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Blockchain-based trust management for data exchange in internet of vehicle network

Multimedia Tools and Applications 2025年第8期84卷 4837-4855页

作者： Srivastava, Sandeep Agarwal, Deepshikha Chaurasia, Brijesh Kumar Adhikari, Mainak Computer Science Indian Institute of Information Technology UP Lucknow226002 India Information Technology Indian Institute of Information Technology UP Lucknow226002 India Computer Science Pranveer Singh Institute of Technology UP Kanpur208029 India School of Data Science Indian Institute of Science Education & Research Thiruvananthapuram India

Internet of Vehicles (IoV) integrates with various heterogeneous nodes, such as connected vehicles, roadside units, etc., which establishes a distributed network. Vehicles are managed nodes providing all the services required during inter-vehicular communication which makes it essential to use a trust management mechanism. Existing trust management systems are insufficient to address the emerging IoV needs of transparency, mobility, immutability, and scalability. Motivated by the need to address the outlined issues, Proposed additive increase and multiplicative decrease (AIMD) trust management model, to improve the quality of communication, as well as vehicle trustworthiness based on multiple inter-vehicular interactions. The proposed layered architecture helps to reduce overhead in terms of latency and average execution time compared to other trust management mechanism and avoid risk of single point of failure. The Proposed AIMD trust management is implemented on permissioned blockchain, where transactions in the IoV network are only managed by minor nodes which accumulate interactions and uses PBFT and AIMD mechanism to derive immutable reputation score securely and transparently. Simulation results show the AIMD trust mechanism using permissioned blockchain can manage the trust of vehicles efficiently by reducing the latency by 0.29 sec, increasing packet delivery ratio from 0.8 to 0.95, and significantly reduce transaction finality time. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Blockchain

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Graph Augmentation Empowered Contrastive Learning for Recommendation

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ACM Transactions on Information Systems 2025年第2期43卷 1-27页

作者： Xu, Lixiang Liu, Yusheng Xu, Tong Chen, Enhong Tang, Yuanyan School of Artificial Intelligence and Big Data Hefei University Hefei China State Key Laboratory of Cognitive Intelligence School of Computer Science and Technology University of Science and Technology of China Hefei China Zhuhai UM Science and Technology Research Institute FST University of Macau China

The application of contrastive learning (CL) to collaborative filtering (CF) in recommender systems has achieved remarkable success. CL-based recommendation models mainly focus on creating multiple augmented views by employing different graph augmentation methods and utilizing these views for self-supervised learning. However, current CL methods for recommender systems usually struggle to fully address the problem of noisy data. To address this problem, we propose the Graph Augmentation Empowered Contrastive Learning (GAECL) for recommendation framework, which uses graph augmentation based on topological and semantic dual adaptation and global co-modeling via structural optimization to co-create contrasting views for better augmentation of the CF paradigm. Specifically, we strictly filter out unimportant topologies by reconstructing the adjacency matrix and mask unimportant attributes in nodes according to the PageRank centrality principle to generate an augmented view that filters out noisy data. Additionally, GAECL achieves global collaborative modeling through structural optimization and generates another augmented view based on the PageRank centrality principle. This helps to filter the noisy data while preserving the original semantics of the data for more effective data augmentation. Extensive experiments are conducted on five datasets to demonstrate the superior performance of our model over various recommendation models. © 2025 Copyright held by the owner/author(s).

关键词： Self-supervised learning

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Importance-Based Pruning for Genetic programming Based Symbolic Regression 37th

Importance-Based Pruning for Genetic Programming Based Symb...

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37th Australasian Joint Conference on Artificial Intelligence, AJCAI 2024

作者： Rimas, Mohamad Chen, Qi Zhang, Mengjie Centre for Data Science and Artificial Intelligence and School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand

ISBN: (纸本)9789819603503

Pruning is a major research field in neural networks, enhancing their efficiency and generalization. The field of pruning approaches in genetic programming (GP) is continually evolving, with researchers actively exploring new techniques and approaches to optimise the performance of GP models. This research introduces a novel pruning algorithm for Genetic programming-based Symbolic Regression (GPSR). The proposed method employs a weighting mechanism to identify and filter out unimportant subtrees in each generation. To achieve this, the method arranges all subtrees linearly and assigns weights to each subtree and terminal. It then uses Ordinary Least Squares (OLS) to optimize these weights, enabling the identification of unimportant subtrees and terminals for effective pruning. The algorithm’s effectiveness was evaluated using ten regression datasets, including high-dimensional and complex feature sets. Furthermore, comparisons were made with two other algorithms to evaluate its performance. The results indicate that the proposed approach not only achieves better learning and generalisation performance but also generates smaller trees compared to standard GP, thereby improving interpretability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Genetic programming Ordinary Least Squares Pruning Regression Subtrees

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AI-Powered Mobile Application for Image-Based Food Ingredient Detection and Recipe Generation 9

AI-Powered Mobile Application for Image-Based Food Ingredien...

