IEEE Open Journal of the Computer Society最新文献

{"title":"Enhancing Cross-Language Multimodal Emotion Recognition With Dual Attention Transformers","authors":"Syed Aun Muhammad Zaidi;Siddique Latif;Junaid Qadir","doi":"10.1109/OJCS.2024.3486904","DOIUrl":"https://doi.org/10.1109/OJCS.2024.3486904","url":null,"abstract":"Despite the recent progress in emotion recognition, state-of-the-art systems are unable to achieve improved performance in cross-language settings. In this article we propose a Multimodal Dual Attention Transformer (MDAT) model to improve cross-language multimodal emotion recognition. Our model utilises pre-trained models for multimodal feature extraction and is equipped with dual attention mechanisms including graph attention and co-attention to capture complex dependencies across different modalities and languages to achieve improved cross-language multimodal emotion recognition. In addition, our model also exploits a transformer encoder layer for high-level feature representation to improve emotion classification accuracy. This novel construct preserves modality-specific emotional information while enhancing cross-modality and cross-language feature generalisation, resulting in improved performance with minimal target language data. We assess our model's performance on four publicly available emotion recognition datasets and establish its superior effectiveness compared to recent approaches and baseline models.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"5 ","pages":"684-693"},"PeriodicalIF":0.0,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10736634","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142663505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Auditable, Privacy-Preserving, Transparent Unspent Transaction Output Model for Blockchain-Based Central Bank Digital Currency","authors":"Md. Mainul Islam;Hoh Peter IN","doi":"10.1109/OJCS.2024.3486193","DOIUrl":"https://doi.org/10.1109/OJCS.2024.3486193","url":null,"abstract":"Auditability, privacy, transparency, and resiliency are four essential properties of a central bank digital currency (CBDC) system. However, it is difficult to satisfy these properties at once. This issue has become a crucial challenge to ongoing CBDC projects worldwide. In this article, we propose a novel unspent transaction output (UTXO) model, which offers auditable, privacy-preserving, transparent CBDC payments in a consortium blockchain network. The proposed model adopts a high-speed, non-interactive zero-knowledge proof scheme named zero-knowledge Lightweight Transparent ARgument of Knowledge (zk-LTARK) scheme to verify the ownership of UTXOs. The scheme provides low-latency proof generation and verification while maintaining 128-bit security with a smaller proof size. It also provides memory-efficient, privacy-preserving multi-party computation and multi-signature protocols. By using zk-LTARKs, users do not require numerous private–public key pairs to preserve privacy, which reduces risks in key management. Decentralized identifiers are used to authenticate users without interacting with any centralized server and avoid a single point of failure. The model was implemented in a customized consortium blockchain network with the proof-of-authority consensus algorithm.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"5 ","pages":"671-683"},"PeriodicalIF":0.0,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10734236","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142663504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Video-Based Deception Detection via Capsule Network With Channel-Wise Attention and Supervised Contrastive Learning","authors":"Shuai Gao;Lin Chen;Yuancheng Fang;Shengbing Xiao;Hui Li;Xuezhi Yang;Rencheng Song","doi":"10.1109/OJCS.2024.3485688","DOIUrl":"https://doi.org/10.1109/OJCS.2024.3485688","url":null,"abstract":"Deception detection is essential for protecting the public interest and maintaining social order. Its application in various fields helps to establish a safer and trustworthy social environment. This study focuses on the problem of deception detection in videos and proposes a visual deception detection method based on a capsule network (DDCapsNet). The DDCapsNet model predicts deception classification using the fusion of facial expression features and video-based heart rate feature via a channel attention mechanism. Supervised contrastive learning is further introduced to enhance the generalization ability of the DDCapsNet. The proposed model is evaluated on a self-collected dataset (physiological-assisted visual deception detection dataset, PV3D) and the public Bag-of-Lies (BOL) dataset, respectively. The results show that DDCapsNet outperforms the unimodal system and other state-of-the-art (SOTA) methods, where the ACC reaches 77.97% and the AUC reaches 78.45% on PV3D, and the ACC reaches 73.19% and the AUC reaches 72.78% on BOL dataset.