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A Neural Network Model for Autonomous Vehicles Safety Check Using Camera-only/Camera-Lidar Images

A Neural Network Model for Autonomous Vehicles Safety Check ...

引用

International Conference on Computing, Networking and Communications (ICNC)

作者： Dengyi Liu Honggang Wang Hua Fang Graduate Computer Science and Engineering Department Katz School of Health and Science Yeshiva University NY Dept. of Computer and Information Science University of Massachusetts Dartmouth MA

ISBN: (数字)9798331520960

ISBN: (纸本)9798331520977

Camera and LiDAR sensors play a crucial role in vehicle perception systems, enabling accurate detection of obstacles and other vehicles in autonomous driving technologies. Despite their complementary advantages, ensuring the reliability of camera data under varying environmental conditions poses a significant challenge. This paper introduces a novel system for camera data consistency checking through two methods: (1) a camera-only consistency checking mechanism utilizing an effective point-to-point feature extraction neural network inspired by the D2-Net method, and (2) a camera-LiDAR fusion data consistency checking approach employing advanced sensor fusion techniques. By integrating a critical reliability check for camera data and fusing it with LiDAR information, our system leverages the strengths of both sensor types to enhance the robustness and efficiency of autonomous vehicle navigation. Experimental results demonstrate that the proposed methods significantly improve the safety and performance of autonomous driving by ensuring reliable sensor data fusion, enabling safe navigation even in challenging environments.

关键词： Deep learning Accuracy Neural networks Sensor fusion Cameras Feature extraction Sensor systems Sensors Safety Autonomous vehicles

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A New Approach Towards Deployment and Management of Machine Learning Models Using K-serve Platform for Building Robust Machine Learning Systems 4

A New Approach Towards Deployment and Management of Machine ...

引用

4th IEEE Global Conference for Advancement in Technology, GCAT 2023

作者： Gill, Kanwarpartap Singh Anand, Vatsala Gupta, Rupesh Khosla, Charu Hsiung, Pao-Ann Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Dept. of Computer Science and Information Engineering National Chung Cheng university Chiayi County Taiwan

ISBN: (纸本)9798350305258

Kserve, commonly referred to as KFServing, is an open-source serving framework for machine learning (ML) models that runs on Kubernetes. enables the deployment, servicing, and management of ML models in a production environment in a scalable and effective manner. Kserve resources make it easier to develop scalable and reliable machine learning serving systems by streamlining the deployment and management of ML models in production. With contributions from several organisations and developers in the ML community, it is a dynamic open-source project. The influence that Kserve's work has had on the Data science community is extensively covered in this research. This research also discusses the Kserve design, elements, and features. The additional Kserve platforms that aid in the deployment and administration of machine learning models for creating reliable machine learning systems have also been brought to light. © 2023 IEEE.

关键词： Artificial Intelligence Data science Deep Learning Docker Kserve Kubernetes Machine Learning

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HDLEval Benchmarking LLMs for multiple HDLs

HDLEval Benchmarking LLMs for multiple HDLs

引用

Aided Design Workshop (LAD), IEEE LLM

作者： Farzaneh Rabiei Kashanaki Mark Zakharov Jose Renau Dept. of Computer Science and Engineering University of California Santa Cruz

ISBN: (数字)9798350376081

ISBN: (纸本)9798350376098

Large Language Models (LLMs) are transforming code generation and documentation processes across programming languages, including hardware description languages (HDLs). However, existing benchmarks primarily focus on individual HDLs, limiting comprehensive evaluations. To address this, we introduce HDLEval, a versatile benchmarking system that evaluates LLM performance across multiple HDLs, enabling meaningful comparisons. By sharing standardized test benches, HDLEval ensures consistent performance assessments between HDLs, offering insights into language-agnostic LLM *** system employs formal verification to verify generated code and leverages prompt engineering techniques to overcome syntactic differences between HDLs. HDLEval supports scalable evaluation, making it adaptable for current and future HDLs. Our experiments demonstrate how HDLEval can identify strengths and weaknesses in LLM-generated logic, providing a framework for improving HDL programming with LLMs.

关键词： Codes Limiting Large language models Benchmark testing Syntactics Programming Hardware Prompt engineering Logic Hardware design languages

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Hyperparameter Tuning of Convolution Neural Network for Intrusion Detection Systems using Particle Swarm Optimization

Hyperparameter Tuning of Convolution Neural Network for Intr...

