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Interest Points Analysis for Internet Forum Based on Long-Short Windows Similarity

Computers, Materials & Continua 2022年第8期72卷 3247-3267页

作者： Xinghai Ju Jicang Lu Xiangyang Luo Gang Zhou Shiyu Wang Shunhang Li Yang Yang State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001China School of Computing and Information Systems Singapore Management University188065Singapore College of Computer Science and Big Data Fuzhou UniversityFuzhou350116China

For Internet forum Points of Interest(PoI),existing analysis methods are usually lack of usability analysis under different conditions and ignore the long-term variation,which lead to blindness in method *** address this problem,this paper proposed a PoI variation prediction framework based on similarity analysis between long and short *** on the framework,this paper presented 5 PoI analysis algorithms which can be categorized into 2 types,i.e.,the traditional sequence analysis methods such as autoregressive integrated moving average model(ARIMA),support vector regressor(SVR),and the deep learning methods such as convolutional neural network(CNN),long-short term memory network(LSTM),Transformer(TRM).Specifically,this paper firstly divides observed data into long and short windows,and extracts key words as PoI of each ***,the PoI similarities between long and short windows are calculated for training and ***,series of experiments is conducted based on real Internet forum *** results show that,all the 5 algorithms could predict PoI variations well,which indicate effectiveness of the proposed *** the length of long window is small,traditional methods perform better,and SVR is the *** the contrary,the deep learning methods show superiority,and LSTM performs *** results could provide beneficial references for PoI variation analysis and prediction algorithms selection under different parameter configurations.

关键词： Point of interest(PoI)analysis long and short windows sequential analysis deep learning

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Optical resonance and chaos control in a Reuleaux-triangle microcavity

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Chinese Optics Letters 2024年第11期22卷 54-60页

作者： Jinhao Fei Xiaobei Zhang Qi Zhang Yong Yang Zijie Wang Zong Cao Fang Zhang Chuanlu Deng Yi Huang Tingyun Wang Key Laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced CommunicationShanghai Institute for Advanced Communication and Data ScienceSchool of Communication and Information EngineeringShanghai UniversityShanghai 200444China

The mode selection ability of whispering gallery mode(WGM) microcavities is crucial in applications such as sensors, lasers,and nonlinear optics. Though various shapes of microcavities have been studied for mode suppression, single-mode operation is still difficult to realize. Here, we demonstrate a Reuleaux-triangle resonator(RTR) with corner-cuts, which can reconstruct phase space to realize single-mode control. According to classical ray dynamics, the boundary of the RTR is optimized to obtain the stable 9-period islands with the consideration of suppression of standing waves and strong light scattering. The single-mode characteristic of the RTR is experimentally verified under the optimal coupling position, with a Q factor of 1.1 × 10^(4). Our investigation reveals a new thread for mode suppression with potential in the fields of single-mode lasers and nonlinear optics.

关键词： deformed microcavity optical chaos mode suppression.

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A New Estimator for the Number of High-Dimensional Signals Using Ratio of Higher-Order Moments

A New Estimator for the Number of High-Dimensional Signals U...

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IET International Radar Conference 2023, IRC 2023

作者： Zhang, Lewen Rong, Yao Tang, Mengjiao Du, Xiaolin Li, Fan Yunnan Key Laboratory of Statistical Modeling and Data Analysis Yunnan University Kunming China School of Computer and Control Engineering Yantai University Yantai China CAAC Key Laboratory of Flight Techniques and Flight Safety Civil Aviation Flight University of China Guanghan China

ISBN: (纸本)9781839539954

This paper provides a novel estimation procedure for determining the number of high-dimensional complex-valued signals embedded in Gaussian white noise. Initially, this problem is formulated as a sequence of nested hypothesis tests. To enhance the decision performance, we introduce new test statistics based on the ratio of the fourth- and square second-order moments of the population covariance eigenvalues. This enables us to leverage higher-order moments information in the observed data, distinguishing it from conventional tests that rely solely on the first and second order moments. In the context of high-dimensional, complex-valued data and within a sub-sphericity testing framework, unbiased and consistent estimates are derived for these two higher-order moments. Subsequently, we conduct an asymptotic analysis to incoporate these estimates into the construction of the test statistics and the design of our proposed estimation scheme for determining the number of sources. Finally, numerical examples are presented to show its effectiveness and superiority over several classical estimation methods. © The Institution of engineering & Technology 2023.

