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Adaptive and augmented active anomaly detection on dynamic network traffic streams Front. Inform. Technol. Electron. Eng. (IF 3.0) Pub Date : 2024-03-23 Bin Li, Yijie Wang, Li Cheng
Active anomaly detection queries labels of sampled instances and uses them to incrementally update the detection model, and has been widely adopted in detecting network attacks. However, existing methods cannot achieve desirable performance on dynamic network traffic streams because (1) their query strategies cannot sample informative instances to make the detection model adapt to the evolving stream
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Identity-based searchable attribute signcryption in lattice for a blockchain-based medical system Front. Inform. Technol. Electron. Eng. (IF 3.0) Pub Date : 2024-03-23 Huifang Yu, Xiaoping Bai
Electronic healthcare systems can offer convenience but face the risk of data forgery and information leakage. To solve these issues, we propose an identity-based searchable attribute signcryption in lattice for a blockchain-based medical system (BCMS-LIDSASC). BCMS-LIDSASC achieves decentralization and anti-quantum security in the blockchain environment, and provides fine-grained access control and
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Large language model and domain-specific model collaboration for smart education Front. Inform. Technol. Electron. Eng. (IF 3.0) Pub Date : 2024-03-23 Yawei Luo, Yi Yang
提出旨在增强智能教育的大型语言与领域特定模型协作(LDMC)框架。LDMC框架充分利用大型领域通用模型的综合全面知识,将其与小型领域特定模型的专业和学科知识相结合,并融入来自学习理论模型的教育学知识。这种整合产生的多重知识表达促进了个性化和自适应的教育体验。在智能教育背景下探讨了LDMC框架的各种应用,包括群体学习、个性化辅导、课堂管理等。LDMC融合了多种规模模型的智能,代表了一种先进而全面的教育辅助框架。随着人工智能的不断发展,该框架有望在智慧教育领域展现较大潜力。
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A robust tensor watermarking algorithm for diffusion-tensor images Front. Inform. Technol. Electron. Eng. (IF 3.0) Pub Date : 2024-03-23 Chengmeng Liu, Zhi Li, Guomei Wang, Long Zheng
Watermarking algorithms that use convolution neural networks have exhibited good robustness in studies of deep learning networks. However, after embedding watermark signals by convolution, the feature fusion efficiency of convolution is relatively low; this can easily lead to distortion in the embedded image. When distortion occurs in medical images, especially in diffusion tensor images (DTIs), the
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Low-rank matrix recovery with total generalized variation for defending adversarial examples Front. Inform. Technol. Electron. Eng. (IF 3.0) Pub Date : 2024-03-23 Wen Li, Hengyou Wang, Lianzhi Huo, Qiang He, Linlin Chen, Zhiquan He, Wing W. Y. Ng
Low-rank matrix decomposition with first-order total variation (TV) regularization exhibits excellent performance in exploration of image structure. Taking advantage of its excellent performance in image denoising, we apply it to improve the robustness of deep neural networks. However, although TV regularization can improve the robustness of the model, it reduces the accuracy of normal samples due
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Towards understanding bogus traffic service in online social networks Front. Inform. Technol. Electron. Eng. (IF 3.0) Pub Date : 2024-03-23 Ping He, Xuhong Zhang, Changting Lin, Ting Wang, Shouling Ji
Critical functionality and huge influence of the hot trend/topic page (HTP) in microblogging sites have driven the creation of a new kind of underground service called the bogus traffic service (BTS). BTS provides a kind of illegal service which hijacks the HTP by pushing the controlled topics into it for malicious customers with the goal of guiding public opinions. To hijack HTP, the agents of BTS
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Towards adaptive graph neural networks via solving prior-data conflicts Front. Inform. Technol. Electron. Eng. (IF 3.0) Pub Date : 2024-03-23 Xugang Wu, Huijun Wu, Ruibo Wang, Xu Zhou, Kai Lu
Graph neural networks (GNNs) have achieved remarkable performance in a variety of graph-related tasks. Recent evidence in the GNN community shows that such good performance can be attributed to the homophily prior; i.e., connected nodes tend to have similar features and labels. However, in heterophilic settings where the features of connected nodes may vary significantly, GNN models exhibit notable
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Vibration harmonic suppression technology for electromagnetic vibrators based on an improved sensorless feedback control method Front. Inform. Technol. Electron. Eng. (IF 3.0) Pub Date : 2024-03-01
Abstract To realize low harmonic distortion of the vibration waveform output from electromagnetic vibrators, we propose a vibration harmonic suppression technology based on an improved sensorless feedback control method. Without changing the original driving circuit, the alternating current (AC) equivalent resistance of the driving coil is used to obtain high-precision vibration velocity information