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"5 ","pages":"660-670"},"PeriodicalIF":0.0,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10734158","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142598655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Innovative Dense ResU-Net Architecture With T-Max-Avg Pooling for Advanced Crack Detection in Concrete Structures","authors":"Ali Sarhadi;Mehdi Ravanshadnia;Armin Monirabbasi;Milad Ghanbari","doi":"10.1109/OJCS.2024.3481000","DOIUrl":"https://doi.org/10.1109/OJCS.2024.3481000","url":null,"abstract":"Computer vision which uses Convolutional Neural Network (CNN) models is a robust and accurate tool for precise monitoring and pixel-level detection of potential damage in concrete structures. Using a state-of-the-art Dense ResU-Net model integrated with T-Max-Avg pooling layers, the present study introduces a novel and effective method for crack detection in concrete structures. The major innovation of this research is the introduction of the T-Max-Avg pooling layer within the Dense ResU-Net architecture which synergistically combines the strengths of both max and average pooling to improve feature retention and minimize information loss during crack detection. In addition, the incorporation of Residual and Dense blocks within the U-Net framework significantly enhances feature extraction and network depth, resulting in a more robust anomaly detection. The implementation of extensive data augmentation techniques improves the robustness of the model while the application of spatial dropout and L2 regularization techniques prevents overfitting. The proposed model showed a superior performance, outperforming traditional and state-of-the-art models. It had a Dice Coefficient score of 97.41%, an Intersection-over-Union (IoU) score of 98.63%, and an accuracy of 99.2% using a batch size of 32. These results confirmed the reliability and efficacy of the Dense ResU-Net with T-Max-Avg pooling layer for accurate crack detection, demonstrating its potential for real-world applications in structural health monitoring. By taking advantage of advanced deep learning techniques, the proposed method addressed the limitations of traditional crack detection techniques and offered significant improvements in robustness and accuracy.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"5 ","pages":"636-647"},"PeriodicalIF":0.0,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10720206","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142555104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MusicTalk: A Microservice Approach for Musical Instrument Recognition","authors":"Yi-Bing Lin;Chang-Chieh Cheng;Shih-Chuan Chiu","doi":"10.1109/OJCS.2024.3476416","DOIUrl":"https://doi.org/10.1109/OJCS.2024.3476416","url":null,"abstract":"Musical instrument recognition is the process of using machine learning or audio signal processing to identify and classify different musical instruments from an audio recording. This capability enables more precise analysis of musical pieces, aiding in tasks like transcription, music recommendation, and automated composition. The challenges include (1) recognition models not being accurate enough, (2) the need to retrain the entire model when a new instrument is added, and (3) differences in audio formats that prevent direct usage. To address these challenges, this article introduces MusicTalk, a microservice based musical instrument (MI) detection system, with several key contributions. Firstly, MusicTalk introduces a novel patchout mechanism named Brightness Characteristic Based Patchout for the ViT algorithm, which enhances MI detection accuracy compared to existing solutions. Secondly, MusicTalk integrates individual MI detectors as microservices, facilitating efficient interaction with other microservices. Thirdly, MusicTalk incorporates an audio shaper that unifies diverse music open datasets such as Audioset, Openmic-2018, MedleyDB, URMP, and INSTDB. By employing Grad-CAM analysis on Mel-Spectrograms, we elucidate the characteristics of the MI detection model. This analysis allows us to optimize ensemble combinations of ViT with patchout and CNNs within MusicTalk, resulting in high accuracy rates. For instance, the system achieves precision and recall rates of 96.17% and 95.77% respectively for violin detection, which are the highest among previous approaches. An additional advantage of MusicTalk lies in its microservice-driven visualization capabilities. By integrating MI detectors as microservices, MusicTalk enables seamless visualization of songs using animated avatars. In a case study featuring “Peter and the Wolf,” we demonstrate that improved MI detection accuracy enhances the visual storytelling impact of music. The overall F1-score improvement of MusicTalk over previous approaches for this song is up to 12%.