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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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Detection of Spinal Fracture Lesions Based on Improved Yolo-tiny

Detection of Spinal Fracture Lesions Based on Improved Yolo-...

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2020 IEEE International Conference on Advances in Electrical Engineering and computer Applications, AEECA 2020

作者： Sha, Gang Wu, Junsheng Yu, Bin Northwestern Polytechnical University School of Computer Science Xi'an China Northwestern Polytechnical University School of Software and Microelectronics Xi'an China Xi'dian University School of Computer Science and Technology Xian China

ISBN: (纸本)9781728165202

Spinal CT images are very important for the diagnosis of spinal diseases. However, due to the complication of spine CT images, and also artificial bias and low efficiency in clinical, which makes doctors to take many time in reading spine CT images, so it can not satisfy the clinical real time demands. In this paper, we prestent a method to detect spinal fracture lesions of (cervical fracture, cfracture), (thoracic fracture, tfracture), (lumbar fracture, lfracture) by proposed Yolo-tiny. We add three 1×1 convolution layers to the original Yolo-tiny network to make spinal lesions easier to detect. Our experimental results shows the mAP (mean Average Precision) of our proposed method is 86.63%, detection time is 0.02155 seconds per detection, IOU ((Intersection Over Union) is 87.3. © 2020 IEEE.

关键词： Fracture

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Policy learning from tutorial books via understanding, rehearsing and introspecting 24

Policy learning from tutorial books via understanding, rehea...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Xiong-Hui Chen Ziyan Wang Yali Du Shengyi Jiang Meng Fang Yang Yu Jun Wang National Key Laboratory for Novel Software Technology Nanjing University China & School of Artificial Intelligence Nanjing University China Cooperative AI Lab Department of Informatics King's College London The University of Hong Kong University of Liverpool AI Centre Department of Computer Science University College London

ISBN: (纸本)9798331314385

When humans need to learn a new skill, we can acquire knowledge through written books, including textbooks, tutorials, etc. However, current research for decisionmaking, like reinforcement learning (RL), has primarily required numerous real interactions with the target environment to learn a skill, while failing to utilize the existing knowledge already summarized in the text. The success of Large Language Models (LLMs) sheds light on utilizing such knowledge behind the books. In this paper, we discuss a new policy learning problem called Policy Learning from tutorial Books (PLfB) upon the shoulders of LLMs' systems, which aims to leverage rich resources such as tutorial books to derive a policy network. Inspired by how humans learn from books, we solve the problem via a three-stage framework: Understanding, Rehearsing, and Introspecting (URI). In particular, it first rehearses decision-making trajectories based on the derived knowledge after understanding the books, then introspects about the imaginary dataset to distill a policy network. We build two benchmarks for PLfB based on Tic-Tac-Toe and Football games. In the experiment, URI's policy achieves a minimum of 44% net winning rate against GPT-based agents without any real data. In the much more complex football game, URI's policy beat the built-in AIs with a 37% winning rate while GPT-based agents can only achieve a 6% winning rate. The project page: https://***/.

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A Comprehensive Evaluation of Large Language Models for Method Name Prediction

SSRN

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SSRN 2024年

作者： Qian, Hanwei Xu, Tingting Ding, Ziqi Liu, Wei Zhu, Shaomin Yin, Jie Feng, Xia Fang, Chunrong Zhao, Jianhua Sun, Weisong State Key Laboratory for Novel Software Technology Nanjing University Jiangsu Nanjing210093 China Jiangsu Police Institute China City University of Macau 999078 China Tongji University Shanghai200092 China School of Computer Science and Engineering Nanyang Technological University 639798 Singapore

High-quality method names are essential for understanding and maintaining programs. However, naming methods can be challenging, especially for new developers. To address this, deep-learning techniques have been developed to predict suitable names for method code bodies. Large Language Models (LLMs) have recently shown exceptional performance in Natural Language Processing (NLP), significantly impacting the software engineering (SE) field. LLMs have been applied to tasks like code summarization, indicating their considerable potential. Yet, there hasn't been a systematic study on adapting LLMs for method name prediction (MNP). This paper fills this gap with the first empirical study exploring LLMs' capabilities in MNP tasks. We evaluate LLMs’ comprehension and interaction patterns in MNP, finding they perform best with a 0 temperature and a few-shot prompt template. Comparing prompt-guided LLMs and fine-tuned PLMs, GPT-3.5 excels among LLMs while CodeT5 leads among PLMs, with minimal performance differences. SentenceBert with Cosine Similarity (SBCS) quantifies semantic differences between predicted and ground-truth names. Human evaluation surpasses the F1 metric for LLMs, identifying challenges like insufficient global information and dataset quality. Finally, we discuss applying LLMs to MNP tasks and suggest future research directions. © 2024, The Authors. All rights reserved.

关键词： Forecasting

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Sequential Recommendation via Temporal Data Augmentation and Fourier Convolution

Sequential Recommendation via Temporal Data Augmentation and...