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Engineering applications and technical challenges of active array microsystems Front. Inform. Technol. Electron. Eng. (IF 3.0) Pub Date : 2024-03-01
Abstract In the post-Moore era, the development of active phased array antennas will inevitably trend towards active array microsystems. In this paper, the characteristics and composition of the active array antenna are briefly described. Owing to the high efficiency, low profile, and light weight of the active array microsystems, the application prospects and advantages in the engineering of multi-functional
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Outlier-resistant distributed fusion filtering for nonlinear discrete-time singular systems under a dynamic event-triggered scheme Front. Inform. Technol. Electron. Eng. (IF 3.0) Pub Date : 2024-02-13 Zhibin Hu, Jun Hu, Cai Chen, Hongjian Liu, Xiaojian Yi
This paper investigates the problem of outlier-resistant distributed fusion filtering (DFF) for a class of multi-sensor nonlinear singular systems (MSNSSs) under a dynamic event-triggered scheme (DETS). To relieve the effect of measurement outliers in data transmission, a self-adaptive saturation function is used. Moreover, to further reduce the energy consumption of each sensor node and improve the
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Secure control and filtering for industrial metaverse Front. Inform. Technol. Electron. Eng. (IF 3.0) Pub Date : 2024-02-13 Qing-Long Han, Derui Ding, Xiaohua Ge
元宇宙可被视为一个社会化和虚拟化的网络空间, 与现实世界平行但互动. 得益于云计算和数字孪生的快速发展, 元宇宙正在将带有传统控制和滤波范式的工业自动化系统转变为信息物理社会融合系统. 在此情况下, 未来的工业自动化系统可能是在一定时间和空间范围内具有强大计算能力的现实世界系统与虚拟孪生系统的集成. 在该领域中, 元宇宙重点涉及信息传输管理、 用户的行为识别以及控制、 滤波和决策, 且性能和成本将是系统在网络空间和现实世界中行为的综合反映. 毫无疑问, 虚拟网络空间和现实世界之间的信息交换的内在特征, 唤起了对安全控制和滤波的新需求. 如何保证期望的系统性能和实现理想的参数设计, 已然成为该领域研究的重大挑战. 然而, 目前对元宇宙的研究主要集中在解决其社会关切和寻求其在各个领域的应用, 其在控制领域的发展仍处于初级阶段. 当网络空间与现实世界交换感兴趣的信息时, 恶意攻击不可避免, 这可能导致数据不可靠或隐私泄露
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Modified dynamic event-triggered scaled formation control for multi-agent systems via a sparrow search algorithm based co-design algorithm Front. Inform. Technol. Electron. Eng. (IF 3.0) Pub Date : 2024-02-13 Yanping Yang, Siyu Ma, Dawei Li, Jinghui Suo
This paper is concerned with the scaled formation control problem for multi-agent systems (MASs) over fixed and switching topologies. First, a modified resilient dynamic event-triggered (DET) mechanism involving an auxiliary dynamic variable (ADV) based on sampled data is proposed. In the proposed DET mechanism, a random variable obeying the Bernoulli distribution is introduced to express the idle
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Prompt learning in computer vision: a survey Front. Inform. Technol. Electron. Eng. (IF 3.0) Pub Date : 2024-02-08 Yiming Lei, Jingqi Li, Zilong Li, Yuan Cao, Hongming Shan
Prompt learning has attracted broad attention in computer vision since the large pre-trained vision-language models (VLMs) exploded. Based on the close relationship between vision and language information built by VLM, prompt learning becomes a crucial technique in many important applications such as artificial intelligence generated content (AIGC). In this survey, we provide a progressive and comprehensive
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Advances and challenges in artificial intelligence text generation Front. Inform. Technol. Electron. Eng. (IF 3.0) Pub Date : 2024-02-08 Bing Li, Peng Yang, Yuankang Sun, Zhongjian Hu, Meng Yi
Text generation is an essential research area in artificial intelligence (AI) technology and natural language processing and provides key technical support for the rapid development of AI-generated content (AIGC). It is based on technologies such as natural language processing, machine learning, and deep learning, which enable learning language rules through training models to automatically generate
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Six-Writings multimodal processing with pictophonetic coding to enhance Chinese language models Front. Inform. Technol. Electron. Eng. (IF 3.0) Pub Date : 2024-02-08 Li Weigang, Mayara Chew Marinho, Denise Leyi Li, Vitor Vasconcelos De Oliveira
While large language models (LLMs) have made significant strides in natural language processing (NLP), they continue to face challenges in adequately addressing the intricacies of the Chinese language in certain scenarios. We propose a framework called Six-Writings multimodal processing (SWMP) to enable direct integration of Chinese NLP (CNLP) with morphological and semantic elements. The first part
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Style-conditioned music generation with Transformer-GANs Front. Inform. Technol. Electron. Eng. (IF 3.0) Pub Date : 2024-02-08 Weining Wang, Jiahui Li, Yifan Li, Xiaofen Xing