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"5 ","pages":"612-623"},"PeriodicalIF":0.0,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10709650","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142518112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Polarity Classification of Low Resource Roman Urdu and Movie Reviews Sentiments Using Machine Learning-Based Ensemble Approaches","authors":"Muhammad Ehtisham Hassan;Iffat Maab;Masroor Hussain;Usman Habib;Yutaka Matsuo","doi":"10.1109/OJCS.2024.3476378","DOIUrl":"https://doi.org/10.1109/OJCS.2024.3476378","url":null,"abstract":"The complex linguistic characteristics and limited resources present sentiment analysis in Roman Urdu as a unique challenge, necessitating the development of accurate NLP models. In this study, we investigate the performance of prominent ensemble methods on two diverse datasets of UCL and IMDB movie reviews with Roman Urdu and English dialects, respectively. We perform a comparative examination to assess the effectiveness of ensemble techniques including stacking, bagging, random subspace, and boosting, optimized through grid search. The ensemble techniques employ four base learners (Support Vector Machine, Random Forest, Logistic Regression, and Naive Bayes) for sentiment classification. The experiment analysis focuses on different N-gram feature sets (unigrams, bigrams, and trigrams), Chi-square feature selection, and text representation schemes (Bag of Words and TF-IDF). Our empirical findings underscore the superiority of stacking across both datasets, achieving high accuracies and F1-scores: 80.30% and 81.76% on the UCL dataset, and 90.92% and 91.12% on the IMDB datasets, respectively. The proposed approach has significant performance compared to baseline approaches on the relevant tasks and improves the accuracy up to 7% on the UCL dataset.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"5 ","pages":"599-611"},"PeriodicalIF":0.0,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10707202","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142517837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Verifiable Random Function Schemes Based on SM2 Digital Signature Algorithm and its Applications for Committee Elections","authors":"Yongxin Zhang;Jiacheng Yang;Hong Lei;Zijian Bao;Ning Lu;Wenbo Shi;Bangdao Chen","doi":"10.1109/OJCS.2024.3463649","DOIUrl":"https://doi.org/10.1109/OJCS.2024.3463649","url":null,"abstract":"A verifiable random function (VRF) is a pseudorandom function that enables source verification. By providing a public verification key and accompanying proof with the output, all parties can verify the correctness of the output without interaction. VRF has gained widespread adoption in blockchain applications, including Algorand, Ouroboros, and ChainLink. This article introduces SM2VRF, the first VRF based on the Chinese standard SM2 cryptographic algorithm, and extends it to a batch construction called SM2VRF-B for efficient verification of multiple sources. We showcase the applicability of SM2VRF in an electronic random committee election scenario, where the blockchain is utilized for storing candidate parameters and votes. By employing the Hamming distance, our scheme eliminates the risk of election failure. We provide a security proof for the proposed scheme, followed by an evaluation of the performance of both SM2VRF and SM2VRF-B. We implement our committee election scheme with Ethereum to assess the feasibility and efficiency.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"5 ","pages":"480-490"},"PeriodicalIF":0.0,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10699362","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142377066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Scalability and Security of Blockchain-Empowered Metaverse: A Survey","authors":"Huawei Huang;Zhaokang Yin;Qinglin Yang;Taotao Li;Xiaofei Luo;Lu Zhou;Zibin Zheng","doi":"10.1109/OJCS.2024.3468445","DOIUrl":"https://doi.org/10.1109/OJCS.2024.3468445","url":null,"abstract":"Metaverse brings unlimited space and tremendous potential since it is an integrated application of multiple fundamental technologies such as artificial intelligence, blockchain, networking, Internet of Things, and interactivity. During those building blocks of metaverse, blockchain is a type of technology operated by a group of individual participants and known for its immutability feature. The massive adoption of blockchain has been severely prevented by various security and scalability issues in blockchain-based