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International Symposium on Advanced Computing and Communication (ISACC)

作者： Devesh Kumar Kamlesh Kumar Rahul Kumar Choudhary Varnika Singh Dept. of Computer Science & Engineering NIT Hamirpur India Dept. of Electrical Engineering NIT Hamirpur India Dept. of Electronics & Communication Engineering UIET Panjab University Chandigarh India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

This research examines artificial intelligence ethics implementation across diverse cultural contexts through case studies in healthcare, finance, education, and corporate sectors. Using mixed-methods analysis across six cultural regions, the study reveals fundamental variations in approaches to AI ethics, particularly regarding data privacy and human-technology relationships. The integration of Western, Eastern, and African perspectives yields three frameworks: a cultural intelligence model for AI development, an adaptive governance mechanism, and a cross-cultural collaboration approach. Results demonstrate that successful AI implementation requires balancing diverse cultural perspectives with technical innovation through flexible governance structures, providing practical pathways for culturally sensitive AI development.

关键词： Measurement Ethics Technological innovation Systematics Standards organizations Medical services Cultural differences Global communication Stakeholders Artificial intelligence

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Smart Traffic Management System for Urban Mobility Enhancement Using RF Sensors, Cameras, and Machine Learning

Smart Traffic Management System for Urban Mobility Enhanceme...

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International Conference on Power, Energy, Control and Transmission Systems (ICPECTS)

作者： Vivekanandan G Pathmanaban J Prabahar Godwin James Barani Sree S Priyanka V Samvardhini V M Dept. of Computer Science and Engineering Sri Sai Ram Institute of Technology

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508852

This paper introduces a Smart Traffic Management System (STMS)employing RF sensors, cameras, and machine learning algorithms to monitor and optimize urban traffic. The system dynamically adjusts traffic signal timings, offers real-time route recommendations based on GPS data, and incorporates adaptive control mechanisms to reduce congestion and improve overall mobility. Simulation studies and real-world testing demonstrate the effectiveness of the STMS in enhancing traffic flow, minimizing waittimes, and contributing to sustainable urbandevelopment.

关键词： Radio frequency Machine learning algorithms Object detection Light emitting diodes Sensor systems Real-time systems Liquid crystal displays Object recognition Intelligent sensors Monitoring

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Bidirectional Multi-Step Domain Generalization for Visible-Infrared Person Re-Identification

Bidirectional Multi-Step Domain Generalization for Visible-I...

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IEEE Workshop on Applications of computer Vision (WACV)

作者： Mahdi Alehdaghi Pourya Shamsolmoali Rafael M. O. Cruz Eric Granger Dept. of Systems Engineering ETS LIVIA ILLS Montreal Canada Dept. of Computer Science University of York UK

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

A key challenge in visible-infrared person re-identification (V-I ReID) is training a backbone model capable of effectively addressing the significant discrepancies across modalities. State-of-the-art methods that generate a single intermediate bridging domain are often less effective, as this generated domain may not adequately capture sufficient common discriminant information. This paper introduces Bidirectional Multi-step Domain Generalization (BMDG), a novel approach for unifying feature representations across diverse modalities. BMDG creates multiple virtual intermediate domains by learning and aligning body part features extracted from both I and V modalities. In particular, our method aims to minimize the cross-modal gap in two steps. First, BMDG aligns modalities in the feature space by learning shared and modality-invariant body part prototypes from V and I images. Then, it generalizes the feature representation by applying bidirectional multi-step learning, which progressively refines feature representations in each step and incorporates more prototypes from both modalities. Based on these prototypes, multiple bridging steps enhance the feature representation. Experiments 1 1 Our code is available at: ***/BMDG conducted on V-I ReID datasets indicate that our BMDG approach can outperform state-of-the-art part-based and intermediate generation methods, and can be integrated into other part-based methods to enhance their V-I ReID performance.

关键词： Training computer vision Accuracy Codes Prototypes Feature extraction Identification of persons

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Multi-Target Integrated Sensing and Communication with MIMO In-Band Full-Duplex Systems 58

Multi-Target Integrated Sensing and Communication with MIMO ...

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58th Asilomar Conference on Signals, Systems and computers, ACSSC 2024

作者： Chih, Yung-Chih Shen, Chung-An Celik, Abdulkadir Fouda, Mohammed E. Eltawil, Ahmed National Taiwan University of Science and Technology Electronic and Computer Engineering Dept. Taipei Taiwan King Abdullah University of Science and Technology Thuwal Saudi Arabia Rain Neuromorphics Inc. San FranciscoCA United States

ISBN: (纸本)9798350354058

In recent years, the integration of Multi-Input Multi-Output (MIMO) technology with In-Band Full-Duplex (IBFD) systems has emerged as a promising approach for multi-targets Integrated Sensing and Communication (ISAC), particularly in the context of 6G and vehicle-to-everything (V2X) communications. However, the simultaneous transmission and reception of signals in IBFD systems face significant challenges, mainly due to self-interference (SI), which severely impacts system performance. This paper presents a novel framework that combines self-interference cancellation (SIC) techniques with the MIMO-IBFD-ISAC system to enhance multi-target sensing capabilities. The framework utilizes an independent component analysis (ICA) algorithm to effectively separate self-interference from the desired signals. Additionally, a specialized multi-targets detection method is employed to recover radar signals reflected from targets accurately. Simulation results demonstrate that the proposed approach improves target detection accuracy and communication efficiency, making it a viable solution for next-generation ISAC systems. © 2024 IEEE.

