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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Hua Jiang Qiuxia Shi Siying Liu Jiuying Zhang Qinglin Zhao Bin Hu Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Key Laboratory of Special Functional Materials and Structural Design Ministry of Education Lanzhou University Lanzhou China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences Chinese Academy of Sciences Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University & Institute of Semiconductors Engineering Research Center of Open Source Software and Real-Time System (Lanzhou University) Ministry of Education Key Laboratory of Special Functional Materials and Structural Design (Lanzhou University) Ministry of Education Lanzhou China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Electroencephalogram (EEG) equipped with high time resolution that distracted by incoherent brain sources which including ocular artifacts (OAs) generating by blinks is of great significance. Therefore, these OAs got corrected before extracting information from EEG is indispensable. Improved Moment Matching (IMM) is a high-speed denoising algorithm suitable for removing OA in multi-channel EEG, which is an improvement of the moment matching method used to remove stripe noise in hyperspectral images. On foundation of this, this paper proposes an optimization algorithm for IMM based on Gaussian decay function (IMM_G). In the first place, the construction of the reference signal is optimized via utilizing a Gaussian decay function, thereby preventing the signal distortion caused by the filter. And then, a method for judging the to-be-processed interval is proposed to realizes the individualized processing of different channels. As a result, through quantitative comparison experiments with simulated data and real data from the UAIS laboratory, it was identified that IMM_G showed less time complexity, significantly enhanced arithmetic speed and denoising consequence while the detail retention ability of the original method for the non-blinking region of multi-channel EEG data is maintained. Hence, this method could be extensively used in High-density EEG (hdEEG) OAs removal scene.

关键词： Electrooculography Noise reduction Filtering algorithms Distortion Electroencephalography Recording Time-domain analysis

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Ultra-Fast and Ultra-Efficient Removal of Cr (Vi) by the Aqueous Solutions of Monolayer Mxene (Ti3C2Tx)

SSRN

引用

SSRN 2022年

作者： Wang, Hongwei Wu, Fang Wang, Zhiheng Ruan, Haibo Wang, Yang Zhang, Shufang Luo, Haijun Zheng, Zhiqin Zhou, Miao Fang, Liang State Key Laboratory of Power Transmission Equipment & System Safety and New Technology Chongqing Key Laboratory of Soft Condensed Matter Physics and Smart Materials College of Physics Chongqing University Chongqing 400044 China Research Center for Materials Interdisciplinary Sciences Chongqing University of Arts and Sciences Chongqing 402160 China Key Laboratory of Optoelectronic Technology and Systems Education Ministry of China College of Optoelectronic Engineering Chongqing University Chongqing 400044 China College of Software Chongqing College of Electronic Engineering Chongqing 401331 China Key Laboratory on Optoelectronic Functional Materials College of Physics and Electronic Engineering Chongqing Normal University Chongqing 401331 China Fundamental Science on Nuclear Wastes and Environmental Safety Laboratory Southwest University of Science and Technology Sichuan Mianyang621010 China

Chromium (VI) removal is crucial to the safety of water resources, but there is still a lack of effective fast dislodge methods, especially the on-site rapid remediation and emergency removal technology. Herein, the monolayer (ML) Ti3C2Tx nanosheets (NSs) were prepared to remove Cr (VI) from water. The solution sample of the two-dimensional (2D) Ti3C2Tx NSs demonstrate ultrafast desorption kinetics (the concentration decreasing from 300 to 3 mg L-1 in 10 min) and impressively high capacity (1483 mg g-1), which is several times higher than that of the most of the previous reported Cr (VI) adsorption by Ti3C2Tx. The analysis of the adsorption kinetics and isotherm models indicates that the adsorption reaction is endothermic, coexists with physical and chemical adsorption, but mainly ML chemical adsorption. The removal efficiency decreases gradually with pH increasing from 2 to 12, while the maximum adsorption capacity increases with increasing temperature. The joint study of the experimental analysis and theoretical calculation based on the density functional theory (DFT) demonstrates that the extraordinary outstanding removal performance of Cr (VI) is due to the synergetic effect of reduction reaction removal and electrostatic adsorption. It is found that the Cr (VI) is mainly reduced by the OH- terminals on Ti3C2, and the detoxified Cr (III) is anchored on the surface of Ti3C2Tx through complexation reaction with O terminal. The advantages of ultrafast desorption kinetics, extra-high adsorption capacity, and broad working pH range of the single-layered Ti3C2Tx for Cr (VI) removal make it a remarkable candidate for rapid and urgent removal of Cr (VI) wastewater. © 2022, The Authors. All rights reserved.