关键词： White noise

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Multimodal Identity Recognition of Spiking Neural Network Based on Brain-Inspired Reward Propagation 22

Multimodal Identity Recognition of Spiking Neural Network Ba...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Liu, Yang Zhang, Hongxia Li, Yahui Wang, Quanqiang Liu, Cheng Henan University Henan Key Laboratory of Big Data Analysis and Processing Henan Kaifeng475004 China Henan University School of Computer and Information Engineering Henan Kaifeng475004 China Huaihe Hospital of Henan University Department of Clinical Laboratory Henan Kaifeng475004 China

ISBN: (纸本)9798331509712

Deep learning has strongly promoted the research progress of unimodal feature learning such as face recognition and speech recognition, but it still needs to be strengthened in multimodal data representation learning, especially the problem of high power consumption, which is not conducive to the real-time application of mobile devices under limited resources. In order to realize multimodal identity recognition for low-energy edge smart devices, this paper proposes a multimodal identity recognition method of spiking neural network based on brain-like mechanism (SNN-MIR-BiM). Simulating the attention mechanism of the cognitive system to specific information improves the response of multimodal features, mimics the regulation mechanism of the neurotransmitter dopamine in the nervous system, and releases reward signals from the recognition results, which affects the neuronal state and regulates the synaptic weights through global propagation, and then optimizes the network structure. Experimental results on large-scale audio and video datasets show that the proposed method can reach 98.94% recognition accuracy, and the energy consumption of the model is about 1/10 of that of CNN under the same architecture, which verifies the effectiveness of the attention mechanism and reward propagation in network design. This research is of great practical significance in the industry applications of unmanned autonomous systems such as human-computer interaction of intelligent machines and intelligent security. © 2024 IEEE.

关键词： Face recognition

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An effective scheme for top-k frequent itemset mining under differential privacy conditions

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Science China(Information Sciences) 2020年第5期63卷 200-202页

作者： Wenjuan LIANG Hong CHEN Jing ZHANG Dan ZHAO Cuiping LI Key Lab of Data Engineering and Knowledge Engineering of MOE Renmin University of China College of Computer and Information Engineering Henan University

Dear editor,Frequent itemset mining (FIM) is important in many data mining applications [1], such as web log mining and trend analysis. However, if the data are sensitive (e.g., web browsing history), directly releasing frequent itemsets and their support may breach user privacy. The protection of user privacy while obtaining statistical information is im-

关键词： An effective scheme for top-k frequent itemset mining under differential privacy conditions

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Intersecting-Boundary-Sensitive Fingerprinting for Tampering Detection of DNN Models 41

Intersecting-Boundary-Sensitive Fingerprinting for Tampering...

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41st International Conference on Machine Learning, ICML 2024

作者： Bai, Xiaofan He, Chaoxiang Ma, Xiaojing Zhu, Bin Benjamin Jin, Hai School of Cyber Science and Engineering Huazhong University of Science and Technology China National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China Microsoft United States School of Computer Science and Technology Huazhong University of Science and Technology China Cluster and Grid Computing Lab China

Cloud-based AI services offer numerous benefits but also introduce vulnerabilities, allowing for tampering with deployed DNN models, ranging from injecting malicious behaviors to reducing computing resources. Fingerprint samples are generated to query models to detect such tampering. In this paper, we present Intersecting-Boundary-Sensitive Fingerprinting (IBSF), a novel method for black-box integrity verification of DNN models using only top-1 labels. Recognizing that tampering with a model alters its decision boundary, IBSF crafts fingerprint samples from normal samples by maximizing the partial Shannon entropy of a selected subset of categories to position the fingerprint samples near decision boundaries where the categories in the subset intersect. These fingerprint samples are almost indistinguishable from their source samples. We theoretically establish and confirm experimentally that these fingerprint samples' expected sensitivity to tampering increases with the cardinality of the subset. Extensive evaluation demonstrates that IBSF surpasses existing state-of-the-art fingerprinting methods, particularly with larger subset cardinality, establishing its state-of-the-art performance in black-box tampering detection using only top-1 labels. The IBSF code is available at: https://***/CGCL-codes/IBSF. Copyright 2024 by the author(s)

关键词： HTTP

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Impacts of aggregation method on the simulation of multispectral broadband reflectance 4

Impacts of aggregation method on the simulation of multispec...

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4th International Conference on Geology, Mapping, and Remote Sensing, ICGMRS 2023

作者： Chen, Feng Yu, Guanghao Gao, Zhibin Song, Yuejun College of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China Big Data Institute of Digital Natural Disaster Monitoring in Fujian Xiamen University of Technology Xiamen361024 China Jiangxi Key Laboratory of Soil Erosion and Prevention Jiangxi Academy of Water Science and Engineering Nanchang330029 China

ISBN: (纸本)9781510672741

Simulation of multispectral broadband data from hyperspectral profiles is applicable for many purposes, mainly including sensor assessment and cross-calibration. For the simulation process, several aggregation methods have been proposed. In this paper, three aggregation methods were implemented, to investigate the impacts associated with method on the simulation of multispectral broadband. Comparative analyses were conducted in terms of channel reflectance and several derived vegetation indices, while mainly taking the wide field of view (WFV) sensor onboard Gaofen-6 for case study. Furthermore, impacts on the between-sensor comparability with the Landsat 8 Operational Land Imager (OLI) were discussed. Differences among aggregation methods in reflectance of the Gaofen-6 WFV varied along with channels, and obvious differences were observed in channels over near-infrared (770-890 nm) and red-edge (690-730 nm). Between-sensor difference relative to the Landsat 8 OLI was observed mainly over near-infrared, while the variations among aggregation methods were about 0.4%-2.0%. © 2024 COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Reflection

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Review of graph construction and graph learning in stock price prediction 9th

Review of graph construction and graph learning in stock pri...

