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AmplitudeArrow: On-the-Go AR Menu Selection Using Consecutive Simple Head Gestures and Amplitude Visualization

IEEE Transactions on Visualization and computer Graphics 2025年第05期31卷 3408-3417页

作者： Tian, Yang Zhang, Youpeng Yan, Yukang Zhao, Shengdong Ma, Xiaojuan Shi, Yuanchun Guangxi University Department of Computer Science Guangxi Key Laboratory of Multimedia Communications and Network Technology China University of Rochester Department of Computer Science United States City University of Hong Kong School of Creative Media and the Department of Computer Science Hong Kong Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong Tsinghua University Department of Computer Science and Technology China

Heads-up computing aims to provide synergistic digital assistance that minimally interferes with users' on-the-go daily activities. Currently, the input modalities of heads-up computing are mainly voice and finger gestures. In this work, we propose and evaluate the AmplitudeArrow (AA) technique designed for on-the-go AR menu selection to demonstrate that consecutive simple head gestures can also be an effective input modality for heads-up computing. Specifically, AA arranges menu icons into one/two row(s). To select a target icon, the user first makes their head yaw to pre-select the target icon or the column containing it and then makes their head pitch to make the arrow in the target icon expand until the arrow covers the target icon completely, i.e., the pitch amplitude surpasses the selection confirmation threshold. User studies indicated that AA demonstrated robust resistance to walking-caused head perturbation and external factors such as other people/obstacles, delivering high accuracy (error rate © 1995-2012 IEEE.

关键词： Augmented reality

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An overview on IRS-enabled sensing and communications for 6G: architectures, fundamental limits, and joint beamforming designs

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Science China(Information Sciences) 2025年第5期68卷 170-193页

作者： Xianxin SONG Yuan FANG Feng WANG Zixiang REN Xianghao YU Ye ZHANG Fan LIU Jie XU Derrick Wing Kwan NG Rui ZHANG Shuguang CUI School of Science and Engineering (SSE) Shenzhen Future Network of Intelligence Institute (FNii-Shenzhen)and Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Department of Electrical Engineering City University of Hong Kong School of Information Engineering Guangdong University of Technology Key Laboratory of Wireless-Optical Communications Chinese Academy of Sciences School of Information Science and Technology University of Science and Technology of China Computer School Beijing Information Science and Technology University National Mobile Communications Research Laboratory School of Information Science and Engineering Southeast University School of Electrical Engineering and Telecommunications University of New South Wales School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Department of Electrical and Computer Engineering National University of Singapore

This study presents an overview on intelligent reflecting surface(IRS)-enabled sensing and communication for the forthcoming sixth-generation(6G) wireless networks, in which IRSs are strategically deployed to proactively reconfigure wireless environments to improve both sensing and communication(S&C) performance. First, we exploit a single IRS to enable wireless sensing in the base station's(BS's) non-line-of-sight(NLoS) area. In particular, we present three IRS-enabled NLoS target sensing architectures with fully-passive, semi-passive, and active IRSs, respectively. We compare their pros and cons by analyzing the fundamental sensing performance limits for target detection and parameter estimation. Next, we consider a single IRS to facilitate integrated sensing and communication(ISAC), in which the transmit signals at the BS are used for achieving both S&C functionalities, aided by the IRS through reflective beamforming. We present joint transmit signal and receiver processing designs for realizing efficient ISAC, and jointly optimize the transmit beamforming at the BS and reflective beamforming at the IRS to balance the fundamental performance tradeoff between S&C. Furthermore, we discuss multi-IRS networked ISAC, by particularly focusing on multi-IRS-enabled multi-link ISAC, multi-region ISAC, and ISAC signal routing, respectively. Finally, we highlight various promising research topics in this area to motivate future work.

关键词： integrated sensing and communication (ISAC) intelligent reflecting surface (IRS) non-line-of-sight (NLoS) sensing sensing and communication tradeoff

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TalkSee: Interactive Video Retrieval Engine Using Large Language Model 30th

TalkSee: Interactive Video Retrieval Engine Using Large Lang...

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30th International Conference on MultiMedia Modeling, MMM 2024

作者： Gu, Guihe Wu, Zhengqian He, Jiangshan Song, Lin Wang, Zhongyuan Liang, Chao Wuhan China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan China School of Computer Science Wuhan University Wuhan China

ISBN: (纸本)9783031533013

The current interactive retrieval system mostly relies on collecting user’s positive and negative feedback and updating the retrieval content based on this feedback. However, this method is not always sufficient to accurately express users’ retrieval intent. Inspired by the powerful language understanding capability of the Large Language Model (LLM), we propose TalkSee, an interactive video retrieval engine using LLM for interaction in order to better capture users’ latent retrieval intentions. We use the large language model for processing positive and negative feedback into natural language interactions. Specifically, combined with feedback, we leverage LLM to generate questions, update the queries, and conduct re-ranking. Last but not least, we design a tailored interactive user interface (UI) in conjunction with the above method for more efficient and effective video retrieval. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： User interfaces

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A Technique for Ensured Cross Chain IBC Transactions Using TPM 2

A Technique for Ensured Cross Chain IBC Transactions Using T...

