IEEE Open Journal of the Computer Society最新文献

UVtrack: Multi-Modal Indoor Seamless Localization Using Ultra-Wideband Communication and Vision Sensors

IEEE Open Journal of the Computer Society Pub Date : 2025-01-20 DOI: 10.1109/OJCS.2025.3531442

Yi Xu;Zhigang Chen;Ming Zhao;Fengxiao Tang;Yangfan Li;Jiaqi Liu;Nei Kato

{"title":"UVtrack: Multi-Modal Indoor Seamless Localization Using Ultra-Wideband Communication and Vision Sensors","authors":"Yi Xu;Zhigang Chen;Ming Zhao;Fengxiao Tang;Yangfan Li;Jiaqi Liu;Nei Kato","doi":"10.1109/OJCS.2025.3531442","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3531442","url":null,"abstract":"High precision and robust indoor positioning system has a broad range of applications in the area of mobile computing. Due to the advancement of image processing algorithms, the prevalence of surveillance ambient cameras shows promise for offering sub-meter accuracy localization services. The tracking performance in dynamic contexts is still unreliable for ambient camera-based methods, despite their general ability to pinpoint pedestrians in video frames at fine-grained levels. Contrarily, ultra-wideband-based technology can continuously track pedestrians, but they are frequently susceptible to the effects of non-line-of-sight (NLOS) errors on the surrounding environment. We see a chance to combine these two most viable approaches in order to get beyond the aforementioned drawbacks and return to the pedestrian localization issue from a different angle. In this article, we propose UVtrack, a localization system based on UWB and ambient cameras that achieves centimeter accuracy and improved reliability. The key innovation of UVtrack is a well-designed particle filter which adopts UWB and vision results in the weight update of the particle set, and an adaptive distance variance weighted least squares method (DVLS) to improve UWB sub-system robustness. We take UVtrack into use on common smartphones and test its effectiveness in three different situations. The results demonstrated that UVtrack attains an outstanding localization accuracy of 7 cm.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"272-281"},"PeriodicalIF":0.0,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10845877","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143107126","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}

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2024 List of Reviewers* 2024 年审查员名单*

IEEE Open Journal of the Computer Society Pub Date : 2025-01-14 DOI: 10.1109/OJCS.2025.3527836

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New Incoming EIC Editorial 新到来的EIC社论

IEEE Open Journal of the Computer Society Pub Date : 2025-01-10 DOI: 10.1109/OJCS.2025.3525947

Vincenzo Piuri

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Comparative Analysis of Traditional and Modern NLP Techniques on the CoLA Dataset: From POS Tagging to Large Language Models

IEEE Open Journal of the Computer Society Pub Date : 2025-01-07 DOI: 10.1109/OJCS.2025.3526712

Abdessamad Benlahbib;Achraf Boumhidi;Anass Fahfouh;Hamza Alami

{"title":"Comparative Analysis of Traditional and Modern NLP Techniques on the CoLA Dataset: From POS Tagging to Large Language Models","authors":"Abdessamad Benlahbib;Achraf Boumhidi;Anass Fahfouh;Hamza Alami","doi":"10.1109/OJCS.2025.3526712","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3526712","url":null,"abstract":"The task of classifying linguistic acceptability, exemplified by the CoLA (Corpus of Linguistic Acceptability) dataset, poses unique challenges for natural language processing (NLP) models. These challenges include distinguishing between subtle grammatical errors, understanding complex syntactic structures, and detecting semantic inconsistencies, all of which make the task difficult even for human annotators. In this article, we compare a range of techniques, from traditional methods such as Part-of-Speech (POS) tagging and feature extraction methods like CountVectorizer with Term Frequency-Inverse Document Frequency (TF-IDF) and N-grams, to modern embeddings such as FastText and Embeddings from Language Models (ELMo), as well as deep learning architectures like transformers and Large Language Models (LLMs). Our experiments show a clear improvement in performance as models evolve from traditional to more advanced approaches. Notably, state-of-the-art (SOTA) results were obtained by fine-tuning GPT-4o with extensive hyperparameter tuning, including experimenting with various epochs and batch sizes. This comparative analysis provides valuable insights into the relative strengths of each technique for identifying morphological, syntactic, and semantic violations, highlighting the effectiveness of LLMs in these tasks.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"248-260"},"PeriodicalIF":0.0,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10829978","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106179","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}