Recently, various algorithms have been developed for generating appealing music. However, the style control in the generation process has been somewhat overlooked. Music style refers to the representative and unique appearance presented by a musical work, and it is one of the most salient qualities of music. In this paper, we propose an innovative music generation algorithm capable of creating a complete
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Recent advances in artificial intelligence generated content Front. Inform. Technol. Electron. Eng. (IF 3.0) Pub Date : 2024-02-08 Junping Zhang, Lingyun Sun, Cong Jin, Junbin Gao, Xiaobing Li, Jiebo Luo, Zhigeng Pan, Ying Tang, Jingdong Wang
人工智能生成内容(AIGC)是近年来人工智能(AI)领域一个研究热点,它有望取代人类以较低成本高效率执行内容生成工作,如音乐、绘画、多模态内容生成、新闻文章、总结报告、股评摘要,以至元宇宙中的内容生成和数字人。AIGC为未来AI发展和实现提供了一条新的技术路径。 在此背景下,《信息与电子工程前沿(英文)》期刊组织了一期关于AIGC最新进展的特刊。本期特刊关注AIGC理论、算法、应用及相关领域。通过吸引高质量论文,我们希望帮助学术界和工业界研究人员更深入了解AIGC背后的基本理论及其潜在应用,激励更多研究人员加入并推进AIGC领域的研究。因此,我们就以下主题(但不限于)征集论文:(1)AI生成音乐;(2)AI生成绘画;(3)AI对话模型;(4)AI新闻摘要;(5)AI与元宇宙;(6)AI与数字人;(7)AI图像编辑;(8)AI生成短视频;(9)AI生成多媒体内容;(10)ChatGPT相关工作。经严格评审
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Energy efficiency optimization for a RIS-assisted multi-cell communication system based on a practical RIS power consumption model Front. Inform. Technol. Electron. Eng. (IF 3.0) Pub Date : 2024-01-29 Danning Xu, Yu Han, Xiao Li, Jinghe Wang, Shi Jin
Reconfigurable intelligent surface (RIS) is widely accepted as a potential technology to assist in communication between base stations (BSs) and users in edge areas. We study the energy efficiency of a RIS-assisted multi-cell communication system with a realistic RIS power consumption model. With the goal of maximizing the energy efficiency of the system, we optimize the transmit beamforming vectors
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Realizing complex beams via amplitude-phase digital coding metasurfaces and semidefinite relaxation optimization Front. Inform. Technol. Electron. Eng. (IF 3.0) Pub Date : 2024-01-29 Junwei Wu, Qiong Hua, Hui Xu, Hanqing Yang, Zhengxing Wang, Qiang Cheng, Tie Jun Cui
Complex beams play important roles in wireless communications, radar, and satellites, and have attracted great interest in recent years. In light of this background, we present a fast and efficient approach to realize complex beams by using semidefinite relaxation (SDR) optimization and amplitude-phase digital coding metasurfaces. As the application examples of this approach, complex beam patterns
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Simultaneously transmitting and reflecting (STAR) RISs for 6G: fundamentals, recent advances, and future directions Front. Inform. Technol. Electron. Eng. (IF 3.0) Pub Date : 2024-01-29 Yuanwei Liu, Jiaqi Xu, Zhaolin Wang, Xidong Mu, Jianhua Zhang, Ping Zhang
Simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) have been attracting significant attention in both academia and industry for their advantages of achieving 360° coverage and enhanced degrees-of-freedom. This article first identifies the fundamentals of STAR-RIS, by discussing the hardware models, channel models, and signal models. Then, three representative
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Deep3DSketch-im: rapid high-fidelity AI 3D model generation by single freehand sketches Front. Inform. Technol. Electron. Eng. (IF 3.0) Pub Date : 2024-01-01
Abstract The rise of artificial intelligence generated content (AIGC) has been remarkable in the language and image fields, but artificial intelligence (AI) generated three-dimensional (3D) models are still under-explored due to their complex nature and lack of training data. The conventional approach of creating 3D content through computer-aided design (CAD) is labor-intensive and requires expertise
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TendiffPure: a convolutional tensor-train denoising diffusion model for purification Front. Inform. Technol. Electron. Eng. (IF 3.0) Pub Date : 2024-01-01
Abstract Diffusion models are effective purification methods, where the noises or adversarial attacks are removed using generative approaches before pre-existing classifiers conducting classification tasks. However, the efficiency of diffusion models is still a concern, and existing solutions are based on knowledge distillation which can jeopardize the generation quality because of the small number
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Controllable image generation based on causal representation learning Front. Inform. Technol. Electron. Eng. (IF 3.0) Pub Date : 2024-01-01
Abstract Artificial intelligence generated content (AIGC) has emerged as an indispensable tool for producing large-scale content in various forms, such as images, thanks to the significant role that AI plays in imitation and production. However, interpretability and controllability remain challenges. Existing AI methods often face challenges in producing images that are both flexible and controllable
