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Data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： Sudharsan S Rajesh S Selvam N Sangeeth Raj P S Babu Prasad R Duraimurugan A Department of Electrical and Electronics Engineering M. Kumarasamy College of Engineering Karur Department of Computer Science and Business System M. Kumarasamy College of Engineering Karur

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

The Smart Safety system is designed to enhance road safety by monitoring both the driver and passenger activities and ensuring proactive measures are taken to prevent accidents. The system leverages sensors and real-time data monitoring to ensure driver sobriety and attentiveness while also alerting the driver about potential passenger hazards. The system primarily focuses on two areas: driver safety and passenger detection. On the driver’s side, an alcohol sensor detects any sign of intoxication, and an eye-blink sensor monitors the driver’s focus, especially if distracted by using a phone. If intoxication is detected, the vehicle is automatically disabled through the motor control system, and an alert is triggered via an alarm and notification sent to a web-based platform. If the driver is distracted or drowsy, a warning alarm is activated. On the passenger side, an IR sensor and an ultrasonic sensor detect whether passengers are improperly standing or not seated, and a notification is displayed to the driver. The system integrates an Arduino Uno with several sensors, including alcohol, eye-blink, IR, and ultrasonic sensors. The collected data is transmitted wirelessly through a Wi-Fi module to a cloud platform, such as ThingSpeak, and then displayed on a website built with Java, HTML, and JavaScript. The website provides a login interface to enroll driver information and monitor alcohol sensor readings. In case of driver intoxication, the system triggers the suspension of the vehicle by turning off the motor. This smart safety system provides an efficient, real-time mechanism for preventing road accidents by monitoring driver behavior and ensuring passenger safety.

关键词： Cloud computing Motors Sensor systems Real-time systems Road safety Safety Protection Monitoring Intelligent sensors Vehicles

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A Design Window Search Using Nonlinear Dynamic Simulation for Polymer Micro-machined Transmission in Insect-inspired Flapping wing Nano Air Vehicles 6

A Design Window Search Using Nonlinear Dynamic Simulation fo...

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6th International Conference on Robotics and Automation engineering, ICRAE 2021

作者： Rashmikant Ishihara, Daisuke Kyushu Institute of Technology Department of Computer Science and System Engineering Fukuoka Japan

ISBN: (纸本)9781665406970

The novelty of this study is the proposition of a design window (DW) search using the nonlinear dynamic simulation, and the finding of the DWs of the polymer micro transmission that satisfies the nonlinear and unsteady design requirements conflicting with each other simultaneously. Here, the DW is defined as an existing area of satisfactory solutions in a design parameter space, which is defined as the flapping frequency equivalent to small flies and the mass of the micro wing supported by the transmission design. Since this micro transmission is the key component of the flapping wing nano air vehicle (FWNAVs) which has a 2.5-D structure and is fabricated using polymer micromachining so proposed DWs will contribute to further miniaturizing FWNAVs. © 2021 IEEE.

关键词： Biomimetics

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Attribute-Based Data Sharing Scheme with Flexible Search Functionality for Cloud-Assisted Autonomous Transportation system

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IEEE Transactions on Industrial Informatics 2023年第11期19卷 10977-10986页

作者： Xiong, Hu Wang, Hanxiao Meng, Weizhi Yeh, Kuo-Hui University of Electronic Science and Technology of China School of Information and Software Engineering Network and Data Security Key Laboratory of Sichuan Province Chengdu610054 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610025 China Technical University of Denmark Department of Applied Mathematics and Computer Science Kongens Lyngby2800 Denmark National Dong Hwa University Department of Information Management Hualien97401 Taiwan National Sun Yat-sen University Department of Computer Science and Engineering Kaohsiung804201 Taiwan

The existing group public key encryption with equality test schemes could only support one-to-one data sharing and are not suitable for cloud-assisted autonomous transportation systems, which demand one-to-many data sharing. To tackle this problem efficiently, in this article, we put forward the group-attribute-based encryption with equality test (G-ABEET) scheme. The presented G-ABEET allows sensors equipped in vehicles to encrypt traffic data with an expressive access policy before sharing it. Only users with attributes required by the access policy ought to access the shared ciphertexts, thus achieving selective one-to-many data sharing. Meanwhile, the authorized cloud server could provide group users with equality tests over the ciphertexts, realizing ciphertext search ability. Furthermore, with the group mechanism, the G-ABEET scheme could resist offline message recovery attacks. Besides, in the standard model, we give rigorous security proof of the G-ABEET construction. The feasibility and efficiency of G-ABEET are demonstrated by experimental simulations. © 2005-2012 IEEE.

