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Poster: VeriCharge: Towards A Formal Framework for Designing and Verifying Secure and Transparent Vehicle-to-Vehicle Transaction Platforms 2

Poster: VeriCharge: Towards A Formal Framework for Designing...

引用

2nd IEEE International Conference on Mobility, Operations, Services and Technologies, MOST 2024

作者： Wang, Yuxuan Makar, Brendan Chen, Jingshu Oakland University Dept. of Computer Science RochesterMI United States

ISBN: (纸本)9798350307733

In the rapidly advancing domain of electric vehicle (EV) technology, Vehicle-to-Vehicle (V2V) charging emerges as a key innovation, enabling EVs with excess energy to act as mobile charging units. In this work, we propose integrating smart contracts into V2V systems to secure and transparent transactions, addressing the current lack of a strategic framework for buyer-seller bidding processes. We introduce a lightweight formal framework leveraging smart contract-based platforms and the Rubinstein bargaining model to ensure transactional integrity. Our framework, utilizing a Discrete Time Markov Chain model, evaluates various bidding strategies, identifying optimal approaches for both parties. © 2024 IEEE.

关键词： Smart contract

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Media Competence is the key requirement when using Generative AI in Academic Education in a meaningful way

Media Competence is the key requirement when using Generativ...

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2024 IEEE International Conference of Artificial Intelligence x Humanities, Education, and Art, AIxHEART 2024

作者： Schimanke, Florian Dept. of Computer Science HSW University of Applied Sciences Hameln Germany

ISBN: (纸本)9798350391015

With the release of ChatGPT, Pandora's box of generative AI has been opened and the new technology is here to stay. This also impacts academic education, where it provides new opportunities while also bearing certain challenges. Students and educators alike will have to adapt to the newly available technology and find ways to use it in a meaningful and profitable way. Summarized, this means that both parties need to develop media competence in using generative AI on their end in academic education. New ways of learning and teaching are required, as well as a change in how educators grade the students' learning status. While the latter should be made aware of the opportunities that generative AI provides them with, educators should actively make use of it in their courses in order to enhance their teaching. © 2024 IEEE.

关键词： Generative adversarial networks

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Biomimetic and Biological Sensing Technologies for the Assessment of Water Contaminants 1

Biomimetic and Biological Sensing Technologies for the Asses...

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1st International Conference on Advanced Computing and Emerging Technologies, ACET 2024

作者： Thaluka, Mohan Raj Maheswari, K. Nuthanakanti, Bhaskar Srujan Raju, K. Jonnadula, Narasimharao Okali, Rakshita Dept. Computer science and engineering CMR Technical Campus Hyderabad India

ISBN: (纸本)9798350367720

Biological sensors, including biosensors and biomimetic sensors, have gained significant attention in recent years due to their exceptional capabilities in detecting and monitoring pollutants in water environments. These sensors employ biological components such as bacteria, Fungi, enzymes, antibodies, microbial cells, or biomimetic materials to recognize and quantify specific contaminants to pathogens, with high sensitivity and selectivity. This review provides an in-dept. analysis of the current state-of-the-art in biological sensor technology for water quality assessment. It discusses the underlying principles of biological sensing mechanisms and explores recent advances in sensor fabrication, including materials, immobilization techniques, and signal transduction methods. Moreover, the review examines the diverse applications of biological sensors in water monitoring, ranging from detecting chemical pollutants to assessing microbial contamination. Additionally, challenges such as stability, reproducibility, and field deployment are addressed, along with future directions for research and development to further enhance the performance and applicability of biological sensors in safeguarding water resources and public health. Here, we employed three models - SVM, logistic regression (LR), and random forest (RF) - to determine the biological and chemical components of water, such as bacteria, fungi, enzymes, antibodies, microbial cells, or biomimetic materials, in order to determine whether it is being utilized for drinking water. Because there are fewer datasets available for biological and chemical components, the dataset was created using machine learning (ML) models. Three machine learning models were used to calculate water pollutants percentages, with SVM proving most effective for irrigation and domestic water, and LR for drinking water, resulting in accurate water supply. © 2024 IEEE.

关键词： Antibodies

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Multi-Musical Instrument Recognition Neural Network 6

Multi-Musical Instrument Recognition Neural Network

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6th World Symposium on Artificial Intelligence, WSAI 2024

作者： Timouri, Bardia Aulakh, Ravdeep Cruz, Gathrean Dela British Columbia Institute of Technology Dept. of Computer Science Vancouver Canada

ISBN: (纸本)9798350374162

Artificial intelligence continues to evolve, particularly in the realms of natural language processing (LLMs), image generation, and task automation. Despite these advancements, multi-musical instrument recognition remains a challenging area with limited effective solutions. Addressing this, our research introduces an innovative methodology using a convolutional neural network (CNN) embedded within an artificial neural network framework. This method utilizes the OpenMIC-2018 dataset, meticulously refined for our purposes. We process entire songs by converting them into mel-spectrograms, which are instrumental in distinguishing subtle variations in pitch, dynamics, and timbre. Our approach sets a new benchmark in the field, adept.y identifying and categorizing up to 10 distinct musical instruments within complex audio recordings. It boasts an impressive F1 score of 56%. This significant achievement not only advances audio signal processing but also highlights the versatility and effectiveness of CNNs in handling sophisticated tasks like multi-instrument recognition. Our work serves as a stepping stone for future explorations in audio recognition, potentially paving the way for more nuanced and accurate audio analysis in various applications, from music production to sound engineering. © 2024 IEEE.

