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arXiv 2022年

作者： Heidari, Moein Kazerouni, Amirhossein Soltany, Milad Azad, Reza Aghdam, Ehsan Khodapanah Cohen-Adad, Julien Merhof, Dorit School of Electrical Engineering Iran University of Science and Technology Tehran Iran Institute of Imaging and Computer Vision RWTH Aachen University Aachen Germany Department of Electrical Engineering Shahid Beheshti University Tehran Iran Functional Neuroimaging Unit CRIUGM University of Montreal Canada NeuroPoly Lab Institute of Biomedical Engineering Polytechnique Montreal Canada MILA Quebec AI Institute Montreal Canada Faculty of Informatics and Data Science University of Regensburg Regensburg Germany Fraunhofer Institute for Digital Medicine MEVIS Bremen Germany

Convolutional neural networks (CNNs) have been the consensus for medical image segmentation tasks. However, they suffer from the limitation in modeling long-range dependencies and spatial correlations due to the nature of convolution operation. Although transformers were first developed to address this issue, they fail to capture low-level features. In contrast, it is demonstrated that both local and global features are crucial for dense prediction, such as segmenting in challenging contexts. In this paper, we propose HiFormer, a novel method that efficiently bridges a CNN and a transformer for medical image segmentation. Specifically, we design two multi-scale feature representations using the seminal Swin Transformer module and a CNN-based encoder. To secure a fine fusion of global and local features obtained from the two aforementioned representations, we propose a Double-Level Fusion (DLF) module in the skip connection of the encoder-decoder structure. Extensive experiments on various medical image segmentation datasets demonstrate the effectiveness of HiFormer over other CNN-based, transformer-based, and hybrid methods in terms of computational complexity, quantitative and qualitative results. Our code is publicly available at GitHub. © 2022, CC BY.

关键词： Convolution

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Exploring Bio-Behavioral Signal Trajectories of State Anxiety During Public Speaking

Exploring Bio-Behavioral Signal Trajectories of State Anxiet...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Ehsanul Haque Nirjhar Amir Behzadan Theodora Chaspari Department of Computer Science & Engineering HUman Bio-Behavioral Signals (HUBBS) Lab Construction Informatics and Built Environment Research (CIBER) Lab Construction Science Texas A&M University College Station Texas USA

ISBN: (数字)9781509066315

ISBN: (纸本)9781509066322

Public speaking anxiety (PSA) is among the top social phobias in the world. Quantifying PSA in a reliable and unobtrusive manner can lay the foundation toward personalized and inexpensive technology-based interventions. Existing work for quantifying PSA often relies on self-reported measures and statistical aggregates of bio-behavioral indices, such as physiology and speech. Such aggregated bio-behavioral indices are not able to capture time-based trajectories of PSA variation, that can be very useful for better understanding and reliably predicting moments of anxiety. We tackle this problem by introducing temporal parametric models to quantify bio-behavioral trajectories of PSA throughout a public speaking encounter. Using data from 55 participants in a real-life public speaking task, the parameters of the proposed models are found to be significantly correlated with individuals' trait characteristics of general and communication-based anxiety, outperforming aggregate mean bio-behavioral measures.

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Pore Detection from Human Skin Image using U-Net 9

Pore Detection from Human Skin Image using U-Net

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9th International Congress on Advanced Applied informatics, IIAI-AAI 2020

作者： Kachi, Keisuke Iwahori, Yuji Usami, Hiroyasu Fukui, Shinji Wang, Aili Bhuyan, M.K. Chubu University Dept. of Computer Science Kasugai487-8501 Japan Aichi University of Education Informatics and Communication Tech. Kariya448-8542 Japan Harbin University of Science and Technology Higher Education Key Lab. Harbin150006 China Indian Institute of Technology Guwahati Dept. of Electronics and Electrical Eng. Guwahati781039 India

ISBN: (纸本)9781728173979

There are medical hair removal and esthetic's hair removal in the current hair removal approach. Hair loss damages the tissues from which hair grows (hair cells, dermal papilla). Esthetic's hair removal has more constraints than the medical hair removal without a resident medical doctor and the effectiveness of the hair removal is lower. Therefore, pore detection is important to monitor the effect of hair removal. This paper proposes pore detection approach using U-Net [1] which is used in medical image segmentation and shows that proposed method detects pores from human skin images with high accuracy. © 2020 IEEE.