关键词： Cloud computing

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Question-Answer Cross Language Image Matching for Weakly Supervised Semantic Segmentation

arXiv

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arXiv 2024年

作者： Deng, Songhe Zhuo, Wei Xie, Jinheng Shen, Linlin Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China

Class Activation Map (CAM) has emerged as a popular tool for weakly supervised semantic segmentation (WSSS), allowing the localization of object regions in an image using only image-level labels. However, existing CAM methods suffer from under-activation of target object regions and false-activation of background regions due to the fact that a lack of detailed supervision can hinder the model’s ability to understand the image as a whole. In this paper, we propose a novel Question-Answer Cross-Language-Image Matching framework for WSSS (QA-CLIMS), leveraging the vision-language foundation model to maximize the text-based understanding of images and guide the generation of activation maps. First, a series of carefully designed questions are posed to the VQA (Visual Question Answering) model with Question-Answer Prompt engineering (QAPE) to generate a corpus of both foreground target objects and backgrounds that are adaptive to query images. We then employ contrastive learning in a Region Image Text Contrastive (RITC) network to compare the obtained foreground and background regions with the generated corpus. Our approach exploits the rich textual information from the open vocabulary as additional supervision, enabling the model to generate high-quality CAMs with a more complete object region and reduce false-activation of background regions. We conduct extensive analysis to validate the proposed method and show that our approach performs state-of-the-art on both PASCAL VOC 2012 and MS COCO datasets. Code is available at: https://***/CVI-SZU/QA-CLIMS Copyright © 2024, The Authors. All rights reserved.

关键词： Chemical activation

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A track donation system using blockchain 2

A track donation system using blockchain

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2nd IEEE International Conference on Electronic engineering, ICEEM 2021

作者： Shaheen, Eisa Hamed, Mohamed Abdl Zaghloul, Walaa Al Mostafa, Eman El Sharkawy, Amr Mahmoud, Asmaa Labeb, Amira Al Enany, Marwa O Attiya, Gamal Ministry of Higher Education Higher Institute of Computer Science and Information System Computer Science Department Egypt Menoufia University Faculty of Electronic Engineering Computer Science and Engineering Department Egypt

ISBN: (纸本)9781665418423

Charities face a difficult funding environment, mainly because charities are not highly transparent. Day over day It became more difficult to know whether donations are reaching the right place, or taking another course, such as financing suspicious campaigns, terrorist projects, etc. As a result, donors lose faith in these organizations, leading to a halt to interactions with charitable organizations. This makes it more difficult for charities to raise money at a great cost. This research proposed a solution to the mentioned problem using Blockchain technology, a decentralized database that provides security, transparency, and lower funding cost by eliminating the presence of third parties between donors and charities. A new track donation model was proposed which introduced several new participants that control the process of donation and remove suspicion of the charity. All donations can be tracked in Blockchain, allowing donors to know where and how their money is being used. The proposed model was implemented using Hyperledger composer then it was tested to obtain a live full track of a donation process. All donations are made using smart contracts, which allow donors to know exactly when and how their donations will be received. The Blockchain track donation system results in time-saving, reducing the cost of donation, and reduces the risk of reaching suspicious campaign donations or terrorist projects. A website was created as a facilitator for users for ease of use of the proposed system. © 2021 IEEE.

关键词： Smart contract

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Cloud Connected IoT Platform for Tracking Solar PV Performance Across Diverse Weather Condition

Cloud Connected IoT Platform for Tracking Solar PV Performan...