applications due to the inherent characteristics of this technology. To accelerate the massive adoption of blockchain, many previous studies have been carried out to address the security and scalability issues. This article reviews blockchain-related publications collected from four major security conferences (i.e., NDSS, CCS, S&P, and USENIX Security) published in the past three years. Through this overview, we disclose the security and scalability issues of mainstream blockchains such as Bitcoin and Ethereum. Our study aims to help researchers better understand the bottleneck of blockchain-empowered metaverse, and how to address user requirements for security and scalability from the perspective of blockchains.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"5 ","pages":"648-659"},"PeriodicalIF":0.0,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10695094","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142565541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Photogenic Guided Image-to-Image Translation With Single Encoder","authors":"Rina Oh;T. Gonsalves","doi":"10.1109/OJCS.2024.3462477","DOIUrl":"https://doi.org/10.1109/OJCS.2024.3462477","url":null,"abstract":"Image-to-image translation involves combining content and style from different images to generate new images. This technology is particularly valuable for exploring artistic aspects, such as how artists from different eras would depict scenes. Deep learning models are ideal for achieving these artistic styles. This study introduces an unpaired image-to-image translation architecture that extracts style features directly from input style images, without requiring a special encoder. Instead, the model uses a single encoder for the content image. To process the spatial features of the content image and the artistic features of the style image, a new normalization function called Direct Adaptive Instance Normalization with Pooling is developed. This function extracts style images more effectively, reducing the computational costs compared to existing guided image-to-image translation models. Additionally, we employed a Vision Transformer (ViT) in the Discriminator to analyze entire spatial features. The new architecture, named Single-Stream Image-to-Image Translation (SSIT), was tested on various tasks, including seasonal translation, weather-based environment transformation, and photo-to-art conversion. The proposed model successfully reflected the design information of the style images, particularly in translating photos to artworks, where it faithfully reproduced color characteristics. Moreover, the model consistently outperformed state-of-the-art translation models in each experiment, as confirmed by Fréchet Inception Distance (FID) and Kernel Inception Distance (KID) scores.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"5 ","pages":"624-635"},"PeriodicalIF":0.0,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10694773","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142517855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing Fault Tolerance in High-Performance Computing: A Real Hardware Case Study on a RISC-V Vector Processing Unit","authors":"Marcello Barbirotta;Francesco Minervini;Carlos Rojas Morales;Adrian Cristal;Osman Unsal;Mauro Olivieri","doi":"10.1109/OJCS.2024.3468895","DOIUrl":"https://doi.org/10.1109/OJCS.2024.3468895","url":null,"abstract":"High-Performance Computing (HPC) systems are designed for large-scale processing and complex dataset analysis leveraging scalability, efficiency, and parallelism, often integrating specialized hardware structures such as Vector Processing Units (VPUs). As these systems have grown in complexity and scale, their vulnerability to errors and failures has become an important and complex issue in the HPC world. Our research addresses this challenge by exploring and implementing advanced fault tolerance techniques inside the Vitruvius+ architecture, a partial out-of-order Vector Processing Unit. To the best of our knowledge, this is the first full RTL-level implementation of instruction replication in an HPC-class vector processor for reliability. Specifically, we investigate the integration and interaction of redundancy mechanisms inside the most sensitive architectural units, obtaining a reduction of 75% in non-silent faults causing system failure, proven by an extensive fault injection simulation campaign, with a hardware overhead of only 7.5% and a negligible variation in clock frequency.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"5 ","pages":"553-565"},"PeriodicalIF":0.0,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10694791","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142397274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}