关键词： Image segmentation

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Kurdish Handwritten Character Recognition using Deep Learning Techniques

arXiv

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arXiv 2022年

作者： Ahmed, Rebin M. Rashid, Tarik A. Fattah, Polla Alsadoon, Abeer Bacanin, Nebojsa Mirjalili, Seyedali Vimal, S. Chhabra, Amit IT Department Faculty of Applied Science Tishk International University Erbil Iraq Computer Science and Engineering Department University of Kurdistan Hewler Erbil Iraq Software and Informatics Engineering Salahaddin University-Erbil- Erbil Iraq School of Computing and Mathematics Charles Sturt University Sydney Australia Sydney Australia Singidunum University Belgrade Serbia Centre for Artificial Intelligence Research and Optimisation Torrens University Australia Yonsei Frontier Lab Yonsei University Seoul Korea Republic of Department of Artificial Intelligence and Data Science Ramco Institute of Technology North Venganallur Village Virudhunagar District Tamilnadu Rajapalayam626 117 India Department of Computer Engineering and Technology Guru Nanak Dev University Amritsar India

Handwriting recognition is one of the active and challenging areas of research in the field of image processing and pattern recognition. It has many applications that include: a reading aid for visual impairment, automated reading and processing for bank checks, making any handwritten document searchable, and converting them into structural text form, etc. Moreover, high accuracy rates have been recorded by handwriting recognition systems for English, Chinese Arabic, Persian, and many other languages. Yet there is no such system available for offline Kurdish handwriting recognition. In this paper, an attempt is made to design and develop a model that can recognize handwritten characters for Kurdish alphabets using deep learning techniques. Kurdish (Sorani) contains 34 characters and mainly employs an Arabic\Persian based script with modified alphabets. In this work, a Deep Convolutional Neural Network model is employed that has shown exemplary performance in handwriting recognition systems. Then, a comprehensive dataset was created for handwritten Kurdish characters, which contains more than 40 thousand images. The created dataset has been used for training the Deep Convolutional Neural Network model for classification and recognition tasks. In the proposed system, the experimental results show an acceptable recognition level. The testing results reported a 96% accuracy rate, and training accuracy reported a 97% accuracy rate. From the experimental results, it is clear that the proposed deep learning model is performing well and is comparable to the similar model of other languages' handwriting recognition systems. © 2022, CC BY.

关键词： Convolutional neural networks

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DataLoc+: A data augmentation technique for machine learning in room-level indoor localization

arXiv

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arXiv 2021年

作者： Hilal, Amr Arai, Ismail El-Tawab, Samy Informatics Lab Virginia Tech BlacksburgVA United States Information Initiative Center Nara Institute of Science and Technology Ikoma Nara Japan College of Integrated Science and Engineering James Madison University HarrisonburgVA United States The Department of Computer and Systems Engineering Alexandria University Egypt James Madison University VA United States

Indoor localization has been a hot area of research over the past two decades. Since its advent, it has been steadily utilizing the emerging technologies to improve accuracy, and machine learning has been at the heart of that. Machine learning has been increasingly used in fingerprint-based indoor localization to replace or emulate the radio map that is used to predict locations given a location signature. The prediction quality of a machine learning model primarily depends on how well the model was trained, which relies on the amount and quality of data used to train it. Data augmentation has been used to improve quality of the trained models by synthetically producing more training data, and several approaches were used in the literature that tackles the problem of lack of training data from different angles. In this paper, we propose DataLoc+, a data augmentation technique for room-level indoor localization that combines different approaches in a simple algorithm. We evaluate the technique by comparing it to the typical direct snapshot approach using data collected from a field experiment conducted in a hospital. Our evaluation shows that the model trained using the proposed technique achieves higher accuracy. We also show that the technique adapts to larger problems using a limited dataset while maintaining high accuracy. Copyright © 2021, The Authors. All rights reserved.

关键词： Indoor positioning systems

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Performance evaluation results of evolutionary clustering algorithm star for clustering heterogeneous datasets

arXiv

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arXiv 2021年

作者： Hassan, Bryar A. Rashid, Tarik A. Mirjalili, Seyedali Kurdistan Institution for Strategic Studies and Scientific Research Sulaimani Iraq Computer Science and Engineering Department University of Kurdistan Hewler Erbil Iraq Department of Computer Networks Technical College of Informatics Sulaimani Polytechnic University Sulaimani Iraq Centre for Artificial Intelligence Research and Optimisation Torrens University Australia Yonsei Frontier Lab Yonsei University Seoul Korea Republic of