关键词： Spectrographs

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Augmenting Sentiments into Chat-GPT Using FacialEmotion Recognition 10

Augmenting Sentiments into Chat-GPT Using FacialEmotion Reco...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Iyer, Aditya A. Vojjala, Saipranav Andrew, J. Manipal Institute of Technology Dept. of Computer Science and Engineering Manipal India

ISBN: (纸本)9798350384369

This research initiative addresses the task of enhancing Chat Generative Pre-trained Transformer's (ChatGPT's) conversational capabilities by integrating the comprehension and response to user emotions conveyed through facial expressions. The central challenge lies in refining the AI system's proficiency to tailor responses according to users' detected emotional states. In this pursuit, our study adopts a comprehensive approach, aiming to seamlessly incorporate emotional intelligence into AI-driven interactions. To achieve this objective, the methodology involves integrating real-time sentiment analysis based on facial expressions into the ChatGPT framework. This is carried out through the utilization of a deep convolutional neural network (DCNN) architecture, designed to recognize and interpret various emotions exhibited in facial expressions. The primary goal is to enable ChatGPT to dynamically adjust its responses, fostering a more empathetic and contextually relevant interaction with users. In terms of evaluation metrics for facial expression recog- nition, our assessment employs a confusion matrix to quantify the model's performance across different emotional categories. Additionally, a heuristic approach is implemented, wherein the sum total probability of each detected emotion is calculated over the duration the user enters the prompt. These evaluation methodologies aim to provide a comprehensive understanding of the model's accuracy and effectiveness in discerning and responding to user emotions. Overall, this research contributes to the ongoing endeavor of imbuing AI systems with emotional intelligence, paving the way for more nuanced and human-like interactions. © 2024 IEEE.

关键词： Chatbots

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Comprehensive Testing Strategies for Water Purity by Assessing and Eliminating Viruses and Bacteria using Machine Learning algorithms

Comprehensive Testing Strategies for Water Purity by Assessi...

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2024 International Conference on Communication, computer sciences and Engineering, IC3SE 2024

作者： Thaluka, Mohan Raj Maheswari, K. Srujan Raju, K. Nuthanakanti, Bhaskar Pavan Kumar, G. Santhuja, P. Cmr Technical Campus Dept. Of Computer Science And Engineering Hyderabad India

ISBN: (纸本)9798350366846

Water, essential for life, is often contaminated with bacteria and viruses, depriving many accesses towards purified drinking and irrigation water. The presence of pathogens in unpurified water puts human health at risk and contaminates crops, leading to waterborne illnesses. Detecting indicator bacteria and viruses in water is crucial for assessing microbiological quality. Despite efforts by regulatory authorities, disease outbreaks linked to drinking water persist globally. The World Health Organization released some threshold values based on some water parameters. The metrics are named as The Water Purity by Assessing and Eliminating Viruses and Bacteria (WPAEVB) and Irrigation WQI (IWQI) which can measure the water quality. This paper proposed a network architecture to analyze all the parameters by using machine learning tools (ML) tools which will determine the drinking water and irrigation water based on virus and bacteria microbiological values. The model is developed based on LoRa and land topology. Here we used three models SVM, logistic regression (LR), and random forest (RF) to know whether irrigation water is being used for drinking water by detecting the percentage and levels of bacteria and virus. The dataset was developed based on the ML models due to the lower availability of datasets related to irrigation and drinking water and bacteria and virus percentage is also calculated by using three models. Following the application of every model, SVM produced the greatest results for irrigation water, while LR produced the best performance for drinking water. The three ML models were applied in order to complete the iterative feature elimination. © 2024 IEEE.

关键词： Bacteria

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Measurement In DevOps Standard: Proposed Improvements 33

Measurement In DevOps Standard: Proposed Improvements

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Joint of the 33rd International Workshop on Software Measurement and the 18th International Conference on Software Process and Product Measurement, IWSM-MENSURA 2024

作者： Lapointe-Boisvert, Alexandra Trudel, Sylvie Desharnais, Jean-Marc Dept. of Computer Science UQAM Montreal Canada ÉTS Montreal Canada

Context. DevOps is about improving the software delivery performance. Lord Kelvin stated: "If you can not measure it, you can not improve it." For this reason, well-defined measurement is particularly important when using DevOps. Method. This paper critically examines the measurement process outlined in the ISO/IEC/IEEE-32675 DevOps standard and proposes several improvements based on measurement-related standards (ISO-15939, ISO-25021, VIM, etc.). Findings. The measures (base or derived) stated in the DevOps standard are neither characterized nor defined, as described in several standards. Some measures given as examples are ambiguous. Also, the DevOps standard claims to follow ISO/IEC/IEEE-12207, but some expected measures are missing (e.g. effort and size). This article proposes a way to define the different measures and explains why having effort and size measures is important in the context of DevOps. Conclusion. The actual measurement process, as described in ISO/IEC/IEEE-32675, could be improved by better defining the base measures, including effort and size, using a measurement method, defining the derived measures using a measurement function, and avoiding ambiguous measures. © 2024 Copyright for this paper by its authors.