引用次数: 0

Optimizing Energy Efficiency in UPA-Assisted SWIPT Massive MIMO Systems Over Rician Fading Channels

IEEE Open Journal of the Computer Society Pub Date : 2025-01-03 DOI: 10.1109/OJCS.2025.3525519

Mohammad Hassan Adeli;Dariush Abbasi-Moghadam;Hossein Fotouhi;S. Mohammad Razavizadeh

{"title":"Optimizing Energy Efficiency in UPA-Assisted SWIPT Massive MIMO Systems Over Rician Fading Channels","authors":"Mohammad Hassan Adeli;Dariush Abbasi-Moghadam;Hossein Fotouhi;S. Mohammad Razavizadeh","doi":"10.1109/OJCS.2025.3525519","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3525519","url":null,"abstract":"Massive Multiple Input Multiple Output (mMIMO) is a promising solution for enabling green communication in next-generation wireless networks. Integrating mMIMO with Simultaneous Wireless Information and Power Transfer (SWIPT) technology can further enhance the system efficiencies in terms of Energy Efficiency (EE) and spectral efficiency. This article studies the feasibility and energy-efficient design of a uniform planar antenna (UPA)-assisted mMIMO-enabled SWIPT system. The downlink transmission of the SWIPT mMIMO system over the Rician fading channels is investigated with terminals harvesting energy based on a nonlinear energy harvesting model. We derive approximate expressions for signal-to-interference-plus-noise Ratio (SINR) and harvested power. Additionally, we formulate an EE optimization problem considering user-level quality of service and total transmit power constraints. To solve this nonconvex problem, we jointly optimize the allocated power and Power Splitting (PS) ratios by exploiting the fractional programming and convex-concave procedure approaches. Results demonstrate the superiority of our proposed design compared to the conventional scenarios with equal power allocation and fixed PS ratio algorithms with about 2 to 5 times EE improvements. The Results also indicate a considerably higher growth rate on EE by increasing the number of antennas and Rician factors compared to the two other methods.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"236-247"},"PeriodicalIF":0.0,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10820514","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143107127","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}

引用次数: 0

Leveraging Deep Learning and Multimodal Large Language Models for Near-Miss Detection Using Crowdsourced Videos

IEEE Open Journal of the Computer Society Pub Date : 2025-01-03 DOI: 10.1109/OJCS.2025.3525560

Shadi Jaradat;Mohammed Elhenawy;Huthaifa I. Ashqar;Alexander Paz;Richi Nayak

{"title":"Leveraging Deep Learning and Multimodal Large Language Models for Near-Miss Detection Using Crowdsourced Videos","authors":"Shadi Jaradat;Mohammed Elhenawy;Huthaifa I. Ashqar;Alexander Paz;Richi Nayak","doi":"10.1109/OJCS.2025.3525560","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3525560","url":null,"abstract":"Near-miss traffic incidents, positioned just above \"unsafe acts\" on the safety triangle theory, offer crucial predictive insights for preventing crashes. However, these incidents are often underrepresented in traffic safety research, which tends to focus primarily on actual crashes. This study introduces a novel AI-based framework designed to detect and analyze near-miss and crash events in crowdsourced dashcam footage. The framework consists of two key components: a deep learning model to segment video streams and identify potential near-miss or crash incidents and a multimodal large language model (MLLM) to further analyze and extract narrative information from the identified events. We evaluated three deep learning models—CNN, Vision Transformers (ViTs), and CNN+LSTM—on a dataset specifically curated for three-class classification (crashes, near-misses, and normal driving events). CNN achieved the highest accuracy (90%) and F1-score (89%) at the frame level. At the event level, ViTs delivered a strong performance with a test accuracy of 77.27% and an F1-score of 67.37%, while CNN+LSTM, although lower in overall performance, demonstrated significant potential with a test accuracy of 78.1% and an F1-score of 68.69%. For a deeper analysis, we applied GPT-4o to process critical safety events (near-misses and crashes), utilizing both zero-shot and few-shot learning for narrative generation and feature extraction. The zero-shot learning method performed better, achieving an accuracy of 81.2% and an F1-score of 81.9%. This study underscores the potential of combining deep learning with MLLMs to enhance traffic safety analysis by integrating near-miss data as a key predictive layer. Our approach highlights the importance of leveraging near-miss incidents to proactively enhance road safety, thereby reducing the likelihood of crashes through early intervention and better event understanding.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"223-235"},"PeriodicalIF":0.0,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10820995","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106743","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}

