - Data Science CMR Engineering College Kandlakoya Telangana Hyderabad501401 India

ISBN: (纸本)9789819971367

Blockchain is a distributed database that multiple parties can maintain and share. This new technology is expected to greatly impact the healthcare industry. It can help address various issues related to patient care. Through the use of blockchain technology, data can be moved in real-time without exposing it to various risks such as theft and forgery. This paper aims to introduce the general idea of the Blockchain and its applications in the healthcare industry. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Blockchain

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CONTESTS: a Framework for Consistency Testing of Span Probabilities in Language Models

CONTESTS: a Framework for Consistency Testing of Span Probab...

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

作者： Wagner, Eitan Slavutsky, Yuli Abend, Omri School of Computer Science and Engineering Department of Statistics and Data Science Hebrew University of Jerusalem Israel

ISBN: (纸本)9798891761643

Although language model scores are often treated as probabilities, their reliability as probability estimators has mainly been studied through calibration, overlooking other aspects. In particular, it is unclear whether language models produce the same value for different ways of assigning joint probabilities to word spans. Our work introduces a novel framework, ConTestS (Consistency Testing over Spans), involving statistical tests to assess score consistency across interchangeable completion and conditioning orders. We conduct experiments on post-release real and synthetic data to eliminate training effects. Our findings reveal that both Masked Language Models (MLMs) and autoregressive models exhibit inconsistent predictions, with autoregressive models showing larger discrepancies. Larger MLMs tend to produce more consistent predictions, while autoregressive models show the opposite trend. Moreover, for both model types, prediction entropies offer insights into the true word span likelihood and therefore can aid in selecting optimal decoding strategies. The inconsistencies revealed by our analysis, as well their connection to prediction entropies and differences between model types, can serve as useful guides for future research on addressing these limitations. © 2024 Association for Computational Linguistics.

关键词： Prediction models

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Precise control of page cache for containers

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Frontiers of computer science 2024年第2期18卷 17-30页

作者： Kun WANG Song WU Shengbang LI Zhuo HUANG Hao FAN Chen YU Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing Lab School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China

Container-based virtualization isbecoming increasingly popular in cloud computing due to its efficiency and *** isolation is a fundamental property of *** works have indicated weak resource isolation could cause significant performance degradation for containerized applications and enhanced resource ***,current studies have almost not discussed the isolation problems of page cache which is a key resource for *** leverage memory cgroup to control page cache ***,existing policy introduces two major problems in a container-based ***,containers can utilize more memory than limited by their cgroup,effectively breaking memory ***,the Os kernel has to evict page cache to make space for newly-arrived memory requests,slowing down containerized *** paper performs an empirical study of these problems and demonstrates the performance impacts on containerized *** we propose pCache(precise control of page cache)to address the problems by dividing page cache into private and shared and controlling both kinds of page cache separately and *** do so,pCache leverages two new technologies:fair account(f-account)and evict on demand(EoD).F-account splits the shared page cache charging based on per-container share to prevent containers from using memory for free,enhancing memory *** EoD reduces unnecessary page cache evictions to avoid the performance *** evaluation results demonstrate that our system can effectively enhance memory isolation for containers and achieve substantial performance improvement over the original page cache management policy.

关键词： page cache memory cgroup container isolation cloud computing

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Foundation models in bioinformatics

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National science Review 2025年第4期12卷 400-418页

作者： Fei Guo Renchu Guan Yaohang Li Qi Liu Xiaowo Wang Can Yang Jianxin Wang Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Xiangjiang Laboratory Key Laboratory for Symbol Computation and Knowledge Engineering of the Ministry of Education College of Computer Science and Technology Jilin University Department of Computer Science Old Dominion University School of Life Sciences and Technology Tongji University Department of Automation Tsinghua University Department of Mathematics State Key Laboratory of Molecular Neuroscience and Big Data Bio-Intelligence Lab The Hong Kong University of Science and Technology

