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arXiv 2023年

作者： Chiang, Kuan-Jung Dong, Steven Cheng, Chung-Kuan Jung, Tzyy-Ping Department of Computer Science and Engineering University of California San Diego United States Human Factors Center of Excellence Microsoft Corporation United States Institute for Neural Computation Institute of Engineering in Medicine University of California San Diego United States

Objective: The Electroencephalogram (EEG) is gaining popularity as a physiological measure for neuroergonomics in human factor studies because it is objective, less prone to bias, and capable of assessing the dynamics of cognitive states. This study investigated the associations between memory workload and EEG during participants’ typical office tasks on a single-monitor and dual-monitor arrangement. We expect a higher memory workload for the single-monitor arrangement. Approach: We designed an experiment that mimics the scenario of a subject performing some office work and examined whether the subjects experienced various levels of memory workload in two different office setups: 1) a single-monitor setup and 2) a dual-monitor setup. We used EEG band power, mutual information, and coherence as features to train machine learning models to classify high versus low memory workload states. Main results: The study results showed that these characteristics exhibited significant differences that were consistent across all participants. We also verified the robustness and consistency of these EEG signatures in a different data set collected during a Sternberg task in a prior study. Significance: The study found the EEG correlates of memory workload across individuals, demonstrating the effectiveness of using EEG analysis in conducting real-world neuroergonomic studies. © 2023, CC BY-NC-ND.

关键词： Electroencephalography

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Meta-heuristic computing knacks for target angle estimation in monostatic radar system with coprime arrays

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Ain Shams Engineering Journal 2024年第5期15卷

作者： Akbar, Sadiq Sohail, Muhammad Asif Zahoor Raja, Muhammad Zaman, Fawad Ullah, Rizwan Abdul Rehman Khan, Muhammad Ajavakom, Nopdanai Phanomchoeng, Gridsada Department of Mechanical Engineering Faculty of Engineering Chulalongkorn University Bangkok10330 Thailand Hefei230026 China Future Technology Research Center National Yunlin University of Science and Technology Douliou64002 Taiwan Department of Electrical & Computer Engineering COMSATS University Islamabad Islamabad44000 Pakistan Department of Electrical Engineering Faculty of Engineering Chulalongkorn University Bangkok10330 Thailand Department of Electrical & Computer Engineering COMSATS University Islamabad Attock Campus 43600 Pakistan Human-Robot Collaboration and Systems Integration Research Unit Faculty of Engineering Chulalongkorn University Bangkok10330 Thailand

Angle estimation of signals received by antenna arrays is a key step in adaptive beamforming, used to detect and locate targets in space. It is desirable to reduce costs by employing a smaller number of antennas while maintaining efficiency and degrees of freedom (DOF) of the system at the same time. Co-prime antenna arrays not only circumvent the operational constraints of cost and hardware complexity, but also boost the DOF by utilizing far fewer antennas as compared to traditional uniform linear arrays. In this paper, meta-heuristic computing paradigm for estimating arrival angle of targets with co-prime arrays incorporated in monostatic radar is proposed. The system's autocorrelation and Mean Square Error (MSE) are used for fitness function optimized by global/local search with Genetic Algorithm (GA) and Pattern Search (PS). Monte Carlo simulations are performed to evaluate the performance via estimation accuracy, MSE, and computational cost for varying numbers of impinging waves. © 2024 THE AUTHORS

关键词： Genetic algorithms

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A systematic study on the role of SentiWordNet in opinion mining

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Frontiers of computer science 2021年第4期15卷 165-183页

作者： Mujtaba HUSNAIN Malik Muhammad Saad MISSEN Nadeem AKHTAR Mickael COUSTATY Shahzad MUMTAZ V.B.Surya PRASATH Department of IT Faculty of ComputingThe Islamia University of BahawalpurBahawalpur 63100Pakistan Laboratoire Informatique Image et Interaction(L3i)Facultés des Sciences et TechnologiesUniversity of La RochelleLa Rochelle 17000France Division of Biomedical Informatics Cincinnati Children’s Hospital Medical CenterCincinnati 45229USA Department of Pediatrics University of Cincinnati College of MedicineCincinnati 45229USA Department of Biomedical Informatics College of MedicineUniversity of CincinnatiCincinnati 45267USA Department of Electrical Engineering and Computer Science University of CincinnatiCincinnati 45221USA