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9th International Conference on Information Technology and Quantitative Management, ITQM 2022

作者： Wang, Yunong Qu, Yi Chen, Zhensong Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China School of Management and Engineering Capital University of Economics and Business Beijing100070 China

Precise prediction of stock prices leads to more profits and more effective risk prevention, which is of great significance to both investors and regulators. Recent years, various kinds of information not directly-relevant with stock prices have received more attention, such as texts, images or connections. These external information has the potential of reflecting or influencing fluctuations, and thus, given the utilization of advanced analyzing techniques, the forecasting performance of stock prices could be promoted substantially. For instance, graph neural network models have expanded into many other disciplines including stock price prediction, and exhibited impressive representation learning ability. However, in stock markets, well-defined graphs are rarely seen and how to formulate the graph structures needed remains a challenging problem. Towards this end, this article presents a comprehensive overview of graph construction and graph learning in stock price prediction, by reviewing the existing studies, summarizing its general paradigm, special cases and proposing possible prospects. Our research not only systematically reveals the feasible ways of constructing graphs in financial markets, but also provides insights for further implementations of graph learning models into stock prediction tasks. © 2022 The Authors. Published by Elsevier B.V.

关键词： Financial markets

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Adaptive Activation Steering: A Tuning-Free LLM Truthfulness Improvement Method for Diverse Hallucinations Categories 25

Adaptive Activation Steering: A Tuning-Free LLM Truthfulness...

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34th ACM Web Conference, WWW 2025

作者： Wang, Tianlong Jiao, Xianfeng Zhu, Yinghao Chen, Zhongzhi He, Yifan Chu, Xu Gao, Junyi Wang, Yasha Ma, Liantao School of Software and Microelectronics Peking University Beijing China Key Laboratory of High Confidence Software Technologies Ministry of Education Beijing China National Engineering Research Center for Software Engineering Peking University Beijing China Beihang University Beijing China Center on Frontiers of Computing Studies Peking University Beijing China Centre for Medical Informatics University of Edinburgh Edinburgh United Kingdom Health Data Research UK London United Kingdom Key Laboratory of High Confidence Software Technologies Ministry of Education National Engineering Research Center for Software Engineering Peking University Beijing China

ISBN: (纸本)9798400712746

Recent studies have indicated that Large Language Models (LLMs) harbor an inherent understanding of truthfulness, yet often fail to consistently express it and generate false statements. This gap between "knowing" and "telling" poses a challenge for ensuring the truthfulness of generated content. Inspired by recent work on the practice of encoding human-interpretable concepts linearly within large language models, we treat truthfulness as a specially linearly encoded concept within LLMs, and introduce Adaptive Activation Steering (ACT), a tuning-free method that adaptively shifts LLM’s activations in the "truthful" direction during inference. ACT addresses diverse categories of hallucinations by utilizing diverse truthfulness-related steering vectors and adjusting the steering intensity adaptively. Applied as an add-on across various models, ACT significantly improves truthfulness in LLaMA (↑ 142%), LLaMA2 (↑ 24%), Alpaca (↑ 36%), Vicuna (↑ 28%), LLaMA2-Chat (↑ 19%), and LLaMA3(↑ 34%). Furthermore, we verify ACT’s scalability across larger models (13B, 33B, 65B), underscoring the adaptability of ACT to large-scale language models. © 2025 Copyright held by the owner/author(s).

关键词： hallucination

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BjuIR:A multi-omics database with various tools for accelerating functional genomics research in Brassica juncea

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Plant Communications 2024年第8期5卷 96-99页

作者： Linna Zhang Jinyuan Xiao Congyuan Liang Yifan Chen Changchun Yu Xinle Zhao Jiawei Li Mingli Yan Qian Yang Hao Chen Zhongsong Liu Zhengjie Wan Zhiquan Yang Qing-Yong Yang National Key Laboratory for Germplasm Innovation and Utilization of Horticultural Crops College of InformaticsHuazhong Agricultural UniversityWuhan 430070China Hubei Key Laboratory of Agricultural Bioinformatics and Hubei Engineering Technology Research Center of Agricultural Big Data College of InformaticsHuazhong Agricultural UniversityWuhan 430070China National Key Laboratory of Crop Genetic Improvement Hubei Hongshan LaboratoryHuazhong Agricultural UniversityWuhan 430070China Yazhouwan National Laboratory Sanya 572025China National Key Laboratory for Germplasm Innovation and Utilization of Horticultural Crops College of Horticulture and Forestry SciencesHuazhong Agricultural UniversityWuhan 430070China Crop Research Institute Hunan Academy of Agricultural SciencesChangsha 410125China College of Agronomy Hunan Agricultural UniversityChangsha 410128China

Dear Editor,Advances in high-throughput omics technologies,along with methodologies for integrating multi-omics datasets,have substantially enhanced the efficiency of identifying candidate genes in breeding(Gusev et al.,2018;Gupta et al.,2019).However,this process is often complex and *** address this challenge,databases that integrate extensive data and enable convenient and efficient functional genomics studies are being developed(Ma et al.,2021;Yang et al.,2023).

关键词： functional integrating convenient

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