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International Conference on Trends in Electronics and Informatics (ICEI)

作者： B. Rajeshkumar T. S. Venkateswaran Abishek J S Kavin M R Mukilan I Vishnu Varthan R Electrical and Electronics Engineering M.Kumarasamy College of Engineering Karur India Computer Science and Business System M.Kumarasamy College of Engineering Karur India

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

The contribution of the research in this paper presents a new Internet of Things (IoT) platform with cloud computing for the performance monitoring and optimization of solar photovoltaic (PV) systems in varying environmental conditions. The suggested platform has been designed with a real-time massive IoT sensor network for the monitoring of important parameters such as solar irradiance, ambient temperature, humidity, and panel temperature. With cloud computing, the system is calculating this information in real time so rapid assessment can be achieved and continuous monitoring of performance with analysis of the system. Most notable about the platform is its use of advanced analytics and machine learning algorithms, which are used to detect patterns, detect operational anomalies, and predict potential failure of components. This prognostic capacity supports real-time maintenance, lowers system downtime, and optimizes energy generation. Additionally, the remote monitoring and management capability of the platform supports users to stay in command of their solar properties remotely and comfortably from anywhere. The platform was rigorously tested across various climatic regions as well as in varying weather conditions and has become a versatile, reliable, and efficient performer in varied environments. It provides actionable intelligence for maximizing the usage of resources, maximizing energy efficiency, and allowing solar power projects to be sustainable. Scalability of cloud infrastructure allows, beyond integrating new technology, scaling to bigger and better PV system networks. With solar PV system dependability and performance, this IoT solution sets a new standard in the management of renewable energy. It combines technology with sustainability, propelling innovation toward smarter and stronger grids of energy. It is among the major inputs in supporting global action for a shift to cleaner and renewable sources of power, mitigating environmental challenges, an

关键词： Temperature sensors Solar irradiance Cloud computing Renewable energy sources Scalability Real-time systems Power system reliability Internet of Things Sustainable development Meteorology

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HairDiffusion: vivid multi-colored hair editing via latent diffusion 24

HairDiffusion: vivid multi-colored hair editing via latent d...

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Proceedings of the 38th International Conference on Neural Information Processing systems

作者： Yu Zeng Yang Zhang Jiachen Liu Linlin Shen Kaijun Deng Weizhao He Jinbao Wang Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University and Shenzhen Institute of Artificial Intelligence and Robotics for Society and National Engineering Laboratory for Big Data System Computing Technology Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University and Guangdong Provincial Key Laboratory of Intelligent Information Processing

ISBN: (纸本)9798331314385

Hair editing is a critical image synthesis task that aims to edit hair color and hairstyle using text descriptions or reference images, while preserving irrelevant attributes (e.g., identity, background, cloth). Many existing methods are based on StyleGAN to address this task. However, due to the limited spatial distribution of StyleGAN, it struggles with multiple hair color editing and facial preservation. Considering the advancements in diffusion models, we utilize Latent Diffusion Models (LDMs) for hairstyle editing. Our approach introduces Multi-stage Hairstyle Blend (MHB), effectively separating control of hair color and hairstyle in diffusion latent space. Additionally, we train a warping module to align the hair color with the target region. To further enhance multi-color hairstyle editing, we fine-tuned a CLIP model using a multi-color hairstyle dataset. Our method not only tackles the complexity of multi-color hairstyles but also addresses the challenge of preserving original colors during diffusion editing. Extensive experiments showcase the superiority of our method in editing multi-color hairstyles while preserving facial attributes given textual descriptions and reference images.

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DEGSTalk: Decomposed Per-Embedding Gaussian Fields for Hair-Preserving Talking Face Synthesis

DEGSTalk: Decomposed Per-Embedding Gaussian Fields for Hair-...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Kaijun Deng Dezhi Zheng Jindong Xie Jinbao Wang Weicheng Xie Linlin Shen Siyang Song Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Guangdong Provincial Key Laboratory of Intelligent Information Processing Department of Computer Science University of Exeter