With the adoption of foundation models(FMs),artificial intelligence(AI) has become increasingly significant in bioinformatics and has successfully addressed many historical challenges,such as pre-training frameworks,model evaluation and *** demonstrate notable proficiency in managing large-scale,unlabeled datasets,because experimental procedures are costly and labor *** various downstream tasks,FMs have consistently achieved noteworthy results,demonstrating high levels of accuracy in representing biological entities.A new era in computational biology has been ushered in by the application of FMs,focusing on both general and specific biological *** this review,we introduce recent advancements in bioinformatics FMs employed in a variety of downstream tasks,including genomics,transcriptomics,proteomics,drug discovery and single-cell *** aim is to assist scientists in selecting appropriate FMs in bioinformatics,according to four model types:language FMs,vision FMs,graph FMs and multimodal *** addition to understanding molecular landscapes,AI technology can establish the theoretical and practical foundation for continued innovation in molecular biology.

关键词： foundation model bioinformatics genomics transcriptomics proteomics drug discovery single-cell analysis

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An Intelligent LLM-Powered Personalized Assistant for Digital Banking Using LangGraph and Chain of Thoughts 22

An Intelligent LLM-Powered Personalized Assistant for Digita...

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22nd IEEE International Symposium on Intelligent Systems and Informatics, SISY 2024

作者： Easin, Arafat Md Sourav, Saha Tamas, Orosz Data Science and Engineering Dept. Budapest Hungary Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350385601

Text-based applications and chatbots are increasingly popular for delivering banking services and educational tools, offering convenient and efficient solutions for users. Whereas, personalized assistants have transformed user engagement in the digital banking space by utilizing Large Language Models (LLMs) in conjunction with autonomous agents. This study proposes the development of an intelligent personalized assistant for digital banking, utilizing a multi-agent framework based on the LangGraph and Chain of Thoughts (COT) prompting. While COT guarantees context-aware replies, the LangGraph design maps characteristics to nodes to improve user interactions. The objectives of this system are to enhance task efficiency and elevate the capabilities of digital banking assistants. We present a customizable digital banking system powered by LLM-based models, designed to deliver an interactive and personalized banking experience. The system supports a range of services, including adding money, transferring funds, paying bills, accessing telco services like mobile recharge, managing savings interest rates, DPS schemes, fixed deposits, and answering FAQs related to banking information. Therefore, integrating COT for logical reasoning enhances the effectiveness of multi-agent systems, as each single agent benefits from the structured reasoning process. In addition, LangGraph is employed for structured data management, enabling the assistant to support and accelerate various digital banking processes efficiently. The code implementation of this work is available for public access at: https://***/srv-sh/digital-agent. © 2024 IEEE.

关键词： Information management

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Ensemble Based Precautionary Detection of Parkinson's Disease from Voice Signal 1

Ensemble Based Precautionary Detection of Parkinson's Diseas...

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1st International Conference for Women in Computing, InCoWoCo 2024

作者： Bhanusree, Yalamanchili Adhvy, Vodnala Sharan Affan, Mohammed Shaji Praneetha, Kanvapuri Sai Sahithi, Katikireddy Sai Computer Science - Data Science VNR Vignana Jyothi Institute of Engineering & Technology Hyderabad India

ISBN: (纸本)9798331518943

Parkinson's Disease is a neurological progressive disorder that affects the nervous system of a human being, which effects the parts of the body. Tremors, imbalance, slowed movement, stiffness, difficulty in walking and talking etc. are observed. The reasons being, brain disorder, injuries, toxins certain drugs etc. The disease has no cure however treatment can be offered for relief from symptoms. However early detection can cure the disease in the first stages and extend the life span of the patient. This paper proposes a Ensembled learning model for early detection of Parkinsons disease through voice features. Random forest ensemble models have outperformed the Machine learning and Neural Network models in detecting disease from voice. An accuracy of 94.8% is achieved with this model with proper preprocessing techniques and training. As a future work the proposed model's software prototype can be released for mobile application for cost effective early detection of Parkinsons Disease. © 2024 IEEE.

关键词： Neurodegenerative diseases

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