引用

Computational Intelligence in Security, Defence and Biometrics Companion (CISDB Companion), IEEE Symposium on

作者： Gina Grossi Beatrice Ombuki-Berman Glaucio H.S. de Carvalho Dept. of Computer Science Brock University St. Catharines Canada Dept. of Computer Science and Engineering Brock University St. Catharines Canada

ISBN: (数字)9798331508470

ISBN: (纸本)9798331508487

A research challenge is to investigate multi-class classification methods that can successfully identify and categorize intrusions in network traffic problems. Convolutional Neural Networks (CNN) have been shown to successfully detect and classify multiple types of network anomalies. However, to automatically leverage its performance, the hyperparameters of the CNN usually need to be tuned. We study the use of three existing Particle Swarm Optimization (PSO) variants: hybrid cooperative PSO (CPSO-HK), quantum PSO (QPSO), adaptive PSO (APSO), and introduce a new variant QAPSO, for the purpose of tuning hyperparameters of a 1D-CNN model. This work examines the effects this tuning has on improving the model’s ability to classify multiple Botnet intrusion attacks. The QAPSO variant is shown to converge significantly faster than all other variants when tuning. The results indicate that the structure size of the model is influenced by the PSO variant tuning. It is also found that during training and testing, models tuned with QAPSO and QPSO produce more consistent results and are able to outperform models tuned with CPSO-HK and APSO.

关键词： Training Adaptation models Computational modeling Biological system modeling Botnet Intrusion detection Convolutional neural networks Particle swarm optimization Tuning Testing

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A Bengali Physics Chatbot Utilizing Sequence-to-Sequence Model with Beam Search

A Bengali Physics Chatbot Utilizing Sequence-to-Sequence Mod...

引用

Computing, Applications and Systems (COMPAS), IEEE International Conference on

作者： Javed Hossain Dept. of Computer Science & Engineering City University Dhaka Bangladesh

ISBN: (数字)9798331529765

ISBN: (纸本)9798331529772

Chatbots, also known as talkbots or interactive agents, are software applications designed to facilitate communication between humans and machines. While most students in Bangladesh currently waste their valuable time engaging in casual conversations with each other, using a Bengali educational chatbot for such interactions would provide them with a more beneficial use of their time by providing hand note-wise answers for physics. The challenge faced by a Bengali chatbot lies in the necessity of creating a dataset in Bengali, given the scarcity of available Bengali datasets online. We have developed a physics-focused educational chatbot, akin to personalized hand notes, offering comprehensive answers to aid students in learning and revising complex physics concepts. The development of chatbots involves diverse techniques, including rule-based systems, machine learning-based approaches, and generative models. Our proposed system implements the seq2seq model with a beam search decoding algorithm, featuring BiLSTM layers for enhanced context understanding and efficient exploration of potential output sequences. This approach outperforms traditional seq2seq models, ensuring higher accuracy and delivering high-quality responses. Finally, we obtained the highest BLEU1, BLEU2, BLEU3, and BLEU4 scores, respectively, of 0.52, 0.41, 0.29, and 0.23.

关键词： Hands Accuracy Machine learning algorithms Social networking (online) Oral communication Predictive models Chatbots Software Decoding Physics education

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Integrating Tradition and Technology: A Survey of AI Applications in the Design of Temple Architecture

Integrating Tradition and Technology: A Survey of AI Applica...

引用

Artificial Intelligence, Metaverse and Cybersecurity (ICAMAC), International Conference on

作者： J Meghana Harshavardana R Bellad Diya Pardhi Ishwari Magdum Chaitra Nagaraj dept. Computer Science Engineering PES University Bengaluru India

ISBN: (数字)9798350353488

ISBN: (纸本)9798350353495

This article will delve into the complicated inter-building of artificial intelligence and traditional skill of temple architecture of South India. It meticulously analyzes the historical roots of temple design, explaining in detail the subtle means and methods that have always been used in temple construction. Apart from detailed preparation and skilled craftsmanship, it is the cultural heritage that is demonstrated with these monumental creations. In the sequel, the paper sheds light on how AI technologies are transforming temple architecture through systems analysis and optimization, generative design and other methods. Living examples of Artificial Intelligence work do not only confirm the successful AI implementations but also illustrate the smooth link between tradition and novelty in the modern temple designs.

关键词： Surveys Technological innovation Metaverse Design methodology Cultural differences Artificial intelligence computer security System analysis and design Optimization Guidelines

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Innovative Approaches to CT Image Enhancement: Deep Learning Solutions for Denoising and Deblurring

Innovative Approaches to CT Image Enhancement: Deep Learning...

