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IEEE Journal on Indoor and Seamless Positioning and Navigation 2024年 2卷 92-129页

作者： Potorti, Francesco Crivello, Antonino Lee, Soyeon Vladimirov, Blagovest Park, Sangjoon Chen, Yushi Wang, Long Chen, Runze Zhao, Fang Zhuge, Yue Luo, Haiyong Perez-Navarro, Antoni Jimenez, Antonio R. Wang, Han Liang, Hengyi De Cock, Cedric Plets, David Cui, Yan Xiong, Zhi Li, Xiaodong Ding, Yiming Franco, Fernando Javier Alvarez Polo, Fernando Jesus Aranda Rodriguez, Felipe Parralejo Moreira, Adriano Pendao, Cristiano Silva, Ivo Ortiz, Miguel Zhu, Ni Li, Ziyou Renaudin, Valerie Wei, Dongyan Ji, Xinchun Zhang, Wenchao Wang, Yan Ding, Longyang Kuang, Jian Zhang, Xiaobing Dou, Zhi Yang, Chaoqun Kram, Sebastian Stahlke, Maximilian Mutschler, Christopher Coene, Sander Li, Chenglong Venus, Alexander Leitinger, Erik Tertinek, Stefan Witrisal, Klaus Wang, Yi Wang, Shaobo Jin, Beihong Zhang, Fusang Su, Chang Wang, Zhi Li, Siheng Li, Xiaodong Li, Shitao Pan, Mengguan Zheng, Wang Luo, Kai Ma, Ziyao Gao, Yanbiao Chang, Jiaxing Ren, Hailong Guo, Wenfang Torres-Sospedra, Joaquin Pisa56124 Italy Electronics and Telecommunications Research Institute Daejeon34129 Korea Republic of Beijing University of Posts and Telecommunications School of Computer Science National Pilot Software Engineering School Beijing100876 China Chinese Academy of Sciences Beijing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing Technology Beijing100190 China Universitat Oberta de Catalunya Faculty of Computer Sciences Multimedia and Telecommunication Barcelona08035 Spain Arganda del Rey28500 Spain Huawei Technologies Company Ltd. Shenzhen100015 China IMEC-WAVES/Ghent University Department of Information Technology Gent9052 Belgium Nanjing University of Aeronautics and Astronautics College of Automation Nanjing210016 China University of Extremadura Badajoz06006 Spain University of Trás-os-Montes and Alto Douro Department of Engineering School of Sciences and Technology Vila Real5000-801 Portugal Universidade Do Minho Centro Algoritmi Guimarães4800-058 Portugal Ame Department of the University Gustave Eiffel Geoloc Laboratory Bouguenais44344 France Chinese Academy of Sciences Aerospace Information Research Institute Beijing100094 China Wuhan University Gnss Research Center Wuhan430072 China AutoNavi Software Company Ltd. China Fraunhofer Institute for Integrated Circuits Iis Fraunhofer Iis Nuremberg90411 Germany Erlangen91058 Germany Graz University of Technology Christian Doppler Laboratory for Location-Aware Electronic Systems Graz8010 Austria Institute of Software Chinese Academy of Sciences Beijing100190 China Purple Mountain Laboratories Pervasive Communication Research Center Nanjing210000 China Beijing University of Posts and Telecommunications Beijing100876 China

Indoor positioning is a thriving research area, which is slowly gaining market momentum. Its applications are mostly customized, ad hoc installations;ubiquitous applications analogous to Global Navigation Satellite System for outdoors are not available because of the lack of generic platforms, widely accepted standards and interoperability protocols. In this context, the indoor positioning and indoor navigation (IPIN) competition is the only long-term, technically sound initiative to monitor the state of the art of real systems by measuring their performance in a realistic environment. Most competing systems are pedestrian-oriented and based on the use of smartphones, but several competing tracks were set up, enabling comparison of an array of technologies. The two IPIN competitions described here include only off-site tracks. In contrast with on-site tracks where competitors bring their systems on-site - which were impossible to organize during 2021 and 2022 - in off-site tracks competitors download prerecorded data from multiple sensors and process them using the EvaalAPI, a real-time, web-based emulation interface. As usual with IPIN competitions, tracks were compliant with the EvAAL framework, ensuring consistency of the measurement procedure and reliability of results. The main contribution of this work is to show a compilation of possible indoor positioning scenarios and different indoor positioning solutions to the same problem. © 2023 CCBY.