关键词： Density functional theory

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IGTP: A next point-of-interest recommendation method that integrates geospatial and temporal preferences

Research Square

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research Square 2021年

作者： Jiao, Xu Xiao, Yingyuan Zheng, Wenguang Zhu, Ke School of General Education Tianjin Foreign Studies University Tianjin China Engineering Research Center of Learning-Based Intelligent System Ministry of Education Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin China

With the rapid development of location-based social networks(LBSNs), point-of-interest(POI) recommendation has become an important way to meet the personalized needs of users. The purpose of POI recommendation is to provide personalized POI recommendation services for users. However, general POI recommendations cannot meet the individual needs of users. This is mainly because the decision-making process for users to choose POIs is very complicated and will be affected by various user contexts such as time, location, etc. This paper proposes a next POI recommendation method that integrates geospatial and temporal preferences, called IGTP. Compared with general POI recommendation, IGTP can provide more personalized recommendations for users according to their context information. First, IGTP uses users’ preferences information to model users’ check-in histories to effectively overcome the challenge of extremely sparse check-in data. Secondly, IGTP takes into account the geographic distance and density factors that affect people’s choice of POIs, and limits POIs to be recommended to the potential activitive area centered on the current loca- tion of the target user. Finally, IGTP integrates geospatial and users’ temporal preferences information into a unified recommendation process. Compared with six advanced baseline methods, the experimental results demonstrate that IGTP achieves much better performance. © 2021, CC BY.

关键词： Location

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CFDIL: A context-aware feature deep interaction learning for app recommendation

Research Square

引用

research Square 2021年

作者： Hao, Qingbo Zhu, Ke Wang, Chundong Wang, Peng Mo, Xiuliang Liu, Zhen Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin China School of Computer Science and Engineering Tianjin University of Technology Tianjin China Engineering Research Center of Learning-Based Intelligent System Ministry of Education Tianjin China Graduate School of Engineering Nagasaki Institute of Applied Science Nagasaki Japan

The rapid development of Mobile Internet has spawned various mobile applications (apps). A large number of apps make it difficult for users to choose apps conveniently, causing the app overload problem. As the most effective tool to solve the problem of app overload, the app recommendation has attracted extensive attention of researchers. Traditional recommendation methods usually use historical data of apps used by users to explore their preferences, and then make an app recommendation list for users. Although the traditional app recommendation methods have achieved certain results, the performance of app recommendation still needs to be improved due to the following two reasons. On the one hand, it is difficult to construct traditional app recommendation models when facing with the sparse user-app interaction data. On the other hand, contextual information has a large impact on users’ app usage preferences, which is often overlooked by traditional app recommendation methods. To overcome the aforementioned problems, we proposed a Context-aware Feature Deep Interaction Learning (CFDIL) method to explore user preferences, and then perform app recommendation by learning potential user-app relationships in different contexts. The novelty of CFDIL is as follows: (1) CFDIL incorporates contextual features into users’ preferences modeling by constructing a novel user and app feature portrait. (2) The problem of data sparsity is effectively solved by the use of dense user and app feature portraits, as well as the tensor operations for label sets. (3) CFDIL trains a new deep network structure, which can make accurate app recommendation using the contextual information and attribute information of users and apps. We applied CFDIL on three real datasets and conducted extensive experiments, which showed that CFDIL outperformed the benchmark method. © 2021, CC BY.

关键词： User profile

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Role of strategy update rules in the spatial memory-based mixed strategy games

引用

The European Physical Journal B 2021年第1期94卷 1-11页

作者： Zhang, Fan Wang, Juan Gao, Hongyu Li, Xiaopeng Xia, Chengyi Tianjin Key Laboratory for Control Theory and Complicated Industry Systems Tianjin University of Technology Tianjin People’s Republic of China School of Electrical and Electronic Engineering Tianjin University of Technology Tianjin People’s Republic of China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin People’s Republic of China Engineering Research Center of Learning-Based Intelligent System Ministry of Education Tianjin People’s Republic of China

Based on a continuous mixed strategy game model considering the memory mechanism and the uncertainty of individual decision-making, we explore the influence of several different update rules on the spatial cooperation behavior. Here, we mainly investigate the role of the Moran-like process, Fermi rule and replicator dynamics in the evolution of cooperation. Meanwhile, we further compare the evolution of cooperation when the memory mechanism and individual uncertainty are taken into account. In particular, memory length M and strategy adjustment factor $$\delta $$ can promote the cooperation behavior in different ways. Extensive Monte Carlo simulations indicate that the Moran-like process can generally drive the expansion of cooperative clusters faster and finally achieve the highest frequency of cooperation although the Fermi update rule performs better under the same condition. Moreover, the replicator dynamics create the worst scenario as far as the evolution of cooperation is concerned. Therefore, the level of cooperation strongly depends on the strategy update rules; these findings may be helpful to understand and analyze the evolutionary process of cooperation in many real-world natural and social systems.