关键词： ISO Standards

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Tiny Object Detection for Marine Search and Rescue with YOLOv8n-Tiny 15

Tiny Object Detection for Marine Search and Rescue with YOLO...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Nautiyal, Rakhi Deshmukh, Maroti NIT Uttarakahand Dept. of Computer Science and Engg Garhwal Srinagar India

ISBN: (纸本)9798350370249

Marine Search and Rescue (MSAR) operations are invaluable and vital in maritime traffic management, marine environment monitoring, and maritime security and defense efforts. Their prompt reactions also improve our marine environment's security, safety, and sustainability. Accurately identifying tiny objects in images, such as boats, people, and swimmers, is essential for MSAR missions. We recognize the necessity for optimization in detecting particular categories of challenging-to-identify tiny objects present within images. However, MSAR operations come across challenges including the size of tiny objects, complex sea surface backgrounds, low signal-to-noise ratios, and the diversity and density of tiny sea target objects. To address these challenges, we proposed a modified YOLOv8-based model for classification and detection purposes. For experiments, we have selected the SeaDronesSee dataset to serve as the foundation of our research. For classification, the YOLOv8l-Tiny model demonstrates better results when compared to YOLOv8nTiny. On the other hand, the YOLOv8n-Tiny model outperforms the YOLOv8s-Tiny and YOLOv8m-Tiny models in detection tasks. The experimental findings indicate that a classification accuracy of 100% was achieved with losses reduced up to 75% using the YOLOv8I-Tiny model. For tiny object detection, the YOLOv8n-Tiny model attained a Precision score of 0.89, a Mean Average Precision (mAP) of 0.27, and an F1 score of 0.45. Additionally, the cls-loss for detecting tiny objects was notably reduced to 0.10. © 2024 IEEE.

关键词： Marine safety

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Narrative Simplification in Human and AI Retelling

Narrative Simplification in Human and AI Retelling

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2024 IEEE International Conference on Big data, Bigdata 2024

作者： Tanaka, Yuuri Hashimoto, Yasuhiro The University of Aizu Dept. Computer Science & Engineering Aizu-Wakamatsu Japan

ISBN: (纸本)9798350362480

Human beings have long been regarded as "homo narrans,"employing storytelling to comprehend and communicate reality. This study investigates whether contemporary large language models (LLMs) can approximate the dept. and richness of human narrative abilities. Through an experiment comparing retellings by humans and various state-of-the-art LLMs on a dataset of man-made short stories, we examine how word count, vocabulary complexity, and readability evolve during the retelling process. We introduce the Flesch Reading Ease score as a quantitative measure of narrative simplicity. Our findings reveal an inverse correlation between narrative condensation and increased vocabulary density in both human and LLM retellings. While both humans and LLMs tend to condense stories, their approaches differ: humans generally maintain or improve readability, whereas LLMs, without specific instructions on persona-based retellings, often reduce readability. However, with appropriate prompts, LLMs can adjust their storytelling to more closely mimic human tendencies. We also identify a unique "adaptive retelling"ability in humans, who adjust readability based on the original story's readability. These insights advance our understanding of the "AI narrans"concept, highlighting both the current limitations and the potential of LLMs in achieving human-like storytelling capabilities. © 2024 IEEE.

关键词： AI narrans Flesch reading ease score large language models narrative Retelling

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Intelligent Video Surveillance System 4

Intelligent Video Surveillance System

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4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Aryan, Ansh Singh, Dharya Pratap Kumar, Nitish Amity University Dept. of Computer Science and Engineering Uttar Pradesh India

ISBN: (纸本)9798350385793

This research study provides the utilization of an intelligent video surveillance system, a sophisticated technology that integrates video cameras, sensors, and cutting-edge software to monitor and analyze activities within a designated area or locale. By seamlessly combining compute r vision and machine learning techniques, it goes beyond conventional video surveillance installations to provide real-time insights along with automated alerts. Intelligent video surveillance systems harness the power of machine learning and computer vision algorithms to analyze video feeds in real-time, empowering them to detect, track, and comprehend objects, individuals, and occurrences captured within the video frames. Furthermore, these systems can recognize a variety of objects, including cars, people, animals, and particular objects, even in complex and crowded settings. They also offer a variety of analytical tools and insights that are very helpful to organizations and security operations, such as traffic patterns, heatmaps, and other data. This research study provides a thorough explanation of the modern uses of machine learning in the field of surveillance systems. Two crucial issue statements are addressed by it in practice: the identification of human falls and the detection of auto accidents. This study's main goal is to demonstrate an end-to-end system that will act as a beacon for law enforcement and medical personnel, giving them the ability to quickly identify traffic accidents and serious injuries and guarantee that the necessary assistance is provided on time. © 2024 IEEE.

关键词： Deep learning

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