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Estimation of Hammerstein nonlinear systems with noises using filtering and recursive approaches for industrial control Front. Inform. Technol. Electron. Eng. (IF 3.0) Pub Date : 2023-12-29
Abstract This paper discusses a strategy for estimating Hammerstein nonlinear systems in the presence of measurement noises for industrial control by applying filtering and recursive approaches. The proposed Hammerstein nonlinear systems are made up of a neural fuzzy network (NFN) and a linear state`-space model. The estimation of parameters for Hammerstein systems can be achieved by employing hybrid
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A visual analysis approach for data imputation via multi-party tabular data correlation strategies Front. Inform. Technol. Electron. Eng. (IF 3.0) Pub Date : 2023-12-29 Haiyang Zhu, Dongmin Han, Jiacheng Pan, Yating Wei, Yingchaojie Feng, Luoxuan Weng, Ketian Mao, Yuankai Xing, Jianshu Lv, Qiucheng Wan, Wei Chen
Data imputation is an essential pre-processing task for data governance, aimed at filling in incomplete data. However, conventional data imputation methods can only partly alleviate data incompleteness using isolated tabular data, and they fail to achieve the best balance between accuracy and efficiency. In this paper, we present a novel visual analysis approach for data imputation. We develop a multi-party
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Empowering smart city situational awareness via big mobile data Front. Inform. Technol. Electron. Eng. (IF 3.0) Pub Date : 2023-12-29
Abstract Smart city situational awareness has recently emerged as a hot topic in research societies, industries, and governments because of its potential to integrate cutting-edge information technology and solve urgent challenges that modern cities face. For example, in the latest five-year plan, the Chinese government has highlighted the demand to empower smart city management with new technologies
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Event-triggered finite-time command-filtered tracking control for nonlinear time-delay cyber physical systems against cyber attacks Front. Inform. Technol. Electron. Eng. (IF 3.0) Pub Date : 2023-12-29 Yajing Ma, Yuan Wang, Zhanjie Li, Xiangpeng Xie
This article addresses the secure finite-time tracking problem via event-triggered command-filtered control for nonlinear time-delay cyber physical systems (CPSs) subject to cyber attacks. Under the attack circumstance, the output and state information of CPSs is unavailable for the feedback design, and the classical coordinate conversion of the iterative process is incompetent in relation to the tracking
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Asynchronous gain-scheduled control of deepwater drilling riser system with hybrid event-triggered sampling and unreliable communication Front. Inform. Technol. Electron. Eng. (IF 3.0) Pub Date : 2023-12-29 Na Pang, Dawei Zhang, Shuqian Zhu
This paper investigates the recoil control of the deepwater drilling riser system with nonlinear tension force and energy-bounded friction force under the circumstances of limited network resources and unreliable communication. Different from the existing linearization modeling method, a triangle-based polytope modeling method is applied to the nonlinear riser system. Based on the polytope model, to
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Diffusion models for time-series applications: a survey Front. Inform. Technol. Electron. Eng. (IF 3.0) Pub Date : 2023-12-28 Lequan Lin, Zhengkun Li, Ruikun Li, Xuliang Li, Junbin Gao
Diffusion models, a family of generative models based on deep learning, have become increasingly prominent in cutting-edge machine learning research. With distinguished performance in generating samples that resemble the observed data, diffusion models are widely used in image, video, and text synthesis nowadays. In recent years, the concept of diffusion has been extended to time-series applications
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Towards resilient average consensus in multi-agent systems: a detection and compensation approach Front. Inform. Technol. Electron. Eng. (IF 3.0) Pub Date : 2023-12-28
Abstract Consensus is one of the fundamental distributed control technologies for collaboration in multi-agent systems such as collaborative handling in intelligent manufacturing. In this paper, we study the problem of resilient average consensus for multi-agent systems with misbehaving nodes. To protect consensus value from being influenced by misbehaving nodes, we address this problem by detecting
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Recursive filtering of multi-rate cyber-physical systems with unknown inputs under adaptive event-triggered mechanisms Front. Inform. Technol. Electron. Eng. (IF 3.0) Pub Date : 2023-12-27 Ying Sun, Miaomiao Fu, Jingyang Mao, Guoliang Wei