引用次数: 0

Statistical Validity of Neural-Net Benchmarks 神经网络基准的统计有效性

IEEE Open Journal of the Computer Society Pub Date : 2024-12-26 DOI: 10.1109/OJCS.2024.3523183

Alain Hadges;Srikar Bellur

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Energy Efficiency of Kernel and User Space Level VPN Solutions in AIoT Networks AIoT网络中内核和用户空间级VPN解决方案的能源效率

IEEE Open Journal of the Computer Society Pub Date : 2024-12-26 DOI: 10.1109/OJCS.2024.3522566

ALEKSANDAR JEVREMOVIC;Zona Kostic;Ivan Chorbev;Dragan Perakovic;Andrii Shalaginov;Ivan Cvitic

{"title":"Energy Efficiency of Kernel and User Space Level VPN Solutions in AIoT Networks","authors":"ALEKSANDAR JEVREMOVIC;Zona Kostic;Ivan Chorbev;Dragan Perakovic;Andrii Shalaginov;Ivan Cvitic","doi":"10.1109/OJCS.2024.3522566","DOIUrl":"https://doi.org/10.1109/OJCS.2024.3522566","url":null,"abstract":"The ability to process data locally using complex algorithms is becoming increasingly important in Internet of Things (IoT) contexts. Numerous factors contribute to this trend, including the requirement for immediate response, the need to protect data privacy/security, a lack of adequate infrastructure, and the desire to reduce costs. Due to the extensive hardware requirements (in terms of required computing power, memory, and other resources) for handling various scenarios, edge devices are typically configured to utilize general-purpose operating systems, primarily GNU/Linux. However, energy efficiency remains a critical requirement for this devices, especially in battery-powered scenarios (where energy inefficiency could make the device completely inoperable). Local data processing usually minimizes, but not entirely eliminates, data exchange with the environment. Along with energy costs of data processing, it is critical to also consider the energy efficiency of data protection when communicating with the environment. In this article, we evaluate the energy efficiency of kernel-level and user-space-level communication protection solutions: WireGuard and OpenSSL. These systems are evaluated on a range of hardware platforms, including Raspberry Pi 3, Nvidia Jetson NANO, Nvidia Jetson TX2, and Nvidia Jetson AGX Xavier. The energy efficiency of these systems was determined by examining long transfer streams with maximum channel/CPU utilization. We discovered that determining the energy efficiency of a device or protocol is difficult due to the high reliance on factors such as communication speed and direction.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"199-210"},"PeriodicalIF":0.0,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10816053","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993268","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}

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Large Pretrained Foundation Model for Key Performance Indicator Multivariate Time Series Anomaly Detection 关键绩效指标多元时间序列异常检测的大型预训练基础模型

IEEE Open Journal of the Computer Society Pub Date : 2024-12-20 DOI: 10.1109/OJCS.2024.3521217