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2nd International Conference on Big Data and Privacy Computing, BDPC 2024

作者： Wang, YiChuan Yan, YiLiang Liu, XiaoXue Gao, Wen Wang, ZiYan School of Computer Science and Engineering Xi'an University of Technology Xi'an China Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China

ISBN: (纸本)9798350307597

Cross chain contact across several blockchains becomes increasingly significant in importance with the increased use of blockchain technology. However, security cannot be assured when producing equivalent outputs through cross-chain transmission because it is simple to launch an assault, and without security measures;it is simple to cause the transmission material to be stolen. This article combines TPM, IBC, and associates encryption and decryption techniques for protection to overcome this issue. We have establishes the viability and efficacy of the suggested method through testing and performance analyses. The experimental findings show that our approach can successfully preserve privacy and guarantee the security of data transfer. © 2024 IEEE.

关键词： Blockchain

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A hybrid blockchain and machine learning approach for intrusion detection system in Industrial Internet of Things

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Alexandria engineering Journal 2025年 127卷 619-627页

作者： Song, Wu Zhu, Xiangyuan Ren, Sheng Tan, Wenxue Peng, Yibo School of Computer and Electrical Engineering Hunan University of Arts and Science Hunan Changde415000 China Hunan Provincial Key Laboratory of Network Investigational Technology Hunan Police Academy Hunan Changsha410138 China

The Industrial Internet of Things (IIoT) is a key component of Industry 4.0, which enables manufacturing to be automated and data collected in real-time. Edge IoT devices are subject to cybersecurity threats and unauthorised access. Decentralisation and resource limitations of IIoT often prevent traditional security mechanisms from addressing these threats. Intrusion detection systems (IDSs), which are used to detect intrusions in IIoT environments, are presented in this paper as hybrid machine learning-blockchain approaches. Blockchain technology ensures data integrity, secures communication, and prevents unauthorised modifications through the proposed system. To reduce false positives and improve threat detection accuracy, XGBoost is able to reduce the number of false positives. Using the BOT-IoT dataset, the model is demonstrated to be superior to conventional intrusion detection systems. This approach ensures enhanced security and trustworthiness of IIoT networks by offering a scalable, efficient, and secure solution. © 2025

关键词： Industrial internet of things (IIoT)

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Integrating Syntactic and Semantic Knowledge in AMR Parsing with Heterogeneous Graph Attention network 48

Integrating Syntactic and Semantic Knowledge in AMR Parsing ...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Sataer, Yikemaiti Shi, Chuanqi Gao, Miao Fan, Yunlong Li, Bin Gao, Zhiqiang Southeast University School of Computer Science and Engineering Nanjing China Ministry of Education Key Laboratory of Computer Network and Information Integration Nanjing China

ISBN: (纸本)9781728163277

Meaning Representation (AMR) parsing is the task of translating a sentence to an AMR semantic graph which captures the basic meaning of the sentence, and is empowered by pre-trained Transformer models recently. These models encode the syntactic and semantic knowledge implicitly through self-supervised pre-trained tasks. We argue that encoding the syntactic and semantic knowledge explicitly is beneficial to AMR parsing and can improve data efficiency. Specifically, syntactic dependency and semantic role labeling (SRL) have similar sub-structures with AMR. In this work, we propose a novel linguistic knowledge-enhanced AMR parsing model, which augments the pre-trained Transformer with syntactic dependency and semantic role labeling structures of sentences. By applying a heterogeneous graph attention network, we can obtain syntactically and semantically augmented word representation, which can be integrated using an attentive integration layer and gating mechanism. Experimental results show that our model achieves state-of-the-art performance on different benchmarks, especially in out-of-domain and low-resource scenarios. © 2023 IEEE.