关键词： Smartphones

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Drowsy Driving Detection System Using Face Detection

Drowsy Driving Detection System Using Face Detection

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Technological Advancements in Computational Sciences (ICTACS), International Conference on

作者： Ismoiljon Mirabdullayev Mohamed Uvaze Ahamed Ayoobkhan A.M. Jasmine Hashana Liayakath Ali Khan Subair Ali Software Engineering New Uzbekistan University Tashkent Uzbekistan Computer Science and Engineering Francis Xavier Engineering College Tirunelveli India School of Information Technology Digital Media and Mass Communication TMC Institute in Tashkent Tashkent Uzbekistan

Drowsy driving poses a major threat to road safety, demanding effective solutions. To address this issue, this paper introduces a cutting-edge drowsy driving detection system that leverages advanced face detection algorithms to analyze real-time facial features. By closely monitoring eye movements and mouth patterns, the system is capable of accurately identifying early signs of fatigue. In response, timely warnings are promptly issued to the driver through various means, including audible alarms, visual cues, or even physical interventions. Although the system holds great potential in enhancing road safety, there are still challenges to overcome, particularly regarding accuracy and adaptability. However, ongoing research endeavors aim to refine the algorithms and integrate additional physiological and behavioral measures, with the goal of further improving the system's capabilities. By preventing accidents caused by drowsiness, this innovative system demonstrates a promising pathway towards bolstering road safety. By addressing the critical issue of drowsy driving, it contributes significantly to the overall safety of drivers and passengers alike, making our roads a safer place for everyone.

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Integrating Exact Simulation into Sweeping for Datapath Combinational Equivalence Checking

Integrating Exact Simulation into Sweeping for Datapath Comb...

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IEEE International Conference on computer-Aided Design

作者： Zhihan Chen Xindi Zhang Yuhang Qian Qiang Xu Shaowei Cai School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China Department of Computer Science & Engineering The Chinese University of Hong Kong

In the application of IC design for microprocessors, there are often demands for optimizing the implementation of datapath circuits, on which various arithmetic operations are performed. Combinational equivalence checking (CEC) plays an essential role in ensuring the correctness of design optimization. The most prevalent CEC algorithms are based on SAT sweeping, which utilizes SAT to prove the equivalence of the internal node pairs in topological order, and the equivalent nodes are merged. Datapath circuits usually contain equivalent pairs for which the transitive fan-in cones are small but have a high XOR chain density, and proving such node pairs is very difficult for SAT solvers. An exact probability-based simulation (EPS) is suitable for verifying such pairs, while this method is not suitable for pairs with many primary inputs due to the memory cost. We first reduce the memory cost of EPS and integrate it to improve the SAT sweeping method. Considering the complementary abilities of SAT and EPS, we design an engine selection heuristic to dynamically choose SAT or EPS in the sweeping process, according to XOR chain density. Our method is further improved by reducing unnecessary engine calls by detecting regularity. Experiments on a benchmark suite from industrial datapath circuits show that our method is much faster than the state-of-the-art CEC tool namely ABC ‘&cec’ on nearly all instances, and is more than 100× faster on 30% of the instances, 1000× faster on 12% of the instances.

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A Deep Learning Framework for Object Detection and Tracking: CNN-Based Parcel Tracking on Conveyor Belts

A Deep Learning Framework for Object Detection and Tracking:...