Sentiment lexicons(SL)(aka lexical resources)are the repositories of one or several dictionaries that consist of known and precompiled sentiment *** lexicons play an important role in performing several different opinion mining *** efficacy of the lexicon-based approaches in performing opinion mining(OM)tasks solely depends on selecting an appropriate opinion lexicon to analyze the ***,one has to explore the available sentiment lexicons and then select the most suitable *** available resources,SentiWordNet(SWN)is the most widely used lexicon to perform tasks related to opinion *** SWN,each synset of WordNet is being assigned the three sentiment numerical scores;positive,negative and objective that are calculated using by a set of *** this paper,a detailed and comprehensive review of the work related to opinion mining using Senti-WordNet is provided in a very distinctive *** survey will be useful for the researchers contributing to the field of opinion *** features make our contribution worthwhile and unique among the reviews of similar kind:(i)our review classifies the existing literature with respect to opinion mining tasks and subtasks(ii)it covers a very different outlook of the opinion mining field by providing in-depth discussions of the existing works at different granularity levels(word,sentences,document,aspect,clause,and concept levels)(iii)this state-ofart review covers each article in the following dimensions:the designated task performed,granularity level of the task completed,results obtained,and feature dimensions,and(iv)lastly it concludes the summary of the related articles according to the granularity levels,publishing years,related tasks(or subtasks),and types of classifiers *** the end,major challenges and tasks related to lexicon-based approaches towards opinion mining are also discussed.

关键词： opinion mining lexical based resources Senti-WordNet opinion strength

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Structural and functional neural correlates of sensorimotor deficits in progression of hepatic encephalopathy

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Magnetic Resonance Letters 2024年第2期5卷

作者： Sha, Guanchen Liu, Yuefan Cao, Yutong Zhang, Qi Zhang, Yining Chen, Yuanyuan Fan, Qiuyun Cheng, Yue Department of Biomedical Engineering College of Precision Instruments and Optoelectronics Engineering Tianjin University Tianjin300072 China Tianjin Key Laboratory of Brain Science and Neuroengineering Tianjin300072 China Academy of Medical Engineering and Translational Medicine Tianjin University Tianjin300072 China Department of Medical Imaging Tianjin First Central Hospital Tianjin300192 China Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration Tianjin300392 China State Key Laboratory of Advanced Medical Materials and Devices Tianjin300072 China

Hepatic encephalopathy (HE) is a neurological condition that occurs as a complication of liver dysfunction that involves sensorimotor symptoms in addition to cognitive and behavioral changes, particularly in cases of severe liver disease or cirrhosis. Previous studies have reported spatially distributed structural and functional abnormalities related to HE, but the exact relationship between the structural and functional alterations with respect to disease progression remains unclear. In this study, we performed surface-based cortical thickness comparisons and functional connectivity (FC) analyses between three cross-sectional groups: healthy controls (HC, N = 51), patients with minimal hepatic encephalopathy (MHE, N = 50), patients with overt hepatic encephalopathy (OHE, N = 51). In addition to the distributed cortical thinning that is extensively thought to be associated with cognitive decline in HE, we found significant cortical thickening in the left parahippocampal gyrus cortex in the OHE group (p © 2024 The Authors

关键词： Diseases

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Correction to: Automatic COVID-19 detection mechanisms and approaches from medical images: a systematic review

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Multimedia tools and applications 2022年第20期81卷 28799-28800页

作者： Amir Masoud Rahmani Elham Azhir Morteza Naserbakht Mokhtar Mohammadi Adil Hussein Mohammed Aldalwie Mohammed Kamal Majeed Sarkhel H Taher Karim Mehdi Hosseinzadeh Future Technology Research Center National Yunlin University of Science and Technology Douliu Yunlin Taiwan. Research and Development Center Mobile Telecommunication Company of Iran Tehran Iran. Mental Health Research Center Psychosocial Health Research Institute Iran University of Medical Sciences Tehran Iran. Department of Information Technology College of Engineering and Computer Science Lebanese French University Kurdistan Region Iraq. Department of Communication and Computer Engineering Faculty of Engineering Cihan University-Erbil Kurdistan Region Iraq. Information Technology Department Faculty of Applied Science Tishk International University Erbil Iraq. Computer Department College of Science University of Halabja Halabja Iraq. Computer Networks Department Sulaimani Polytechnic University Technical College of Informatics Sulaymaniyah Iraq. Computer Science University of Human Development Sulaymaniyah Iraq.