Cyber-physical systems (CPSs) take on the characteristics of both multiple rates of information collection and processing and the dependency on information exchanges. The purpose of this paper is to develop a joint recursive filtering scheme that estimates both unknown inputs and system states for multi-rate CPSs with unknown inputs. In cyberspace, the information transmission between the local joint
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Enhancing low-resource cross-lingual summarization from noisy data with fine-grained reinforcement learning Front. Inform. Technol. Electron. Eng. (IF 3.0) Pub Date : 2023-12-27 Yuxin Huang, Huailing Gu, Zhengtao Yu, Yumeng Gao, Tong Pan, Jialong Xu
Cross-lingual summarization (CLS) is the task of generating a summary in a target language from a document in a source language. Recently, end-to-end CLS models have achieved impressive results using large-scale, high-quality datasets typically constructed by translating monolingual summary corpora into CLS corpora. However, due to the limited performance of low-resource language translation models
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A low-noise, high-gain, and large-dynamic-range photodetector based on a JFET and a charge amplifier Front. Inform. Technol. Electron. Eng. (IF 3.0) Pub Date : 2023-12-27 Jinrong Wang, Shuang’e Wu, Chengdong Mi, Yaner Qiu, Xin’ai Bai
We demonstrate a low-noise, high-gain, and large-dynamic-range photodetector (PD) based on a junction field-effect transistor (JFET) and a charge amplifier for the measurement of quantum noise in Bell-state detection (BSD). Particular photodiode junction capacitance allows the silicon N-channel JFET 2sk152 to be matched to the noise requirement for charge amplifier A250. The electronic noise of the
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Event-triggered distributed optimization for model-free multi-agent systems Front. Inform. Technol. Electron. Eng. (IF 3.0) Pub Date : 2023-12-12 Shanshan Zheng, Shuai Liu, Licheng Wang
In this paper, the distributed optimization problem is investigated for a class of general nonlinear model-free multi-agent systems. The dynamical model of each agent is unknown and only the input/output data are available. A model-free adaptive control method is employed, by which the original unknown nonlinear system is equivalently converted into a dynamic linearized model. An event-triggered consensus
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Embedding expert demonstrations into clustering buffer for effective deep reinforcement learning Front. Inform. Technol. Electron. Eng. (IF 3.0) Pub Date : 2023-12-07 Shihmin Wang, Binqi Zhao, Zhengfeng Zhang, Junping Zhang, Jian Pu
As one of the most fundamental topics in reinforcement learning (RL), sample efficiency is essential to the deployment of deep RL algorithms. Unlike most existing exploration methods that sample an action from different types of posterior distributions, we focus on the policy sampling process and propose an efficient selective sampling approach to improve sample efficiency by modeling the internal
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Magnetically driven microrobots moving in a flow: a review Front. Inform. Technol. Electron. Eng. (IF 3.0) Pub Date : 2023-12-07 Jiamiao Miao, Xiaopu Wang, Yan Zhou, Min Ye, Hongyu Zhao, Ruoyu Xu, Huihuan Qian
Magnetically driven microrobots hold great potential to perform specific tasks more locally and less invasively in the human body. To reach the lesion area in vivo, microrobots should usually be navigated in flowing blood, which is much more complex than static liquid. Therefore, it is more challenging to design a corresponding precise control scheme. A considerable amount of work has been done regarding
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Software development in the age of intelligence: embracing large language models with the right approach Front. Inform. Technol. Electron. Eng. (IF 3.0) Pub Date : 2023-12-07 Xin Peng
Embracing LLMs is definitely a correct and even necessary direction for software enterprises to improve quality and efficiency. However, achieving systematic and comprehensive intelligent software development still requires careful consideration and there is much fundamental work to do. For enterprises, solidifying the digitization and knowledge accumulation of software development, as well as the
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A novel topology with controllable wideband baseband impedance for power amplifiers Front. Inform. Technol. Electron. Eng. (IF 3.0) Pub Date : 2023-12-07 Yao Yao, Zhijiang Dai, Mingyu Li