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Sustainable Technologies for Industry 4.0 (STI), International Conference on

作者： Md Jobair Hossain Faruk Md Kamrul Siam Alex Theilmann Kristiyan Georgiev Jerry Q. Cheng Hossain Shahriar Department of Computer Science New York Institute of Technology USA Nova School of Business and Economics Portugal R&D Computer Vision and Software Engineering Manager Materials Handling Systems Inc. USA Center for Cybersecurity University of West Florida USA

ISBN: (数字)9798331531973

ISBN: (纸本)9798331531980

Object Detection and Tracking (ODT) represents a critical area within computer vision, with broad applications across various industries, particularly in the automation of logistics operations. However, significant challenges persist, including the logistics-specific datasets and the inefficiency of data annotation, which is crucial for training robust artificial intelligence models for parcel detection and tracking. These challenges are exacerbated when human experts are required to manually assign unique identifiers to objects across frames, hindering the scalability of AI model training. In this research, we present a novel deep learning-based ODT framework, leveraging Convolutional Neural Networks (CNN) integrated with evaluation metrics such as Euclidean Distance and Intersection over Union (IoU) to facilitate robust object detection and tracking. To address the limitations of manual annotation, we develop an automated framework for assigning unique identifiers to bounding boxes across sequential frames. The framework is based on three logistics-related datasets, comprising time-series images of packages on moving conveyor belts. The first two datasets, containing 100 and 1,021 images, are manually annotated, while the third dataset of 5,046 images is automatically annotated using the proposed tool. We also utilize the Hungarian algorithm to optimize identity assignment across frames. The results demonstrate that the framework achieves an accuracy of 99.09% in object detection and tracking with a precision of 99.25% and a recall of 99.10% for the manually annotated dataset. The automated annotation illustrates an accuracy of 98.70%. These findings indicate the robustness and scalability of the proposed framework in automating parcel tracking for logistics operations.

关键词： Training computer vision Accuracy Annotations Scalability Object detection Robustness Convolutional neural networks Object tracking Logistics

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CA-Edit: Causality-Aware Condition Adapter for High-Fidelity Local Facial Attribute Editing

arXiv

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arXiv 2024年

作者： Xian, Xiaole He, Xilin Niu, Zenghao Zhang, Junliang Xie, Weicheng Song, Siyang Yu, Zitong Shen, Linlin Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Key Laboratory of Intelligent Information Processing China University of Exeter United Kingdom Great Bay University China

For efficient and high-fidelity local facial attribute editing, most existing editing methods either require additional finetuning for different editing effects or tend to affect beyond the editing regions. Alternatively, inpainting methods can edit the target image region while preserving external areas. However, current inpainting methods still suffer from the generation misalignment with facial attributes description and the loss of facial skin details. To address these challenges, (i) a novel data utilization strategy is introduced to construct datasets consisting of attribute-text-image triples from a data-driven perspective, (ii) a Causality-Aware Condition Adapter is proposed to enhance the contextual causality modeling of specific details, which encodes the skin details from the original image while preventing conflicts between these cues and textual conditions. In addition, a Skin Transition Frequency Guidance technique is introduced for the local modeling of contextual causality via sampling guidance driven by low-frequency alignment. Extensive quantitative and qualitative experiments demonstrate the effectiveness of our method in boosting both fidelity and editability for localized attribute editing. The code is available at https://***/connorxian/CA-Edit. © 2024, CC BY.

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An End-to-End Conformer-Based Speech Recognition Model for Mandarin Radiotelephony Communications in Civil Aviation 16th

An End-to-End Conformer-Based Speech Recognition Model for M...

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16th Chinese Conference on Biometric Recognition, CCBR 2022

作者： Shi, Yihua Ma, Guanglin Ren, Jin Zhang, Haigang Yang, Jinfeng Institute of Applied Artificial Intelligence of the Guangdong-Hong Kong-Macao Greater Bay Area Shenzhen Polytechnic Shenzhen518055 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

ISBN: (纸本)9783031202322

In civil aviation radiotelephony communications, misunderstandings between air traffic controllers and flight crews can result in serious aviation accidents. Automatic semantic verification is a promising assistant solution to decrease miscommunication, thanks to advancements in speech and language processing. Unfortunately, existing general speech recognition models are ineffective when it comes to capturing contextual long-distance dependent local similarity features in radiotelephony communications. To address these problems, this paper proposes an end-to-end Conformer-based multi-task learning speech recognition model for Mandarin radiotelephony communications in civil aviation. The Conformer model improves local information capture while retaining the global information modeling capabilities of contextual long-distance dependencies, owing to the introduction of the convolution module to the Transformer model. Meanwhile, multi-task learning is used to further improve performance by combining connectionist temporal classification (CTC) and attention-based encoder-decoder (AED) models. The experimental results show that the proposed model can perform global and local acoustic modeling effectively, making it particularly suitable for extracting acoustic features of Mandarin civil aviation radiotelephony communications. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Radio telephone