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9th International Conference on Information Management and Technology, ICIMTech 2024

作者： Rosaline, Thessalonica Maria Luisse Suciawan, Calvin Anacia Ng, William Achmad, Said Moniaga, Jurike V School of Computer Science Bina Nusantara University Mobile Application & Technology Program Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

This research explores the critical impact of dietary habits on health, addressing the lack of awareness regarding nutritional content that leads to issues such as malnutrition. The proposed mobile application utilizes the YOLOv8 algorithm with a deep convolutional neural network (CNN) architecture, deployed on the Roboflow server, a local MySQL database server, and ChatGPT. The architectural framework of this application is integrated through the NodeJs server via an API system embedded within the Android application. Despite this approach, the study highlights the need for further refinement in the accuracy of the ingredients' object detection model. The refined YOLOv8 model, trained on an extensive dataset of 24,583 images across 13 food ingredient classes, outperforms the initial YOLOv8 model trained on 9,867 images. Improvements are notable, particularly in addressing misclassifications of ingredients such as beef, pork, onion, and egg in the initial model. However, the YOLOv8 model can still be improved by introducing more ingredient variations and expanding the training dataset. This application can detect multiple food ingredients from an image and evaluate their nutritional value, specifically in calorie count. It also provides tailored recipes to help users meet their daily nutritional requirements, offering a practical solution for users to achieve their dietary goals. This research connects computer vision and nutrition, providing insights for developing practical solutions in this domain. © 2024 IEEE.

关键词： Convolutional neural networks

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Sentiment Analysis of Makeup Alley Website Cosmetic Reviews Using SVM and TF-IDF 9

Sentiment Analysis of Makeup Alley Website Cosmetic Reviews ...

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9th International Conference on Information Management and Technology, ICIMTech 2024

作者： Putri, Christine Salim, Claudya Veronica Dwiyanti, W.K. Achmad, Said Moniaga, Jurike V School of Computer Science Bina Nusantara University Mobile Application & Technology Program Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

This paper focuses on performing sentiment analysis of cosmetic reviews using a machine learning algorithm. The study aims to classify reviews into positive, neutral, and negative opinions using aspect-based sentiment analysis with TF-IDF and SVM algorithms. The research questions include understanding user opinions on cosmetic products and identifying the most common positive and negative sentiments in reviews. data cleaning, tokenization, stopwords removal, normalization, and lemmatization techniques are employed to achieve these goals. The study uses the Makeup Alley website as a data source and employs web scraping to obtain the data. The Natural Language Toolkit (NLTK) is used to simplify the removal of stopwords. The research seeks to document the step-by-step process of developing a sentiment analysis application tailored to cosmetic product reviews. © 2024 IEEE.

关键词： Support vector machines

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Multi-scale feature fusion for facial expression recognition

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Neural Computing and Applications 2025年第17期37卷 11399-11420页

作者： Guo, Jiatao Peng, Junjie Huang, Yansong Chen, Gan Cai, Zesu Tan, Shuhua School of Computer Engineering and Science Shanghai University Shanghai China Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai China School of Computer Science and Technology Harbin Institute of Technology Harbin China National Engineering Laboratory for Logistics Information Technology YTO Express Co. Ltd. Shanghai China

As an important computer vision task that can be used in many areas, facial expression recognition (FER) has been widely studied which much progress has been obtained especially when deep learning (DL) approaches have been introduced in FER. However, existing methods are restricted to extracting localized information or utilizing attention mechanism to focus on the region most relevant to the emotion, possibly resulting in errors in facial expression interpretation. Moreover, the inconsistency of image sizes in the wild FER datasets further increases the difficulty of the FER task. To address these issues, we propose a multi-scale feature fusion (MSFF) for FER. Specifically, our method first extracts features from facial images using multi-scale feature extraction to obtain rich multi-dimensional information. Meanwhile, extensive feature extraction at different scales is achieved by expanding the receptive field of the model, which provides the basis for more detailed analysis of facial expressions. Eventually, features at different scales are fused to enhance their synergistic effect. Subsequently, the adaptive extraction of comprehensive and valuable information from the fused features is performed, optimizing the performance of expression recognition. Extensive experimental results demonstrate that the proposed method outperforms the state-of-the-art methods on in-the-wild datasets. It is promising for FER in realistic and complex scenarios. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Feature extraction

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Mental illness detection using sentiment analysis in social media 9th

Mental illness detection using sentiment analysis in social ...