关键词： Radar interference

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Deception Detection Deep Learning Comprehensive system Utilizing Explainable AI 16

Deception Detection Deep Learning Comprehensive system Utili...

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16th International Conference on Developments in eSystems engineering, DeSE 2023

作者： Salah, Suhaib Khater, Tarek Almajali, Eqab Khan, Wasiq Hussain, Abir University of Sharjah Dept. of Electrical Engineering Sharjah United Arab Emirates Liverpool John Moores University Department of Computer Science Liverpool United Kingdom

ISBN: (纸本)9798350381344

Deception detection plays a vital role in various domains, from security and law enforcement to human behavior analysis. In this paper, we propose a comprehensive system for deception detection that leverages S&A smart sensing device, deep transfer learning, deep learning techniques, and explainable artificial intelligence. Our approach combines visual, auditory, thermal, cardiovascular, and respiratory cues, offering enhanced accuracy and resistance to countermeasures. Deep Transfer Learning is employed to adapt pre-trained models to the deception detection task, overcoming data limitations. Incorporating Explainable AI techniques enhances transparency and interpretability, fostering trust and collaboration in human-machine interactions. Our research lays the groundwork for future advancements in deception detection technology, addressing challenges and providing promising opportunities in the realm of deception detection. © 2023 IEEE.

关键词： Deep learning

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IoT-Enhanced Smart Bulletin Board: A Multi-Functional Connectivity Hub

IoT-Enhanced Smart Bulletin Board: A Multi-Functional Connec...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Mallikarjuna Rao Gundavarapu VeerlapalIi Swapna Enugu Sriamsh Reddy Pindi Praveen Kumar Abhinay Prathapani Kothapally Shivaji Dept. of Computer Science and Engineering Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India

The problem with traditional notice boards is their static nature, leading to outdated information and manual effort to update. An IoT-based smart notice board solves this by providing real-time updates but requires addressing challenges such as connectivity, power management, compatibility, user interface, security, and maintenance. Addressing these challenges ensures an effective and user-friendly smart notice board. The proposed model involves using an LED dot matrix, ESP32, Kodular app, Firebase Cloud and DS3231 module. The app sends messages to the Firebase then it passes the message to ESP32, which displays message on the LED dot matrix, We use Firebase cloud which provides infinite range. Firebase provides a NoSQL database that allows developers to store and synchronize data in real-time. It is suitable for applications that require real-time updates, such as chat apps, collaborative tools, and live dashboards. The DS3231 module shows real-time information. This study implements a feature that allows the user to retrieve the current time and temperature by pressing a button in the Kodular app. When the button is pressed, a command is sent to the ESP32, which retrieves the relevant data from the DS3231 module and sends it to display. This study creates a buffer to store messages, accessible by indexes, allowing easy retrieval. The proposed system provides a comprehensive solution for displaying messages, real-time data, and efficient message storage. QR codes are also provided to scan.

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Metric Learning from Limited Pairwise Preference Comparisons

arXiv

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arXiv 2024年

作者： Wang, Zhi So, Geelon Vinayak, Ramya Korlakai Dept. of Electrical and Computer Engineering University of Wisconsin–Madison United States Dept. of Computer Science and Engineering University of California San Diego United States

We study metric learning from preference comparisons under the ideal point model, in which a user prefers an item over another if it is closer to their latent ideal item. These items are embedded into Rd equipped with an unknown Mahalanobis distance shared across users. While recent work shows that it is possible to simultaneously recover the metric and ideal items given O(d) pairwise comparisons per user, in practice we often have a limited budget of o(d) comparisons. We study whether the metric can still be recovered, even though it is known that learning individual ideal items is now no longer possible. We show that in general, o(d) comparisons reveal no information about the metric, even with infinitely many users. However, when comparisons are made over items that exhibit low-dimensional structure, each user can contribute to learning the metric restricted to a low-dimensional subspace so that the metric can be jointly identified. We present a divide-and-conquer approach that achieves this, and provide theoretical recovery guarantees and empirical validation. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Built-Up and Land Use Land Cover Mapping for Sustainable Planning in Himalayan Region by Using Machine Learning and Sentinel-2 Satellite Imagery

Built-Up and Land Use Land Cover Mapping for Sustainable Pla...