引用

2025 International Conference on Ambient Intelligence in Health Care, ICAIHC 2025

作者： Prajapat, Gopal Krishan Yadav, Dharmendra Bikaner Technical University Dept. of Computer Science and Engineering Bikaner India

ISBN: (纸本)9798331530259

Computed Tomography (CT) is essential in medical diagnostics, providing detailed images of internal structures. Noise and blur introduced during image acquisition can com-promise diagnostic accuracy, especially in low-dose CT (LDCT) scans aimed at reducing radiation exposure. Deep learning methods have emerged as effective solutions for addressing these challenges, with significant advancements in both denoising and deblurring techniques. This paper reviews recent deep learning approaches for CT image enhancement, covering supervised and self-supervised methods. The paper also explores emerging trends such as diffusion models and transformer-based architectures that offer improved image quality and restoration efficiency. Simultaneous denoising and deblurring techniques are examined for their potential in clinical applications. Promising future directions include the use of large-scale pretrained models, zero-shot learning, and prompt-based frameworks, which have the potential to further enhance CT image quality while maintaining low radiation exposure, thereby advancing diagnostic capabilities. © 2025 IEEE.

关键词： Convolutional neural networks

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Latency-Aware Cell Deletion in Link Scheduling for 6TiSCH Networks

Latency-Aware Cell Deletion in Link Scheduling for 6TiSCH Ne...

引用

International Communication Systems and Networks and Workshops, COMSNETS

作者： Karnish N A Tapadar Manas Khatua Venkatesh Tamarapalli Dept. of Computer Science and Engineering Indian Institute of Technology Guwahati

The 6TiSCH standard uses IEEE 802.15.4 Time Slotted Channel Hopping (TSCH) as a MAC layer protocol. TSCH cell scheduling determines packet transmission schedules in terms of timing and channel allocation. The Minimal Scheduling Function (MSF) and the 6P protocol facilitate schedule management dynamically. MSF involves a decision-making process to add/delete one cell at a time from the current schedule to adapt the schedule to modified traffic rate. The cell deletion is accomplished by randomly selecting one of the scheduled cells between two communicating nodes. However, random cell deletion can elevate end-to-end latency and energy consumption. Therefore, this paper proposes a Latency-aware Cell Deletion (LCD) mechanism to minimize associated average end-to-end latency and energy consumption. Simulations using the 6TiSCH simulator verified that in an 80-node network, compared to the standard MSF and another benchmark scheme IMSF, LCD achieved a substantial 44.40% and 42.39% reduction in end-to-end latency, respectively.

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Design and Development of Expense App

Design and Development of Expense App

引用

Advances in Computing Research on science engineering and Technology (ACROSET), International Conference on

作者： Gunit Girdhar Sachin Kumar Anuj Bhardwaj Megha Sharma Dept. of Computer Science & Engineering Chandigarh University Mohali India

ISBN: (数字)9798350388800

ISBN: (纸本)9798350388817

With the help of Expense Tracker, you can easily and intuitively manage your revenue and costs while keeping track of your everyday, weekly, monthly, and annual spending. Users may choose the kind of expenditure, add location, upload more data, choose the amount of expense, and more. Additionally, the relational database will save this data. Users have the option to see and sort spending weekly, monthly, or annually. We may track the spending and minimize the amount of human computations for their costs by employing this. This allows a user to enter their income to get an estimate of their daily costs, and the results are saved for each individual user. Individuals who frequently go on excursions or to the theatre with friends might use this tracker to keep tabs on their spending. They will find it simple to split the bill using this tracker. This will show graphs according to the chosen perspective. Additionally, a user may write in this TR his monthly income or monthly expenditure cap. An integrated feature set is offered by this tracker system to assist you in managing your spending and cash flow.

关键词： Costs Relational databases Investment

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Improving Multi-Domain Task-Oriented Dialogue System with Offline Reinforcement Learning

Improving Multi-Domain Task-Oriented Dialogue System with Of...

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IEEE International Conference on Big Data

作者： Dharmendra Prajapat Durga Toshniwal Dept of Computer Science and Engineering Indian Institute of Technology Roorkee India

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Task-oriented dialogue (TOD) system is designed to accomplish user-defined tasks through dialogues. The TOD system has progressed towards end-to-end modeling by leveraging pre-trained large language models. Fine-tuning the pre-trained language models using only supervised learning leads to the exposure bias and token loss problem and it deviates the models from completing the user’s task. To address these issues, we propose a TOD system that leverages a unified pre-trained language model, GPT-2, as a base model. It is optimized using supervised learning and offline reinforcement learning (RL). The issues in the TOD system are mitigated using a non-differentiable reward function. The reward is calculated using the weighted sum of the success rate and BLEU evaluation metrics. The success rate and BLEU metrics in reward calculation guide the language model for user task completion while ensuring a coherent and fluent response. Our model is acquired by fine-tuning a pre-trained model on the dialogue-session level which comprises user utterance, belief state, system act, and system response. Experimental results on MultiWOZ2.1 demonstrate that our model increases the inform rate by 1.60% and the success rate by 3.17% compared to the baseline.

关键词： Measurement Adaptation models Large language models Supervised learning Unified modeling language Training data Reinforcement learning Big Data Data models Context modeling

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