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9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： Odja, Kasimirus Derryl Widiarta, Jasson Purwanto, Eko Setyo Ario, Muhamad Keenan Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Mobile Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

This research tries to detect mental illness using sentiment analysis on Reddit data, as well as comparing the performance of the k-Nearest Neighbors (k-NN), Random Forest, and Neural Network models. Using text post data from the pre-pandemic and post-pandemic periods, we concluded that the Random Forest model had the highest overall performance with an F1 Score, accuracy, recall and precision of 80.6%, making it quite effective in detecting depression. Even though the Neural Network model shows slightly lower accuracy, namely 79%, in fact this model has the lowest error rate, namely 0.06496. The k-NN model showed the lowest accuracy and higher error rate. These findings highlight the potential of sentiment analysis and machine learning in identifying mental health issues on social media and suggest that better models can improve early detection and intervention efforts. © 2024 The Authors.

关键词： Neural network models

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Through the Lens: Unveiling the Power and Promise of Google's Visual Search Technology 9

Through the Lens: Unveiling the Power and Promise of Google'...

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9th International Conference on Information Management and Technology, ICIMTech 2024

作者： Salim, Devano Hofker Susanto, Jose Juan Manalu, Sonya R. Shiddiqi, Hafizh Ash School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Mobile Application & Technology Program Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

The digital landscape is undergoing profound changes, with visual search technology like Google Lens emerging as a pivotal force shaping our interaction with information. This research paper delves into Google Lens, a cutting-edge tool redefining how we engage with visual data. We comprehensively examine its functionalities, underlying principles, and real-world applications, particularly in education and library services. Through meticulous analysis, we evaluate its efficacy in addressing practical challenges and assess its accuracy, efficiency, and breadth of capabilities compared to other visual search technologies. By exploring existing research and the intricacies of Google Lens, this paper aims to provide a holistic understanding of this technology and its potential impact on how we access and interact with information in the real world. Finally, we explore the future of technology by contemplating the prospective roles of Google Lens across various industries and its potential for shaping the future of information access. © 2024 IEEE.

关键词： future of technology google lens visual search technology

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Enhancing User Interface and Experience in Skin Type Classification and Skincare Product Recommendation System for Young Adults 9

Enhancing User Interface and Experience in Skin Type Classif...

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9th International Conference on Information Management and Technology, ICIMTech 2024

作者： Himawan, Felicia Soerjadi, Vania Paramitha Handoyo, Yonathan Achmad, Said Moniaga, Jurike V School of Computer Science Bina Nusantara University Mobile Application & Technology Program Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

The escalating interest of skincare among Indonesian young adults reflects the rising trend in the skincare industry. However, this heightened interest also brings several challenges, like the lack of knowledge and confusion when selecting the right skincare products for our skin type. This underscores the need for an effective and efficient skin type classification and product recommendation system. This paper addresses this challenge by focusing on enhancing a skincare application's user interface and overall experience. This research employed User-Centered Design (UCD), implementing Design Sprint, and Goal-Directed Design (GDD) methodologies to achieve a high value of user satisfaction. The Sprint process is used to create the design prototype efficiently, and the Goal-Directed method is performed to create specific, measurable goals. Our evaluation shows that an iterative design process based on user feedback results in higher user satisfaction. The analysis indicated a significant increase of approximately 25% in both effectiveness and efficiency among users when using the application. Initially, the first iteration reveals a low value of approximately 59% in overall effectiveness and efficiency. However, a substantial boost to around 83.5% was seen after a redesign. This improvement proved that User-Centered Design methodologies highly influence user perception and satisfaction. © 2024 IEEE.

关键词： design sprint goal-directed design skincare UI/UX user-centered design

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Quantitative analysis of sign language translation using artificial neural network model 9th

Quantitative analysis of sign language translation using art...

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9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： Wijaya, Fendy Dahendra, Leonardo Purwanto, Eko Setyo Ario, Muhamad Keenan Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Mobile Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Sign language is one of the technique to support communication with deaf and speech impaired people. Nowadays, human needs become more complex, so are the needs of people with those disabilities. Therefore, with the sophistication of modern technology in the field of computer science, it is necessary to have a sign language translation system which is capable to convert human gestures into words or spelling letters in a natural human language, especially Indonesian language and SIBI sign language. This research is conducted to analyze the capabilities of an Artificial Neural Network (ANN) system to translate SIBI sign language into Indonesian language. The analysis is performed quantitatively from the experiment results to gain a descriptive insights for the generated models. By creating two datasets containing alphabets and words in SIBI sign language, two models were generated to predict alphabets and words, respectively. The result shows that ANN model could effectively and efficiently perform a sign language translation with accuracy of alphabet prediction reached 96.15% and words prediction reached 99.45% in duration of prediction without exceeding 0.15 seconds. Regarding to the results, it can be concluded that ANN model quantitatively suitable to be implemented as an Indonesian sign language translator system. © 2024 The Authors.

关键词： Neural machine translation

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