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Algorithms, Computing and Artificial Intelligence (ACAI), International Conference on

作者： Junming Luo Tinghua Zhang Ke Jin Weihao Yu Jin Huang School of Computer Science South China Normal University Guangzhou China Software and Systems Research Department China Electronics Product Reliability and Environmental Testing Research Institute Guangzhou China Network technology research department Research Institute of China Telecom Corporation Ltd Guangzhou China

ISBN: (数字)9798331529314

ISBN: (纸本)9798331529321

Sequence recommendation aims to predict user preferences by modeling users' dynamic historical behaviors. In recent years, advancements in sequence deep learning models such as Transformer have significantly enhanced sequence rec-ommendation. However, most existing sequence recommendation approaches primarily focus on the sequential information of user behaviors while neglecting the critical temporal information. Additionally, noise in interaction records has consistently im-peded the performance of sequence recommendations. To address these two shortcomings, we propose a model named TAFC4Rec. First, to better exploit the relationship between temporal infor-mation and user interest changes, we improve three classical stochastic data augmentation methods by incorporating time interval calculations. Furthermore, following data augmentation, to enhance the encoder's noise filtering capability for better handling augmented data, we design a convolutional structure based on the Fourier transform to denoise the data and capture sequence features. Finally, we conducted experiments on four real-world datasets, and the experimental results demonstrate the effectiveness of our model.

关键词： Fourier transforms Convolution Frequency-domain analysis Noise Stochastic processes Predictive models Data augmentation Transformers Robustness Noise measurement

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Correntropy-Induced Tensor Learning for Multi-view Subspace Clustering

Correntropy-Induced Tensor Learning for Multi-view Subspace ...

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IEEE International Conference on Data Mining (ICDM)

作者： Yongyong Chen Shuqin Wang Jingyong Su Junxin Chen School of Computer Science and Technology Harbin Institute of Technology (Shenzhen) Shenzhen China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies Shenzhen China Institute of Information Science Beijing Jiaotong University Beijing China School of Software Dalian University of Technology Dalian China

Using some specific optimization problems with specific regularizers, multi-view subspace clustering has achieved better performance over single-view subspace clustering. However, they simply assume the noise obeys the Gaussian distribution only, and thus the dataset with non-Gaussian noise or outliers may not be accurately clustered. To address this issue, this paper proposes a novel correntropy-induced tensor learning method for multi-view subspace clustering (CTMSC). Specifically, CTMSC adopts the correntropy-induced metric to substitute the traditional mean square error (MSE) to handle non-Gaussian noise or outliers. Furthermore, the proposed objective function is optimized using an alternating direction method of multipliers with the aid of half-quadratic technology in the form of multiplication. Extensive experimental results on various real-world datasets demonstrate the effectiveness of the proposed method by comparing several state-of-the-art multi-view subspace clustering methods.

关键词： Measurement Learning systems Tensors Clustering methods Mean square error methods Gaussian distribution Linear programming

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Precise Low-Resolution Facial Landmark Detection Supervised by Hallucination and Transfer

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SSRN 2023年

作者： Wan, Jun Liu, Jun Lai, Zhihui Shen, Linlin Zhou, Jie Hou, Xianxu College of Computer Science and Software Engineering Shenzhen University Guangdong Shenzhen518060 China School of Information and Safety Engineering Zhongnan University of Economics and Law Wuhan430073 China Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore487372 Singapore

High-precision facial landmark detection (FLD) results depend on high-resolution deep feature representations, however, the low-resolution inputs hinder learning of high-resolution features, and also reduce the accuracy of FLD. Moreover, insufficient training samples and imprecise annotations further deteriorate the accuracy. In this paper, we propose a weakly-supervised framework, called Supervision-by-Hallucination-and-Transfer (SHT), for more precise low-resolution FLD. SHT contains two novel mutually enhanced modules: Dual Hallucination Learning Network (DHLN) and Facial Pose Transfer Network (FPTN). By incorporating FLD and face hallucination tasks, DHLN is able to learn high-resolution representations with low-resolution inputs for recovering both facial structures and local details, and generating more effective landmark heatmaps. Then, by transforming a person's face from one pose to another, FPTN can boost DHLN to hallucinate more effective faces and landmark heatmaps. Moreover, with the proposed SHT framework, more facial prior knowledge can be introduced from unlabeled data to further detect more accurate landmarks. To the best of our knowledge, this is the first study to explore weakly-supervised FLD by integrating face hallucination and facial pose transfer tasks. Experimental results of both face hallucination and FLD demonstrate that our proposed method outperforms state-of-the-art methods in the literature. © 2023, The Authors. All rights reserved.