[This corrects the article DOI: 10.1007/s11042-022-12952-7.].

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Generative adversarial networks applied to gene expression analysis: An interdisciplinary perspective

Computational and Systems Oncology

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Computational and Systems Oncology 2023年第3期3卷

作者： Ai, Xusheng Smith, Melissa C Feltus, Frank Alex Electrical and Computer Engineering Department Clemson University Clemson SC United States Department of Genetics and Biochemistry Biomedical Data Science & Informatics Program Clemson Center for Human Genetics Clemson University Clemson SC United States

The remarkable flexibility and adaptability of generative adversarial networks (GANs) have led to the proliferation of its models in bioinformatics research. Proteomic and transcriptomic profiles have been shown to be promising methods for discovering and identifying disease biomarkers. However, those analyses were performed by trained human examiners making the process tedious, time consuming, and hard to standardize. With the development of GANs, it is now possible to reduce computational costs and human time for bioinformatics analysis to produce effective biomarkers. Moreover, GANs help address the lack of phenotypic state transitional gene expression data as well as avoid protected human data constraints by generating RNA sequencing (RNA-seq) data from random vectors. The purpose of this review is to summarize the use of GAN approaches and techniques to augment RNA-seq expression data and identify clinically useful biomarkers. We compare different studies that use different types of GAN models to examine the biomarkers. Also, we identify research gaps and challenges that apply GANs to bio-informatics. Finally, we propose potential directions for future research. © 2023 The Authors. Computational and Systems Oncology published by Wiley Periodicals LLC.

关键词： biomarkers data augmentation generative adversarial network RNA sequencing

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Study of Uv-Vis Molar Absorptivity Variation and Quantitation of Anthocyanins Using Molar Relative Response Factor

SSRN

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SSRN 2023年

作者： Dong, Wen Yang, Xin Zhang, Ning Chen, Pei Sun, Jianghao Harnly, James M. Zhang, Mengliang Department of Chemistry Middle Tennessee State University MurfreesboroTN37132 United States Department of Computer Science Middle Tennessee State University MurfreesboroTN37132 United States Department of Mathematics and Computer Science Fisk University NashvilleTN37208 United States Methods and Application of Food Composition Laboratory Beltsville Human Nutrition Research Center Agricultural Research Service United States Department of Agriculture BeltsvilleMD20705 United States

The effects of anthocyanin’s substitution groups on the UV-Vis molar absorptivity were examined by analyzing a group of 31 anthocyanidin/anthocyanin reference standards with ultra-high performance liquid chromatography-diode array detector (UHPLC-DAD). The hydroxylation and glycosylation on aglycones caused absorbance maxima shift and molar absorptivity change, which was dependent on the substitution position. Such molar absorptivity variation was often neglected in anthocyanin quantitation, resulting in significant analytical errors. A simple yet comprehensive strategy based on the molar relative response factors (MRRFs) and a single master reference calibration (i.e., cyanidin-3-glucoside) was proposed to compensate for the quantitative errors. MRRFs of 617 anthocyanins/anthocyanidins were calculated and the information is freely available at https://***/anthocyanin/. The strategy was applied to analyzing anthocyanins in red cabbage, blueberry, and strawberry samples. The results indicate this approach provides an effective, inexpensive, and accurate method for quantitative analysis of many anthocyanins in food materials without using individual reference standards. © 2023, The Authors. All rights reserved.