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Electronics, Communication and Signal Processing (ICECSP), International Conference on

作者： Suraj Singh Rashmi Saini Dept. of Computer Science and Engineering Govind Ballabh Pant Institute of Engineering and Technology India

ISBN: (数字)9798350364590

ISBN: (纸本)9798350375381

The Himalaya region, known for its ecological sensitivity, also faces recurring natural hazards such as earthquakes, landslides, sinking, glacier bursts, and flash floods, which necessitate the implementation of effective planning and management strategies to ensure sustainable development. This study focuses on Joshimath, a subdivision of Chamoli district in Uttarakhand, India, situated in the Greater Himalaya region, covering an area of 4563.52 km 2 , Our objective is to map built-up areas using Machine learning techniques and Sentinel-2 satellite multispectral imagery to support sustainable planning initiatives. Two machine learning models, Random Forest (RF) and Support Vector Machine (SVM), were employed in this study. RF achieved an overall accuracy of 92.62% with a kappa value of 0.911, while SVM achieved an accuracy of 90.557 with a kappa value of 0.886. Precision and recall metrics for the built-up class were calculated, with RF achieving 94% precision and 83.22% recall, and SVM achieving 93.48% precision and 82.24% recall. Additionally, the total area classified as built-up by RF was 236.96 km 2 , whereas SVM classified 198.67 km 2 . The results demonstrate the effectiveness of machine learning techniques, particularly RF and SVM, in accurately mapping built-up areas in the Himalaya region. This information is crucial for sustainable planning and management practices, enabling stakeholders to make informed decisions regarding land use and development while preserving the ecological integrity of the region.

关键词： Support vector machines Radio frequency Accuracy Biological system modeling Vectors Planning Satellite images Stakeholders Sustainable development Random forests

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Real-Time Face Mask Detector 3

Real-Time Face Mask Detector

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3rd IEEE International Conference on Technology, engineering, Management for Societal Impact using Marketing, Entrepreneurship and Talent, TEMSMET 2023

作者： Patel, Vishwajeet Guru Prasad, M.S. Aditya Pai, H. Kumar, A Suresh Praveen Gujjar, J. Naveen Kumar, H.N. Dept. of Computer Science and Engg. Dehradun India JAIN [Deemed-to-be University Faculty of Management Studies Bengaluru India Vidyavardhaka College of Engineering Dept. of Electronics and Communication Engg. Karnataka Mysuru India

ISBN: (纸本)9798350397819

When the COVID-19 pandemic was present we all have faced various challenges in our everyday lives. Pandemic created worldwide emergency which led to the lockdown in most of the countries resulting into disruption in world trade and movement. To reduce the covid cases WHO recommended to wear mask and maintain social distancing and then these steps became mandatory for some places, such as restaurants, hospitals, grocery stores etc., The purpose of limiting interaction with individuals as much as possible in such situations is simply defeated by having a staff personally check each person to make sure their mask is on. To avoid this dilemma, it became necessary to build a project named 'Real-time face mask detector', This reveals whether a mask-wearing individual is there. This project needed the use of some python libraries like TensorFlow, Keras, OpenCV. Using this project, we will be able to Detect mask-on faces in live video clip with accuracy of 97-98 %. © 2023 IEEE.

关键词： COVID-19

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Borneo Smart Forest Information System for Management of Dipterocarp Plants in Kalimantan Rainforest 8

Borneo Smart Forest Information System for Management of Dip...

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8th IEEE Information Technology International Seminar, ITIS 2022

作者： Wati, Masna Puspitasari, Novianti Hairah, Ummul Widians, Joan Angelina Septiarini, Anindita Tjikoa, Ade Fiqri Universitas Mulawarman Faculty of Engineering Dept. of Informatics Samarinda Indonesia Universitas Gadjah Mada Dept. of Computer Science and Electronics Yogyakarta Indonesia

ISBN: (纸本)9798350398199

Kalimantan's tropical rainforest is home to indigenous plants of the Dipterocarpaceae family (tribe), which is renowned for having the most endemic species. Dipterocarp is a family of pantropical plants, many of which are used in the wood industry. This study aims to provide information on the potential of Dipterocarp in Kalimantan forests. Education and conservation efforts for Dipterocarp trees, a prominent component of the forest ecosystem, are crucial in light of the significant harm done to Kalimantan's rainforests. The authors develop the Borneo Smart Forest information system. This system digitally organizes data on the diversity of Dipterocarp in the Kalimantan rainforest based on Web and QR-Code technology. Borneo Smart Forest (BSF) system for monitoring plant inventory and plant markers can be read by smartphones using QR codes. It gives visitors a comprehensive overview of the plants. A QR code is a printed, two-dimensional barcode with limited space for data storage. This technology allows the plant to show earlier administrator-made additions to the inventory information system. The QR code will direct the user to the BSF website, which has data about the plants. The development of this system is one of the efforts to support the achievement of Dipterocarp conservation. © 2022 IEEE.

关键词： Information systems

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