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Noncontact Doppler Radar-based Heart Rate Detection on the SVD and ANC

Noncontact Doppler Radar-based Heart Rate Detection on the S...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Qiuxia Shi Bin Hu Fuze Tian Qinglin Zhao Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University School of Computer and Communication Lanzhou University of Technology Ministry of Education Key Laboratory of Special Functional Materials and Structural Design (Lanzhou University) Lanzhou China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University & Institute of Semiconductors Chinese Academy of Sciences Engineering Research Center of Open Source Software and Real-Time System(Lanzhou University) Ministry of Education Key Laboratory of Special Functional Materials and Structural Design Ministry of Education Lanzhou University Lanzhou China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

In the Doppler biological radar-based applications of noncontact measurement of vital signs, effectively extracting heartbeat information from weak thoracic mechanical motion is an important problem to be solved. This study is aimed at extracting heartbeat signal via the technology combined with Short Time Fourier Transform (STFT), Singular Value Decomposition (SVD) and Adaptive Noise Canceller (ANC) from radar recording. The simulated data and the data collected by Doppler radar biosensor realized in laboratory are employed to validate the proposed method. The results show that the proposed method has the ability of detection for the heart rate and heart rate variability indexes in rest state, it has certain advantages in time-consuming and detection accuracy. Therefore, the current method provides another way to process vital sign signals recorded by Doppler radar.

关键词： Biomedical measurement Noise cancellation Time measurement Doppler radar Recording Doppler effect Biosensors

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Hybrid RAG-empowered Multi-modal LLM for Secure Data Management in Internet of Medical Things: A Diffusion-based Contract Approach

arXiv

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arXiv 2024年

作者： Su, Cheng Wen, Jinbo Kang, Jiawen Wang, Yonghua Su, Yuanjia Pan, Hudan Zhong, Zishao Hossain, M. Shamim School of Automation The GuangdongHongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangdong University of Technology Guangzhou510006 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing210016 China School of Automation The Guangdong Basic Research Center of Excellence for Ecological Security and Green Development Key Laboratory for City Cluster Environmental Safety Green Development The Ministry of Education Guangdong University of Technology Guangzhou510006 China School of Automation The Key Laboratory of Intelligent Detection and IoT in Manufacturing Ministry of Education Guangdong University of Technology Guangzhou510006 China School of Automation Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangdong University of Technology Guangzhou510006 China Guangzhou University of Traditional Chinese Medicine State Key Laboratory of Traditional Chinese Medicine Syndrome The Second Affiliated Hospital of Guangzhou University of Chinese Medicine Chinese Medicine Guangdong Laboratory Hengqin 519000 China Second Affiliated Hospital of Guangzhou University of Chinese Medicine Chinese Medicine Guangdong Laboratory Hengqin 519000 China Department of Software Engineering College of Computer and Information Sciences King Saud University Riyadh12372 Saudi Arabia

Secure data management and effective data sharing have become paramount in the rapidly evolving healthcare landscape, especially with the growing integration of the Internet of Medical Things (IoMT). The rise of generative artificial intelligence has further elevated Multi-modal Large Language Models (MLLMs) as essential tools for managing and optimizing healthcare data in IoMT. MLLMs can support multi-modal inputs and generate diverse types of content by leveraging large-scale training on vast amounts of multi-modal data. However, critical challenges persist in developing medical MLLMs, including security and freshness issues of healthcare data, affecting the output quality of MLLMs. To this end, in this paper, we propose a hybrid Retrieval-Augmented Generation (RAG)-empowered medical MLLM framework for healthcare data management. This framework leverages a hierarchical cross-chain architecture to facilitate secure data training. Moreover, it enhances the output quality of MLLMs through hybrid RAG, which employs multi-modal metrics to filter various unimodal RAG results and incorporates these retrieval results as additional inputs to MLLMs. Additionally, we employ age of information to indirectly evaluate the data freshness impact of MLLMs and utilize contract theory to incentivize healthcare data holders to share their fresh data, mitigating information asymmetry during data sharing. Finally, we utilize a generative diffusion model-based deep reinforcement learning algorithm to identify the optimal contract for efficient data sharing. Numerical results demonstrate the effectiveness of the proposed schemes, which achieve secure and efficient healthcare data management. Copyright © 2024, The Authors. All rights reserved.