This paper presents a novel topology to control the baseband impedance of a power amplifier (PA) to avoid performance deterioration in concurrent dual-band mode. This topology can avoid pure resonance of capacitors and inductors LC, which leads to a high impedance at some frequency points. Consequently, it can be applied to transmitters that are excited by broadband signals. In particular, by adjusting
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A high-isolation coupled-fed building block for metal-rimmed 5G smartphones Front. Inform. Technol. Electron. Eng. (IF 3.0) Pub Date : 2023-12-07 Aidi Ren, Chengwei Yu, Lixia Yang, Wei Cui, Zhixiang Huang, Ying Liu
A compact coupled-fed dual-antenna building block has been constructed in this study. The building block is simple in structure and easy to process, and has a high degree of isolation. The dual-antenna building block is composed of a coupled-fed loop antenna and a coupled-fed slot antenna that completely overlap. Based on this dual-antenna module, an eight-element MIMO system is designed, and the fabricated
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Hybrid-driven Gaussian process online learning for highly maneuvering multi-target tracking Front. Inform. Technol. Electron. Eng. (IF 3.0) Pub Date : 2023-12-07 Qiang Guo, Long Teng, Tianxiang Yin, Yunfei Guo, Xinliang Wu, Wenming Song
The performance of existing maneuvering target tracking methods for highly maneuvering targets in cluttered environments is unsatisfactory. This paper proposes a hybrid-driven approach for tracking multiple highly maneuvering targets, leveraging the advantages of both data-driven and model-based algorithms. The time-varying constant velocity model is integrated into the Gaussian process (GP) of online
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High-emitter identification for heavy-duty vehicles by temporal optimization LSTM and an adaptive dynamic threshold Front. Inform. Technol. Electron. Eng. (IF 3.0) Pub Date : 2023-12-07 Zhenyi Xu, Renjun Wang, Yang Cao, Yu Kang
Heavy-duty diesel vehicles are important sources of urban nitrogen oxides (NOx) in actual applications for environmental compliance, emitting more than 80% of NOx and more than 90% of particulate matter (PM) in total vehicle emissions. The detection and control of heavy-duty diesel emissions are critical for protecting public health. Currently, vehicles on the road must be regularly tested, every six
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A multimodal dense convolution network for blind image quality assessment Front. Inform. Technol. Electron. Eng. (IF 3.0) Pub Date : 2023-12-07 Nandhini Chockalingam, Brindha Murugan
Technological advancements continue to expand the communications industry’s potential. Images, which are an important component in strengthening communication, are widely available. Therefore, image quality assessment (IQA) is critical in improving content delivered to end users. Convolutional neural networks (CNNs) used in IQA face two common challenges. One issue is that these methods fail to provide
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A modified harmony search algorithm and its applications in weighted fuzzy production rule extraction Front. Inform. Technol. Electron. Eng. (IF 3.0) Pub Date : 2023-12-07 Shaoqiang Ye, Kaiqing Zhou, Azlan Mohd Zain, Fangling Wang, Yusliza Yusoff
Harmony search (HS) is a form of stochastic meta-heuristic inspired by the improvisation process of musicians. In this study, a modified HS with a hybrid cuckoo search (CS) operator, HS-CS, is proposed to enhance global search ability while avoiding falling into local optima. First, the randomness of the HS pitch disturbance adjusting method is analyzed to generate an adaptive inertia weight according
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Dynamic parameterized learning for unsupervised domain adaptation Front. Inform. Technol. Electron. Eng. (IF 3.0) Pub Date : 2023-12-07 Runhua Jiang, Yahong Han
Unsupervised domain adaptation enables neural networks to transfer from a labeled source domain to an unlabeled target domain by learning domain-invariant representations. Recent approaches achieve this by directly matching the marginal distributions of these two domains. Most of them, however, ignore exploration of the dynamic trade-off between domain alignment and semantic discrimination learning
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A cloud–edge–device collaborative offloading scheme with heterogeneous tasks and its performance evaluation Front. Inform. Technol. Electron. Eng. (IF 3.0) Pub Date : 2023-12-01 Xiaojun Bai, Yang Zhang, Haixing Wu, Yuting Wang, Shunfu Jin
How to collaboratively offload tasks between user devices, edge networks (ENs), and cloud data centers is an interesting and challenging research topic. In this paper, we investigate the offloading decision, analytical modeling, and system parameter optimization problem in a collaborative cloud–edge–device environment, aiming to trade off different performance measures. According to the differentiated