关键词： Face recognition

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STR: Spatio-temporal trajectory representation learning with dual-focus encoder for whole trajectory similarity computation

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Information Fusion 2025年 123卷

作者： Li, Mengqiu Niu, Xinzheng Zhu, Jiahui Fournier-Viger, Philippe Wu, Youxi School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China school of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Artificial Intelligence Hebei University of Technology Tianjin China

In the field of trajectory data mining, trajectory similarity computation is a key issue associated with trajectory representation learning. Current methods of trajectory similarity computation often represent trajectory data of varying lengths as vectors with uniform dimension. However, existing trajectory representations fall short of comprehensively calculating trajectory similarity: first, they focus solely on spatial information, while overlooking the temporal information of the trajectory, and second, they represent the temporal and spatial information separately. To address these limitations, we propose a novel Spatio-temporal Trajectory Representation method, named STR, aimed at capturing both the features of individual trajectories and the relationships between them in a three-dimensional spatio-temporal region from the perspectives of both trajectory points and whole trajectories, and improving the accuracy of trajectory similarity metrics. STR consists of two main modules: (i) a trajectory feature tokenization module, which considers the uneven density distribution of trajectory points in the three-dimensional space to build a hierarchical structure that considers space and time as a whole, and extracts spatio-temporal features to construct a token sequence;(ii) a dual-focus trajectory encoder learns the features of a given trajectory and those of similar trajectories, thereby enabling the consideration of relationships between whole trajectories. Moreover, a density-based whole trajectory similarity metric is designed to find similar trajectories. Experiments were conducted on three public datasets, and the accuracy of the trajectory representations obtained with STR is significantly greater than for existing state-of-the-art baselines on a variety of distance metrics. © 2025 Elsevier B.V.

关键词： Dual-focus trajectory encoder Representation learning Spatio-temporal trajectory Trajectory similarity

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ML-based Privacy Leakage Behavior Detection in Android Apps at Scale

ML-based Privacy Leakage Behavior Detection in Android Apps ...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： ZhiLiang Bu Chunlei Zhao Liangyi Gong Yan Wang Yi Yang Xi Wang Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Ministry of Education Tianjin China School of Computer Science and Engineering Tianjin University of Technology Tianjin China Computer Network Information Center Chinese Academy of Sciences Beijing China School of Cyber Science and Engineering Southeast University Nanjing China

In recent years, Android application privacy leaking issues frequently occur, with the results that privacy leakage detection becomes a critical role in app market security review, and numerous mobile apps have been removed from the app stores as a consequence. At the same time, manual review is still considered as a main method for market security audit, but it would cause low efficiency and high false positive when faced with thousands of auditing tasks. To overcome the above problems, this paper implements a large volume, automated detection of privacy leaks in Android applications via machine learning algorithms. In order to achieve a comprehensive and in-depth analysis of application behavior features, firstly, 2346 benign software from app market and 1912 malware identified by a national regulatory authority as leaking privacy in the past two years were selected and studied using a hybrid static-dynamic analysis. Then by developing novel sensitivity criteria, we opt privacy-sensitive key permissions and API calls that are highly relevant to privacy leakage behavior. Moreover, in order to improve the practicability of our system, this thesis systematically optimize the application exerciser on the Android emulator, and achieve average per-app analysis time with less than 2 minutes. Finally, the results of the comparison experiments reveal that the stacking ensemble learning algorithm displayed the best classification performance and achieved 97.81% in accuracy of privacy leakage behavior detection.

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COVID TCL:A Joint Metric Loss Function for Diagnosing COVID-19 Patient in the Early and Incubation Period

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computer Systems science & Engineering 2023年第10期47卷 187-204页

作者： Rui Wen Jie Zhou Zhongliang Shen Xiaorui Zhang Sunil Kumar Jha Department of Electromechanical Engineering Zhejiang Business Technology InstituteNingbo315012China Engineering Research Center of Digital Forensics Ministry of EducationJiangsu Engineering Center of Network MonitoringSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Wuxi Research Institute Nanjing University of Information Science&TechnologyWuxi214100China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science&TechnologyNanjing210044China Department of Physics Faculty of ScienceAdani UniversityAhmedabadIndia

Convolution Neural Networks(CNN)can quickly diagnose COVID-19 patients by analyzing computed tomography(CT)images of the lung,thereby effectively preventing the spread of ***,the existing CNN-based COVID-19 diagnosis models do consider the problem that the lung images of COVID-19 patients in the early stage and incubation period are extremely similar to those of the non-COVID-19 *** reduces the model’s classification sensitivity,resulting in a higher probability of the model misdiagnosing COVID-19 patients as non-COVID-19 *** solve the problem,this paper first attempts to apply triplet loss and center loss to the field of COVID-19 image classification,combining softmax loss to design a jointly supervised metric loss function COVID Triplet-Center Loss(COVID-TCL).Triplet loss can increase inter-class discreteness,and center loss can improve intra-class ***,COVID-TCL can help the CNN-based model to extract more discriminative features and strengthen the diagnostic capacity of COVID-19 patients in the early stage and incubation ***,we use the extreme gradient boosting(XGBoost)as a classifier to design a COVID-19 images classification model of CNN-XGBoost architecture,to further improve the CNN-based model’s classification effect and operation *** experiment shows that the classification accuracy of the model proposed in this paper is 97.41%,and the sensitivity is 97.61%,which is higher than the other 7 reference *** COVID-TCL can effectively improve the classification sensitivity of the CNN-based model,the CNN-XGBoost architecture can further improve the CNN-based model’s classification effect.

关键词： Covid-19 diagnose convolutional neural networks XGBoost COVID triplet-center loss early and incubation COVID-19 patients

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