Xu Wang;Qisheng Xu;Kele Xu;Ting Yu;Bo Ding;Dawei Feng;Yong Dou

{"title":"Large Pretrained Foundation Model for Key Performance Indicator Multivariate Time Series Anomaly Detection","authors":"Xu Wang;Qisheng Xu;Kele Xu;Ting Yu;Bo Ding;Dawei Feng;Yong Dou","doi":"10.1109/OJCS.2024.3521217","DOIUrl":"https://doi.org/10.1109/OJCS.2024.3521217","url":null,"abstract":"In the realm of Key Performance Indicator (KPI) anomaly detection, deep learning has emerged as a pivotal technology. Yet, the development of effective deep learning models is hindered by several challenges: scarce and complex labeled data, noise interference from data handling, the necessity to capture temporal dependencies in time series KPI data, and the complexity of multivariate data analysis. Despite recent progress in large models that show potential for handling complex, multidimensional tasks, the lack of extensive, high-quality datasets presents a significant barrier for directly training these models in KPI anomaly detection. This scarcity limits the models' ability to learn and generalize effectively within this specific domain. To overcome this, we propose an innovative approach to adapt fully pretrained large models from other domains to KPI anomaly detection, thereby mitigating data constraints and enhancing detection precision. Our approach involves adapting large models to anomaly detection tasks using patch operations and fine-tuning techniques, which significantly enhances the model's temporal dependency capture capabilities. Furthermore, to address the multivariate challenge, we introduce a novel feature extraction method based on channel independence to optimize information processing across multidimensional features. Additionally, we leverage frequency domain information to design a feature enhancement method, further boosting the model's detection accuracy. By integrating these innovative techniques, we have developed a large-scale KPI anomaly detection model named ViTSD. Empirical evidence from experiments on five benchmark datasets and two additional datasets demonstrates ViTSD's superior performance, outperforming existing models across various evaluation metrics.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"176-187"},"PeriodicalIF":0.0,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10811835","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938111","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}

引用次数: 0

What Time Is It? Finding Which Temporal Features is More Useful for Next Activity Prediction

IEEE Open Journal of the Computer Society Pub Date : 2024-12-20 DOI: 10.1109/OJCS.2024.3519815

Lerina Aversano;Martina Iammarino;Antonella Madau;Giuseppe Pirlo;Gianfranco Semeraro

{"title":"What Time Is It? Finding Which Temporal Features is More Useful for Next Activity Prediction","authors":"Lerina Aversano;Martina Iammarino;Antonella Madau;Giuseppe Pirlo;Gianfranco Semeraro","doi":"10.1109/OJCS.2024.3519815","DOIUrl":"https://doi.org/10.1109/OJCS.2024.3519815","url":null,"abstract":"Process Mining merges data science and process science that allows for the analysis of recorded process data by capturing activities within event-logs. It finds more and more applications for the optimization of the production and administrative processes of private companies and public administrations. This field consists of several areas: process discovery, compliance monitoring, process improvement, and predictive process monitoring. Considering predictive process monitoring, the subarea of next activity prediction helps to obtain a prediction about the next activity performed using control flow data, event data with no attributes other than the timestamp, activity label, and case identifier. A popular approach in this subarea is to use sub-sequences of events, called prefixes and extracted with a sliding window, to predict the next activity. In the literature, several features are added to increase performance. Specifically, this article addresses the problem of predicting the next activity in predictive process monitoring, focusing on the usefulness of temporal features. While past research has explored a variety of features to improve prediction accuracy, the contribution of temporal information remains unclear. In this article it is proposed a comparative analysis of temporal features, such as differences in timestamp, time of day, and day of week, extracted for each event in a prefix. Using both k-fold cross-validation for robust benchmarking and a 75/25 split to simulate real scenarios in which new process events are predicted based on past data, it is shown that timestamp differences within the same prefix consistently outperform other temporal features. Our results are further validated by Shapley's value analysis, highlighting the importance of timestamp differences in improving the accuracy of next activity prediction.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"261-271"},"PeriodicalIF":0.0,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10810467","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143107125","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}

引用次数: 0