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Performance analysis on reconfigurable intelligent surface and network-controlled repeater in 3GPP release-18 Front. Inform. Technol. Electron. Eng. (IF 3.0) Pub Date : 2023-12-01
Abstract As a candidate technique to achieve sixth-generation wireless communication (6G), reconfigurable intelligent surface (RIS) has become popular in both academia and industry. For better exploration of the advantages of RIS, we compare the performances of RIS and network-controlled repeater (NCR) in 3GPP release-18. We first theoretically analyze the received signal power and signal-to-noise
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Joint radio frequency front-end and digital back-end antijamming scheme based on a metasurface antenna array Front. Inform. Technol. Electron. Eng. (IF 3.0) Pub Date : 2023-12-01
Abstract An array’s degree of freedom (DoF) determines the number of jamming incidents that can be managed and the antijamming performance. Conventional arrays can improve the DoF only by increasing the number of antennas. On the other hand, when the received signal is digitized, high-power jamming will reduce the number of bits used to represent the desired signal, further increasing the difficulty
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Attention-based efficient robot grasp detection network Front. Inform. Technol. Electron. Eng. (IF 3.0) Pub Date : 2023-11-07 Xiaofei Qin, Wenkai Hu, Chen Xiao, Changxiang He, Songwen Pei, Xuedian Zhang
To balance the inference speed and detection accuracy of a grasp detection algorithm, which are both important for robot grasping tasks, we propose an encoder–decoder structured pixel-level grasp detection neural network named the attention-based efficient robot grasp detection network (AE-GDN). Three spatial attention modules are introduced in the encoder stages to enhance the detailed information
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RFPose-OT: RF-based 3D human pose estimation via optimal transport theory Front. Inform. Technol. Electron. Eng. (IF 3.0) Pub Date : 2023-11-07 Cong Yu, Dongheng Zhang, Zhi Wu, Zhi Lu, Chunyang Xie, Yang Hu, Yan Chen
This paper introduces a novel framework, i.e., RFPose-OT, to enable three-dimensional (3D) human pose estimation from radio frequency (RF) signals. Different from existing methods that predict human poses from RF signals at the signal level directly, we consider the structure difference between the RF signals and the human poses, propose a transformation of the RF signals to the pose domain at the
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A knowledge-guided and traditional Chinese medicine informed approach for herb recommendation Front. Inform. Technol. Electron. Eng. (IF 3.0) Pub Date : 2023-11-07 Zhe Jin, Yin Zhang, Jiaxu Miao, Yi Yang, Yueting Zhuang, Yunhe Pan
Traditional Chinese medicine (TCM) is an interesting research topic in China’s thousands of years of history. With the recent advances in artificial intelligence technology, some researchers have started to focus on learning the TCM prescriptions in a data-driven manner. This involves appropriately recommending a set of herbs based on patients’ symptoms. Most existing herb recommendation models disregard
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Uncertainty-aware complementary label queries for active learning Front. Inform. Technol. Electron. Eng. (IF 3.0) Pub Date : 2023-11-07 Shengyuan Liu, Ke Chen, Tianlei Hu, Yunqing Mao
In this paper, we tackle the problem of ALCL (Liu et al., 2023). The objective of ALCL is to directly reduce the cost of annotation actions in AL, while providing a feasible approach for obtaining complementary labels. To solve ALCL, we design a sampling strategy USD, which uses the uncertainty in deep learning to guide the queries of active learning in this novel setup. Moreover, we upgrade the WEBB
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Synchronization transition of a modular neural network containing subnetworks of different scales Front. Inform. Technol. Electron. Eng. (IF 3.0) Pub Date : 2023-11-07 Weifang Huang, Lijian Yang, Xuan Zhan, Ziying Fu, Ya Jia
Time delay and coupling strength are important factors that affect the synchronization of neural networks. In this study, a modular neural network containing subnetworks of different scales was constructed using the Hodgkin–Huxley (HH) neural model; i.e., a small-scale random network was unidirectionally connected to a large-scale small-world network through chemical synapses. Time delays were found
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Path guided motion synthesis for Drosophila larvae Front. Inform. Technol. Electron. Eng. (IF 3.0) Pub Date : 2023-11-07 Junjun Chen, Yijun Wang, Yixuan Sun, Yifei Yu, Zi’ao Liu, Zhefeng Gong, Nenggan Zheng