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Accurately synthesizing talking face videos and capturing fine facial features for individuals with long hair presents a significant challenge. To tackle these challenges in existing methods, we propose a decomposed per-embedding Gaussian fields (DEGSTalk), a 3D Gaussian Splatting (3DGS)-based talking face synthesis method for generating realistic talking faces with long hairs. Our DEGSTalk employs Deformable Pre-Embedding Gaussian Fields, which dynamically adjust pre-embedding Gaussian primitives using implicit expression coefficients. This enables precise capture of dynamic facial regions and subtle expressions. Additionally, we propose a Dynamic Hair-Preserving Portrait Rendering technique to enhance the realism of long hair motions in the synthesized videos. Results show that DEGSTalk achieves improved realism and synthesis quality compared to existing approaches, particularly in handling complex facial dynamics and hair preservation. Our code is available at https://***/CVI-SZU/DEGSTalk.

关键词： Hair Training Three-dimensional displays Dynamics Signal processing Rendering (computer graphics) Noise measurement Speech processing Faces Videos

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LibAMM: empirical insights into approximate computing for accelerating matrix multiplication 24

LibAMM: empirical insights into approximate computing for ac...

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Proceedings of the 38th International Conference on Neural Information Processing systems

作者： Xianzhi Zeng Wenchao Jiang Shuhao Zhang National Engineering Research Center for Big DataTechnology 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 Wuhan China and Nanyang Technological University Singapore University of Technology and Design National Engineering Research Center for Big DataTechnology 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 Wuhan China

ISBN: (纸本)9798331314385

Matrix multiplication (MM) is pivotal in fields from deep learning to scientific computing, driving the quest for improved computational efficiency. Accelerating MM encompasses strategies like complexity reduction, parallel and distributed computing, hardware acceleration, and approximate computing techniques, namely AMM algorithms. Amidst growing concerns over the resource demands of large language models (LLMs), AMM has garnered renewed focus. However, understanding the nuances that govern AMM's effectiveness remains incomplete. This study delves into AMM by examining algorithmic strategies, operational specifics, dataset characteristics, and their application in real-world tasks. Through comprehensive testing across diverse datasets and scenarios, we analyze how these factors affect AMM's performance, uncovering that the selection of AMM approaches significantly influences the balance between efficiency and accuracy, with factors like memory access playing a pivotal role. Additionally, dataset attributes are shown to be vital for the success of AMM in applications. Our results advocate for tailored algorithmic approaches and careful strategy selection to enhance AMM's effectiveness. To aid in the practical application and ongoing research of AMM, we introduce LibAMM —a toolkit offering a wide range of AMM algorithms, benchmarks, and tools for experiment management. LibAMM aims to facilitate research and application in AMM, guiding future developments towards more adaptive and context-aware computational solutions.

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Unsupervised Graph-Sequence Anomaly Detection for 5G Core Network Control Plane Traffic 29

Unsupervised Graph-Sequence Anomaly Detection for 5G Core Ne...

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29th IEEE International Conference on Parallel and Distributed systems, ICPADS 2023

作者： Wang, Min Li, Peng Cheng, Zhang Liu, Wenmao Nie, Lei Bao, Haizhou Liu, Qin Zhang, Kai Wuhan University of Science and Technology School of Computer Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System China Nsfocus Technologies Group Co. Ltd China Wuhan University School of Cyber Science and Engineering China

ISBN: (纸本)9798350330717

5G Core network (5GC) employs a Service Based Architecture (SBA). This architecture decomposes the control plane into multiple independent Network Functions (NFs). NFs open interfaces to provide services to other NFs, which makes the control plane more susceptible to external malicious attacks. However, existing anomaly detection methods focus more on traffic statistics features and are difficult to apply to the 5GC control plane. In this paper, we proposed GSAD, a Graph-Sequence analysis-based Anomaly Detection method for 5GC control plane traffic. We model control plane traffic as a directed graph to depict topological and NF interaction information. Further, we use the normalizing flows with temporal dependencies to mine the sequential information in the traffic. GSAD combines the topological and sequential information to provide fine-grained detection. We evaluate our proposed framework on the 5GC testbed using Free5GC and UERANSIM in various scenarios. Experimental results demonstrate that our framework outperforms the baselines significantly in terms of detection performance. © 2023 IEEE.

关键词： 5G core network anomaly detection control plane graph neural network normalizing flows

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