This article presents the data used to evaluate the performance of evolutionary clustering algorithm star (ECA∗) compared to five traditional and modern clustering algorithms. Two experimental methods are employed to examine the performance of ECA∗ against genetic algorithm for clustering++ (GENCLUST++), learning vector quantisation (LVQ), expectation maximisation (EM), K-means++ (KM++) and K-means (KM). These algorithms are applied to 32 heterogenous and multi-featured datasets to determine which one performs well on the three tests. For one, ther paper examines the efficiency of ECA∗ in contradiction of its corresponding algorithms using clustering evaluation measures. These validation criteria are objective function and cluster quality measures. For another, it suggests a performance rating framework to measurethe the performance sensitivity of these algorithms on varos dataset features (cluster dimensionality, number of clusters, cluster overlap, cluster shape and cluster structure). The contributions of these experiments are two-folds: (i) ECA∗ exceeds its counterpart aloriths in ability to find out the right cluster number;(ii) ECA∗ is less sensitive towards dataset features compared to its competitive techniques. Nonetheless, the results of the experiments performed demonstrate some limitations in the ECA∗: (i) ECA∗ is not fully applied based on the premise that no prior knowledge exists;(ii) Adapting and utilising ECA∗ on several real applications has not been achieved yet. © 2021, CC BY-NC-ND.

关键词： Stars

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Formal context reduction in deriving concept hierarchies from corpora using adaptive evolutionary clustering algorithm

arXiv

引用

arXiv 2021年

作者： Hassan, Bryar A. Rashid, Tarik A. Mirjalili, Seyedali Department of Computer Networks Technical College of Informatics Sulaimani Polytechnic University Sulaimani Iraq Kurdistan Institution for Strategic Studies and Scientific Research Sulaimani Iraq Computer Science and Engineering Department University of Kurdistan Hewler Erbil Iraq Centre for Artificial Intelligence Research and Optimisation Torrens University Australia Yonsei Frontier Lab Yonsei University Seoul Korea Republic of

It is beneficial to automate the process of deriving concept hierarchies from corpora since a manual construction of concept hierarchies is typically a time consuming and resource-intensive process. As such, the overall process of learning concept hierarchies from corpora encompasses a set of steps: parsing the text into sentences, splitting the sentences and then tokenised it. After the lemmatisation step, the pairs are extracted using formal context analysis (FCA). However, there might be some uninteresting and erroneous pairs in the formal context. Generating formal context may lead to a time-consuming process, so formal context size reduction is require to remove uninterested and erroneous pairs, taking less time to extract the concept lattice and concept hierarchies accordingly. In this premise, this study aims to propose two frameworks: i) A framework to review the current process of deriving concept hierarchies from corpus utilising formal concept analysis (FCA);ii) A framework to decrease the formal context's ambiguity of the first framework using an adaptive version of evolutionary clustering algorithm (ECA∗). Experiments are conducted by applying 385 samples corpora from Wikipedia on the two frameworks to examine the reducing size of formal context, which leads to yield concept lattice and concept hierarchy. The resulting lattice of formal context is evaluated to the standad one using concept lattice-invariants. Accordingly, the homomorphic between the two lattices preserves the quality of resulting concept hierarchies by 89% in contrast to the basic ones, and the reduced concept lattice inherits the structural relation of the standard one. The adaptive ECA∗ is examined against its four counterpart baseline algorithms (Fuzzy K-means, JBOS approach, AddIntent algorithm, and FastAddExtent) to measure the execution time on random datasets with different densities (fill ratios). The results show that adaptive ECA∗ performs concept lattice faster than other men

关键词： Information analysis

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Potential Indicator for Continuous Emotion Arousal by Dynamic Neural Synchrony 4th

Potential Indicator for Continuous Emotion Arousal by Dyna...

引用

4th International Workshop on Human Brain and Artificial Intelligence, HBAI 2024

作者： Pan, Guandong Wu, Zhaobang Yang, Yaqian Wang, Xin Liu, Longzhao Zheng, Zhiming Tang, Shaoting School of Computer Science and Engineering Beihang University Beijing100191 China Institute of Artificial Intelligence Beihang University Beijing100191 China Key Laboratory of Mathematics Informatics and Behavioral Semantics Beihang University Beijing100191 China Institute of Medical Artificial Intelligence Binzhou Medical University Yantai264003 China Zhongguancun Laboratory Beijing100094 China Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing Beihang University Beijing100191 China PengCheng Laboratory Shenzhen518055 China State Key Lab of Software Development Environment Beihang University Beijing100191 China

ISBN: (纸本)9789819640003

The need for automatic and high-quality emotion annotation is paramount in applications such as continuous emotion recognition and video highlight detection, yet achieving this through manual human annotations is challenging. Inspired by inter-subject correlation (ISC) utilized in neuroscience, this study introduces a novel Electroencephalography (EEG) based ISC methodology that leverages a single-electrode and feature-based dynamic approach. Our contributions are three folds: Firstly, we reidentify two potent emotion features suitable for classifying emotions-first-order difference (FD) an differential entropy (DE). Secondly, through the use of overall correlation analysis, we demonstrate the heterogeneous synchronized performance of electrodes. This performance aligns with neural emotion patterns established in prior studies, thus validating the effectiveness of our approach. Thirdly, by employing a sliding window correlation technique, we showcase the significant consistency of dynamic ISCs across various features or key electrodes in each analyzed film clip. Our findings indicate the method’s reliability in capturing consistent, dynamic shared neural synchrony among individuals, triggered by evocative film stimuli. This underscores the potential of our approach to serve as an indicator of continuous human emotion arousal. The implications of this research are significant for advancements in affective computing and the broader neuroscience field, suggesting a streamlined and effective tool for emotion analysis in real-world applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electroencephalography (EEG) Emotion Annotation Inter-subject Correlation