The deformability and high degree of freedom of mollusks bring challenges in mathematical modeling and synthesis of motions. Traditional analytical and statistical models are limited by either rigid skeleton assumptions or model capacity, and have difficulty in generating realistic and multi-pattern mollusk motions. In this work, we present a large-scale dynamic pose dataset of Drosophila larvae and
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Robust cross-modal retrieval with alignment refurbishment Front. Inform. Technol. Electron. Eng. (IF 3.0) Pub Date : 2023-11-07 Jinyi Guo, Jieyu Ding
Cross-modal retrieval tries to achieve mutual retrieval between modalities by establishing consistent alignment for different modal data. Currently, many cross-modal retrieval methods have been proposed and have achieved excellent results; however, these are trained with clean cross-modal pairs, which are semantically matched but costly, compared with easily available data with noise alignment (i.e
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Towards robust neural networks via a global and monotonically decreasing robustness training strategy Front. Inform. Technol. Electron. Eng. (IF 3.0) Pub Date : 2023-11-07 Zhen Liang, Taoran Wu, Wanwei Liu, Bai Xue, Wenjing Yang, Ji Wang, Zhengbin Pang
Robustness of deep neural networks (DNNs) has caused great concerns in the academic and industrial communities, especially in safety-critical domains. Instead of verifying whether the robustness property holds or not in certain neural networks, this paper focuses on training robust neural networks with respect to given perturbations. State-of-the-art training methods, interval bound propagation (IBP)
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Exploring nonlinear spatiotemporal effects for personalized next point-of-interest recommendation Front. Inform. Technol. Electron. Eng. (IF 3.0) Pub Date : 2023-09-22 Xi Sun, Zhimin Lv
Next point-of-interest (POI) recommendation is an important personalized task in location-based social networks (LBSNs) and aims to recommend the next POI for users in a specific situation with historical check-in data. State-of-the-art studies linearly discretize the user’s spatiotemporal information and then use recurrent neural network (RNN) based models for modeling. However, these studies ignore
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Impact of distance between two hubs on the network coherence of tree networks Front. Inform. Technol. Electron. Eng. (IF 3.0) Pub Date : 2023-09-22 Daquan Li, Weigang Sun, Hongxiang Hu
We study the impact of the distance between two hubs on network coherence defined by Laplacian eigenvalues. Network coherence is a measure of the extent of consensus in a linear system with additive noise. To obtain an exact determination of coherence based on the distance, we choose a family of tree networks with two hubs controlled by two parameters. Using the tree’s regular structure, we obtain
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Mixture test strategy optimization for analog systems Front. Inform. Technol. Electron. Eng. (IF 3.0) Pub Date : 2023-09-22 Wenjuan Mei, Zhen Liu, Ouhang Li, Yuanzhang Su, Yusong Mei, Yongji Long
Since analog systems play an essential role in modern equipment, test strategy optimization for analog systems has attracted extensive attention in both academia and industry. Although many methods exist for the implementation of effective test strategies, diagnosis for analog systems suffers from the impacts of various stresses due to sophisticated mechanism and variable operational conditions. Consequently
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Interactive medical image segmentation with self-adaptive confidence calibration Front. Inform. Technol. Electron. Eng. (IF 3.0) Pub Date : 2023-09-22 Chuyun Shen, Wenhao Li, Qisen Xu, Bin Hu, Bo Jin, Haibin Cai, Fengping Zhu, Yuxin Li, Xiangfeng Wang
Interactive medical image segmentation based on human-in-the-loop machine learning is a novel paradigm that draws on human expert knowledge to assist medical image segmentation. However, existing methods often fall into what we call interactive misunderstanding, the essence of which is the dilemma in trading off short- and long-term interaction information. To better use the interaction information
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LDformer: a parallel neural network model for long-term power forecasting Front. Inform. Technol. Electron. Eng. (IF 3.0) Pub Date : 2023-09-22 Ran Tian, Xinmei Li, Zhongyu Ma, Yanxing Liu, Jingxia Wang, Chu Wang
Accurate long-term power forecasting is important in the decision-making operation of the power grid and power consumption management of customers to ensure the power system’s reliable power supply and the grid economy’s reliable operation. However, most time-series forecasting models do not perform well in dealing with long-time-series prediction tasks with a large amount of data. To address this