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A Novel Framework for Learning and Classifying the Imbalanced Multi-Label Data

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computer Systems Science & engineering 2024年第5期48卷 1367-1385页

作者： P.K.A.Chitra S.Appavu alias Balamurugan S.Geetha Seifedine Kadry Jungeun Kim Keejun Han Department of Computer Science and Engineering SRM Institute of Science and TechnologyTiruchirappalliTamil Nadu603203India Department of Computer Science and Engineering Periyar Maniammai Institute of Science&Technology(Deemed to be University)ThanjavurTamil Nadu613403India School of Computer Science and Engineering ChennaiTamil Nadu600048India Department of Applied Data Science Noroff University CollegeKristiansand4612Norway Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityP.O.Box 346AjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblos10150Lebanon Department of Software Kongju National UniversityCheonan31080Republic of Korea Division of Computer Engineering Hansung UniversitySeoul02876Republic of Korea

A generalization of supervised single-label learning based on the assumption that each sample in a dataset may belong to more than one class simultaneously is called multi-label *** main objective of this work is to create a novel framework for learning and classifying imbalancedmulti-label *** work proposes a framework of two *** imbalanced distribution of themulti-label dataset is addressed through the proposed Borderline MLSMOTE resampling method in phase ***,an adaptive weighted l21 norm regularized(Elastic-net)multilabel logistic regression is used to predict unseen samples in phase *** proposed Borderline MLSMOTE resampling method focuses on samples with concurrent high labels in contrast to conventional *** minority labels in these samples are called difficult minority labels and are more prone to penalize classification *** concurrentmeasure is considered borderline,and labels associated with samples are regarded as borderline labels in the decision *** phase II,a novel adaptive l21 norm regularized weighted multi-label logistic regression is used to handle balanced data with different weighted synthetic *** on various benchmark datasets shows the outperformance of the proposed method and its powerful predictive performances over existing conventional state-of-the-art multi-label methods.

关键词： Multi-label imbalanced data multi-label learning Borderline MLSMOTE concurrent multi-label adaptive weighted multi-label elastic net difficult minority label

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Aspect-Based Sentiment Analysis on Amharic Text for Evaluating Ethio-Telecom Services

Aspect-Based Sentiment Analysis on Amharic Text for Evaluati...

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Emerging Trends in Information technology and engineering (ic-ETITE), Conference on

作者： Tarikwa Tesfa Befikadu Belete Samuel Abera Sudhir Kumar Mohapatra Tapan Kumar Das Department of Software Engineering Addis Ababa Science and Technology University Addis Ababa Ethiopia Ethio Telecom Services Addis Ababa Ethiopia Faculty of Emerging Technologies Sri Sri University Cuttack India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

People are increasingly expressing their views and opinions about a company's goods or service on social media these days. For all types of businesses and organizations, sentiment analysis in text can be used to find prospective customers and obtain quick feedback on the services or goods they provided. As a result, the organization has a better chance and opportunity to meet the needs of the consumer. Using sentiment analysis, we may extract important information from the thoughts and emotions of an organization's supporters and clients. It examines natural language content produced about services and goods. Detailed opinion mining at the aspect/feature level to serve consumer and organizational needs. In this study, three different machine learning algorithms, these are Naïve Bayes (NB), Support Vector Machine (SVM), and K- Nearest Neighbor (KNN) are used to build the proposed aspect-based sentiment analysis model from the customer comments in Amharic text which is collected from Ethio-telecom official Facebook page. The collected data is annotated into sentiment polarity and aspect-based sentiment classes by the help of five annotators. After data annotation, different preprocessing tasks were implemented. Bag of words and TF-IDF word representation techniques were used to represent each unique word to their vector representation. The experiment was conducted on those three models and the result was validated. The labeled aspects were not balanced so that a SMOTE class imbalance handling technique was employed to the dataset. Finally, SVM model trained with TF-IDF word representation technique with SMOTE oversampled dataset has achieved the highest accuracy result of 98% as compared to NB and KNN.