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Converged reconfigurable intelligent surface and mobile edge computing for space information networks

arXiv

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arXiv 2021年

作者： Cao, Xuelin Yang, Bo Huang, Chongwen Yuen, Chau Zhang, Yan Niyato, Dusit Han, Zhu Engineering Product Development Pillar Singapore University of Technology and Design Singapore Singapore College of Information Science and Electronic Engineering Zhejiang Provincial Key Lab of information processing communication and networking International Joint Innovation Center Zhejiang University China Department of Informatics University of Oslo Norway School of Computer Science and Engineering Nanyang Technological University Singapore Singapore Department of Electrical and Computer Engineering University of Houston United States

Space information networks (SIN) are facing an ever-increasing thirst for high-speed and high-capacity seamless data transmission due to the integration of ground, air, and space communications. However, this imposes a new paradigm on the architecture design of the integrated SIN. Recently, reconfigurable intelligent surfaces (RISs) and mobile edge computing (MEC) are the most promising techniques, conceived to improve communication and computation capability by reconfiguring the wireless propagation environment and offloading. Hence, converging RISs and MEC in SIN is becoming an effort to reap the double benefits of computation and communication. In this article, we propose an RIS-assisted collaborative MEC architecture for SIN and discuss its implementation. Then we present its potential benefits, major challenges, and feasible applications. Subsequently, we study different cases to evaluate the system data rate and latency. Finally, we conclude with a list of open issues in this research area. Copyright © 2021, The Authors. All rights reserved.

关键词： Mobile edge computing

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TARGET SPECIFIC DE NOVO DESIGN OF DRUG CANDIDATE MOLECULES WITH GRAPH TRANSFORMER-BASED GENERATIVE ADVERSARIAL NETWORKS

arXiv

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arXiv 2023年

作者： Ünlü, Atabey Çevrim, Elif Sarıgün, Ahmet Yiğit, Melih Gökay Çelikbilek, Hayriye Bayram, Osman Güvenilir, Heval Ataş Koyaş, Altay Kahraman, Deniz Cansen Olğaç, Abdurrahman Rifaioğlu, Ahmet Banoğlu, Erden Doğan, Tunca Biological Data Science Lab Dept. of Computer Engineering Hacettepe University Turkey Dept. of Bioinformatics Graduate School of Health Sciences Hacettepe University Turkey Dept. of Chemistry Middle East Technical University Turkey Dept. of Physics Middle East Technical University Turkey Dept. of Computer Engineering Middle East Technical University Turkey Dept. Of Artificial Intelligence Engineering Bahcesehir University Turkey Cancer Systems Biology Lab Graduate School of Informatics Middle East Technical University Turkey Dept. of Pharmaceutical Chemistry Faculty of Pharmacy Gazi University Turkey Laboratory of Molecular Modeling Evias Pharmaceutical R&D Ltd Turkey Dept. of Electrical and Electronics Engineering Iskenderun Technical University Turkey Institute for Computational Biomedicine Heidelberg University Germany

Discovering novel drug candidate molecules is one of the most fundamental and critical steps in drug development. Generative deep learning models, which create synthetic data given a probability distribution, offer a high potential for designing de novo molecules. However, for them to be useful in real-life drug development pipelines, these models should be able to design drug-like and target-centric molecules. In this study, we propose an end-to-end generative system, DrugGEN, for the de novo design of drug candidate molecules that interact with intended target proteins. The proposed method represents molecules as graphs and processes them via a generative adversarial network comprising graph transformer layers. The system is trained using a large dataset of drug-like compounds and target-specific bioactive molecules to design effective inhibitory molecules against the AKT1 protein, which is critically important in developing treatments for various types of cancer. We conducted molecular docking and dynamics to assess the target-centric generation performance of the model, as well as attention score visualisation to examine model interpretability. Results indicate that our de novo molecules have a high potential for interacting with the AKT1 protein at the level of its native ligands. Using the open-access DrugGEN codebase, it is possible to easily train models for other druggable proteins, given a dataset of experimentally known bioactive molecules. © 2023, CC BY-NC-SA.

关键词： Generative adversarial networks

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