关键词： Anthocyanins

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Exploiting aliasing for manga restoration

arXiv

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arXiv 2021年

作者： Xie, Minshan Xia, Menghan Wong, Tien-Tsin Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology SIAT CAS

As a popular entertainment art form, manga enriches the line drawings details with bitonal screentones. However, manga resources over the Internet usually show screentone artifacts because of inappropriate scanning/rescaling resolution. In this paper, we propose an innovative twostage method to restore quality bitonal manga from degraded ones. Our key observation is that the aliasing induced by downsampling bitonal screentones can be utilized as informative clues to infer the original resolution and screentones. First, we predict the target resolution from the degraded manga via the Scale Estimation Network (SENet) with spatial voting scheme. Then, at the target resolution, we restore the region-wise bitonal screentones via the Manga Restoration Network (MR-Net) discriminatively, depending on the degradation degree. Specifically, the original screentones are directly restored in pattern-identifiable regions, and visually plausible screentones are synthesized in pattern-agnostic regions. Quantitative evaluation on synthetic data and visual assessment on real-world cases illustrate the effectiveness of our method. Copyright © 2021, The Authors. All rights reserved.

关键词： Restoration

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Objective Depression Detection Using EEG and Eye Movement Signals Induced by Oil Paintings

Objective Depression Detection Using EEG and Eye Movement Si...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Luyu Liu Dan Peng Wei-Long Zheng Bao-Liang Lu Department of Computer Science and Engineering Center for Brain-Like Computing and Machine Intelligence the Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering and Brain Science and Technology Research Center Shanghai Jiao Tong University Shanghai People’s Republic of China RuiJin-Mihoyo Laboratory Clinical Neuroscience Center RuiJin Hospital Shanghai Jiao Tong University School of Medicine Shanghai People’s Republic of China

Depression is a mental disorder characterized by persistent sadness and loss of interest, which has become one of the leading causes of disability worldwide. There are currently no objective diagnostic standards for depression in clinical practice. Previous studies have shown that depression causes both brain abnormalities and behavioral disorders. In this study, both electroencephalography (EEG) and eye movement signals were used to objectively detect depression. By presenting 40 carefully selected oil paintings—20 positive and 20 negative—as stimuli, we were able to successfully evoke emotions in 48 depressed patients (DPs) and 40 healthy controls (HCs) from three centers. We then used Transformer, a deep learning model, to conduct emotion recognition and depression detection. The experimental results demonstrate that: a) Transformer achieves the best accuracies of 89.21% and 92.19% in emotion recognition and depression detection, respectively; b) The HC group has higher accuracies than the DP group in emotion recognition for both subject-dependent and subject-independent experiments; c) The neural pattern differences do exist between DPs and HCs, and we find the consistent asymmetry of the neural patterns in DPs; d) For depression detection, using single oil painting achieves the best accuracies, and using negative oil paintings has higher accuracies than using positive oil paintings. These findings suggest that EEG and eye movement signals induced by oil paintings can be used to objectively identify depression.

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Tagging Continuous Labels for EEG-based Emotion Classification

Tagging Continuous Labels for EEG-based Emotion Classificati...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Rong-Fei Gu Li-Ming Zhao Wei-Long Zheng Bao-Liang Lu Department of Computer Science and Engineering Center for Brain-Like Computing and Machine Intelligence the Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering and Brain Science and Technology Research Center Shanghai Jiao Tong University Shanghai People’s Republic of China RuiJin-Mihoyo Laboratory Clinical Neuroscience Center RuiJin Hospital Shanghai Jiao Tong University School of Medicine Shanghai People’s Republic of China

EEG-based emotion classification has long been a critical task in the field of affective brain-computer interface (aBCI). The majority of leading researches construct supervised learning models based on labeled datasets. Several datasets have been released, including different kinds of emotions while utilizing various forms of stimulus materials. However, they adopt discrete labeling methods, in which the EEG data collected during the same stimulus material are given a same label. These methods neglect the fact that emotion changes continuously, and mislabeled data possibly exist. The imprecision of discrete labels may hinder the progress of emotion classification in concerned works. Therefore, we develop an efficient system in this paper to support continuous labeling by giving each sample a unique label, and construct a continuously labeled EEG emotion dataset. Using our dataset with continuous labels, we demonstrate the superiority of continuous labeling in emotion classification through experiments on several classification models. We further utilize the continuous labels to identify the EEG features under induced and non-induced emotions in both our dataset and a public dataset. Our experimental results reveal the learnability and generality of the relation between the EEG features and their continuous labels.

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