关键词： Syntactics

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SDDNet:Infrared small and dim target detection network

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CAAI Transactions on Intelligence Technology 2023年第4期8卷 1226-1236页

作者： Ma Long Shu Cong Huang Shanshan Wei Zoujian Wang Xuhao Wei Yanxi School of Computer Science and Engineering Xi'an Technological UniversityXi'anShaanxiChina State and Local Joint Laboratory of Advanced Network and Monitoring Xi'anShaanxiChina School of Computing University of Kent CanterburyUK

This study focuses on developing deep learning methods for small and dim target *** model infrared images as the union of the target region and background *** on this model,the target detection problem is considered a two‐class segmentation problem that divides an image into the target and ***,a neural network called SDDNet for single‐frame images is *** network yields target extraction results according to the original *** multiframe images,a network called IC‐SDDNet,a combination of SDDNet and an interframe correlation network module is *** and IC‐SDDNet achieve target detection rates close to 1 on typical datasets with very low false positives,thereby performing significantly better than current *** models can be executed end to end,so both are very convenient to use,and their implementation efficiency is very *** speeds of 540+/230+FPS and 170+/60+FPS are achieved with SDDNet and IC‐SDDNet on a single Tesla V100 graphics processing unit and a single Jetson TX2 embedded module ***,neither network needs to use future information,so both networks can be directly used in real‐time *** well‐trained models and codes used in this study are available at https://***/LittlePieces/ObjectDetection.

关键词： deep learning detection of moving objects

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MAGRET: Machine-generated Text Detection with Rewritten Texts 31

MAGRET: Machine-generated Text Detection with Rewritten Text...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Huang, Yifei Cao, Jingxin Luo, Hanyu Guan, Xin Liu, Bo School of Cyber Science and Engineering Southeast University Nanjing211189 China Key Laboratory of Computer Network and Information of Ministry of Education of China Nanjing211189 China School of Computer Science and Engineering Southeast University Nanjing211189 China

ISBN: (纸本)9798891761964

With the quick advancement in text generation ability of Large Language Model(LLM), concerns about the misuse of machine-generated text(MGT) have grown, raising potential violations of legal and ethical standards. Some existing studies concentrate on detecting machine-generated text in open-source models using in-model features, but their performance on closed-source large models is limited. This limitation occurs because, in the closed-source model detection, the only reference that can be obtained is the texts, which may differ significantly due to random sampling. In this paper, we demonstrate that texts generated by the same model can align both semantically and statistically under similar prompts, facilitating effective detection and traceability. Specifically, we fine-tune a BERT encoder through contrastive learning to achieve semantic alignment in randomly generated texts from the same model. Then, we propose a method called Machine-Generated Text Detection with Rewritten Texts, which designed several prompt refactoring methods and used them to request rewritten text from LLMs. Semantic and statistical relationships between rewritten and original texts provide a basis for detection and traceability. Finally, we expanded the text dataset with multi-parameter random sampling and verified the performance of MAGRET on three text-generated datasets. Experimental results show that previous methods struggle with closed-source model detection, while our approach significantly outperforms baseline methods in this regard. It also shows MAGRET's stable performance in detection and tracing tasks across various randomly sampled texts. © 2025 Association for Computational Linguistics.

关键词： Contrastive Learning

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Automated object recognition in high-resolution optical remote sensing imagery

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National Science Review 2023年第6期10卷 38-41页

作者： Yazhou Yao Tao Chen Hanbo Bi Xinhao Cai Gensheng Pei Guoye Yang Zhiyuan Yan Xian Sun Xing Xu Hai Zhang School of Computer Science and Engineering Nanjing University of Science and Technology Aerospace Information Research Institute Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Key Laboratory of Network Information System Technology (NIST) Aerospace Information Research Institute Chinese Academy of Sciences Department of Computer Science and Technology Tsinghua University School of Computer Science and Engineering University of Electronic Science and Technology of China Pazhou Laboratory (Huangpu) School of Mathematics Northwest University

INTRODUCTION With the rapid development of remote sensing technology,high-quality remote sensing images have become widely *** automated object detection and recognition of these images,which aims to automatically locate objects of interest in remote sensing images and distinguish their specific categories,is an important fundamental task in the *** provides an effective means for geospatial object monitoring in many social applications,such as intelligent transportation,urban planning,environmental monitoring and homeland security.

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A survey on event connections and their discovery for cloud service

A survey on event connections and their discovery for cloud ...

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2023 International Conference on Electronic Information engineering and Data Processing, EIEDP 2023

作者： Zhang, Zhen Xia, Chunhe Li, Shuyu Liu, Zhiying Huang, Mingzhi Huang, Chengling Li, Xiaojian School of Computer Science & Engineering Guangxi Normal University Guilin China Beijing Key Laboratory of Network Technology School of Computer Science and Engineering Beihang University Beijing China

ISBN: (纸本)9781510666160

Connection is the effect between events. It is an important basis for judging of violations and source-trace of failure in cloud service accountability. This paper presents a survey on the event connections including the features, method of discovery and correction. Most of recent research on event connections in cloud services relying on manually labeling and correcting to event connections, and a few of them have achieved automatically labeling of event connections. Finally, we give out a taxonomy of the recent research, some problems in the method of discovery and correction, and an approach in the paper. © 2023 SPIE.

关键词： Distributed database systems

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