关键词： Information management

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Semantic Learning and Emulation Based Cross-platform Binary Vulnerability Seeker

arXiv

引用

arXiv 2022年

作者： Gao, Jian Jiang, Yu Liu, Zhe Yang, Xin Wang, Cong Jiao, Xun Yang, Zijiang Sun, Jiaguang The School of Software Tsinghua University Beijing National Research Center for Information Science and Technology Key Laboratory for Information System Security Ministry of Education Beijing100084 China The College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing211106 China The Department of Electrical and Computer Engineering Villanova University VillanovaPA19085 United States The Department of Computer Science Western Michigan University KalamazooMI49008 United States

Clone detection is widely exploited for software vulnerability search. The approaches based on source code analysis cannot be applied to binary clone detection because the same source code can produce significantly different binaries due to different operating systems, microprocessor architectures and compilers. In this paper, we present BinSeeker, a cross-platform binary seeker that integrates semantic learning and emulation. With the help of the labeled semantic flow graph, BinSeeker can quickly identify M candidate functions that are most similar to the vulnerability from the target binary. The value of M is relatively large so this semantic learning procedure essentially eliminates those functions that are very unlikely to have the vulnerability. Then, semantic emulation is conducted on these M candidates to obtain their dynamic signature sequences. By comparing signature sequences, BinSeeker produces top-N functions that exhibit most similar behavior to that of the vulnerability. With fast filtering of semantic learning and accurate comparison of semantic emulation, BinSeeker seeks vulnerability precisely with little overhead. The experiments on six widely used programs with fifteen known CVE vulnerabilities demonstrate that BinSeeker outperforms three state-of-the-art tools Genius, Gemini and CACompare. Regarding search accuracy, BinSeeker achieves an MRR value of 0.65 in the target programs, whereas the MRR values by Genius, Gemini and CACompare are 0.17, 0.07 and 0.42, respectively. If we consider ranking a function with the targeted vulnerability in the top-5 as accurate, BinSeeker achieves the accuracy of 93.33 percent, while the accuracy of the other three tools is merely 33.33, 13.33 and 53.33 percent, respectively. Such accuracy is achieved with 0.27s on average to determine whether the target binary function contains a known vulnerability, and the time for the other three tools are 1.57s, 0.15s and 0.98s, respectively. Compared to the time used to manu

关键词： Semantics

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Arabic Sentiment Analysis Problems and Challenges

Arabic Sentiment Analysis Problems and Challenges

引用

Virtual Campus (JICV), X International Conference on

作者： Youssra Zahidi Yacine EL Younoussi Yassine AL-Amrani Information System and Software Engineering Laboratory Abdelmalek Essaadi University Tetuan Morocco Information Technology and Modeling Systems (TIMS) research tea Abdelmalek Essaadi University Tetuan Morocco

The following topics are dealt with: computer aided instruction; teaching; educational courses; educational institutions; further education; distance learning; Internet; computer science education; physics education; ... 详细信息

ISBN: (数字)9781665415194

ISBN: (纸本)9781665430203

关键词： Sentiment analysis Social networking (online) Education Tools

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Arabic Sentiment Analysis Approaches: An Overview

Arabic Sentiment Analysis Approaches: An Overview

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Virtual Campus (JICV), X International Conference on

ISBN: (数字)9781665415194

ISBN: (纸本)9781665430203

In the last few years, Arabic Sentiment Analysis (ASA) has become the most important trends topic of market and scientific research in Machine Learning (ML) and Natural Language Processing (NLP) fields. It tries to identify the opinion orientation (positivity, neutrality or negativity) of a piece text in different domains and it has gained significant results especially in the field of education, in other words ASA is utilized to ameliorate education by trying to understand the learning degree of students and monitoring the performance of students. Due to Arabic complexities at the level of orthography, morphology and dialects. Sentiment analysis for Arabic language is very difficult and it considered as a more challenging process compared to other languages like English. In this work, we tried to give a comparative analysis of Arabic Sentiment approaches, together with various powerful existing techniques.

关键词： Sentiment analysis Education Morphology Machine learning Market research Complexity theory Monitoring

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