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MFCLIP: Multi-modal Fine-grained CLIP for Generalizable Diffusion Face Forgery Detection

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IEEE Transactions on Information Forensics and Security 2025年 20卷 5888-5903页

作者： Zhang, Yaning Wang, Tianyi Yu, Zitong Gao, Zan Shen, Linlin Chen, Shengyong Qilu University of Technology Shandong Academy of Sciences Faculty of Computer Science and Technology Jinan 250014 China National University of Singapore School of Computing 21 Lower Kent Ridge Rd 119077 Singapore Great Bay University School of Computing and Information Technology Dongguan 523000 China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen 518060 China Qilu University of Technology (Shandong Academy of Sciences) Shandong Artificial Intelligence Institute Jinan 250014 China Tianjin University of Technology Key Laboratory of Computer Vision and System Ministry of Education Tianjin 300384 China Shenzhen University Computer Vision Institute College of Computer Science and Software Engineering Shenzhen 518060 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen 518129 China Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen 518060 China

The rapid development of photo-realistic face generation methods has raised significant concerns in society and academia, highlighting the urgent need for robust and generalizable face forgery detection (FFD) techniques. Although existing approaches mainly capture face forgery patterns using image modality, other modalities like fine-grained noises and texts are not fully explored, which limits the generalization capability of the model. In addition, most FFD methods tend to identify facial images generated by GAN, but struggle to detect unseen diffusion-synthesized ones. To address the limitations, we aim to leverage the cutting-edge foundation model, contrastive language-image pre-training (CLIP), to achieve generalizable diffusion face forgery detection (DFFD). In this paper, we propose a novel multi-modal fine-grained CLIP (MFCLIP) model, which mines comprehensive and fine-grained forgery traces across image-noise modalities via language-guided face forgery representation learning, to facilitate the advancement of DFFD. Specifically, we devise a fine-grained language encoder (FLE) that extracts fine global language features from hierarchical text prompts. We design a multi-modal vision encoder (MVE) to capture global image forgery embeddings as well as fine-grained noise forgery patterns extracted from the richest patch, and integrate them to mine general visual forgery traces. Moreover, we build an innovative plug-and-play sample pair attention (SPA) method to emphasize relevant negative pairs and suppress irrelevant ones, allowing cross-modality sample pairs to conduct more flexible alignment. Extensive experiments and visualizations show that our model outperforms the state of the arts on different settings like cross-generator, cross-forgery, and cross-dataset evaluations. © 2005-2012 IEEE.

关键词： CLIP Diffusion face forgery detection Image-noise fusion Sample pair attention Transformer

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Stellar Spectral Classification Based on Capsule Network

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Chinese Astronomy and Astrophysics 2021年第3期45卷 352-363页

作者： Li-ting, D.U. Li-hua, H.O.N.G. Jin-tao, Y.A.N.G. Ting-ting, X.U. Jing-min, ZHANG Lin-pin, A.I. Wei-hong, Z.H.O.U. School of Mathematics and Computer Science Yunnan Minzu University Kunming 650500 School of Software Engineering Xiamen Institute of Software Technology Xiamen 361000 Center for Astrophysics Guangzhou University Guangzhou 510006 Key Laboratory of the Structure and Evolution of Celestial Objects Chinese Academy of Sciences Kunming 650011

The rapid development of large-scale sky survey project has produced a large amount of stellar spectral data, which make the automatic classification of stellar spectral data a challenging task. In this paper, we have proposed a stellar spectral classification method based on a capsule network. At first, by using the one-dimensional convolutional network and short-time Fourier transform (STFT), the one-dimensional spectra of the F5, G5, and K5 types selected from the LAMOST Data Release 5 (DR5) are converted into the two-dimensional Fourier spectrum images. Then, the two-dimensional Fourier spectrum images are classified automatically by the capsule network. Because the capsule network can preserve the hierarchical pose relationships among the entities in the image, and it does not need any pooling layers, the experimental results show that the capsule network has a better classification performance, for the classifications of the F5, G5, and K5-type stellar spectra, its classification accuracy is superior to other classification methods. © 2021

关键词： methods: data analysis Stars: fundamental parameters techniques: spectral analysis

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