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17th International Conference on Brain Informatics, BI 2024

作者： Doborjeh, Zohreh lavin, Daniel Hunter, Kirsty Heym, Nadja Heasman, Bryony Doborjeh, Maryam Kasabov, Nikola Gibson, Glen Sumich, Alexander School of Engineering Computer and Mathematical Sciences Knowledge Engineering and Discovery Research Institute Auckland University of Technology Auckland New Zealand School of Population Health Faculty of Medical and Health Sciences The University of Auckland Auckland New Zealand Department of Sport Science School of Science and Technology Nottingham Trent University Nottingham United Kingdom NTU Psychology School of Social Sciences Nottingham Trent University Nottingham United Kingdom School of Software Engineering Dalian University Dalian China Institute for Information and Communication Technologies Bulgarian Academy of Sciences Sofia Bulgaria Department of Food and Nutritional Sciences The University of Reading Whiteknights Reading United Kingdom Department of Psychology and Neuroscience Auckland University of Technology Auckland New Zealand

ISBN: (纸本)9789819632930

The immune and enteric systems have been implicated in psychopathology, including depressive disorders. However, the precise neurocognitive mechanisms remain unclear. The present study uses Brain-Inspired Spiking Neural Network (SNN) technique to model electroencephalographic (EEG) data as a function of gut-microbiota, inflammation, and depressive symptoms. Forty-two participants (nonclinical cohort) were assessed for Bacteroides-Prevotella (BAC) colonization in faecal samples, Interleukin 6 (IL-6) in peripheral blood, depressive symptoms (self-report of cognitive and non-cognitive symptoms) and brain function (resting state EEG). SNN models were applied to EEG data to generate connection weight values across scalp regions. Connection weights were visualized as a function of 1) depression;2) inflammation;3) gut microbiome. Lower depression and higher BAC were associated with higher frontal connection weights (cognitive depression = bilateral, non-cognitive and BAC = left hemisphere only). Lower cognitive depression and lower IL-6 were associated with higher posterior connection weights. The SNN models achieved higher accuracy for theta and alpha EEG sub-bands, highlighting their relevance in differentiating between groups. These findings emphasize the potential of SNNs in predicting parameters from brain functional connectivity data and developing targeted diagnostic and intervention strategies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electroencephalography

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A Pairwise Comparison Relation-assisted Multi-objective Evolutionary Neural Architecture Search Method with Multi-population Mechanism

arXiv

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arXiv 2024年

作者： Xue, Yu Zhu, Chenchen Zhou, MengChu Wahib, Mohamed Gabbouj, Moncef School of Software Nanjing University of Information Science and Technology Jiangsu China Department of Electrical and Computer Engineering NewJersey Institute of Technology NewarkNJ07102 United States Japan Department of Computing Sciences Tampere University Tampere Finland

Neural architecture search (NAS) enables researchers to automatically explore vast search spaces and find efficient neural networks. But NAS suffers from a key bottleneck, i.e., numerous architectures need to be evaluated during the search process, which requires a lot of computing resources and time. In order to improve the efficiency of NAS, a series of methods have been proposed to reduce the evaluation time of neural architectures. However, they are not efficient enough and still only focus on the accuracy of architectures. In addition to the classification accuracy, more efficient and smaller network architectures are required in real-world applications. To address the above problems, we propose the SMEM-NAS, a pairwise comparison relation-assisted multi-objective evolutionary algorithm based on a multi-population mechanism. In the SMEM-NAS, a surrogate model is constructed based on pairwise comparison relations to predict the accuracy ranking of architectures, rather than the absolute accuracy. Moreover, two populations cooperate with each other in the search process, i.e., a main population guides the evolution, while a vice population expands the diversity. Our method aims to provide high-performance models that take into account multiple optimization objectives. We conduct a series of experiments on the CIFAR-10, CIFAR- 100 and ImageNet datasets to verify its effectiveness. With only a single GPU searching for 0.17 days, competitive architectures can be found by SMEM-NAS which achieves 78.91% accuracy with the MAdds of 570M on the ImageNet. This work makes a significant advance in the important field of NAS. Copyright © 2024, The Authors. All rights reserved.

关键词： Multiobjective optimization

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The Dataflow Runtime Environment of DFC 1

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21st International Conference on Parallel and Distributed Computing, Applications, and Technologies, PDCAT 2020

作者： Zhang, Jing Li, Jinrong Du, Zheng Shu, Jiwu Luo, Qiuming NHPCC/Guangdong Key Laboratory of Popular HPC College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (数字)9783030692445

ISBN: (纸本)9783030692438

In this paper, we introduce the DFC dataflow language and its runtime environment. DFC runtime library is in charge of constructing the DAG of the dataflow graph, firing the DFC tasks and the synchronizations between the tasks of successive passes. Basing on an elaborately implemented thread pool and queued Active Data, DFC runtime shows an ideal performance comparing with DSPatch. The experiment of a simple dataflow graph shows that DFC has better performance for the cases that the parallelism beneath the core number, while DSPatch shows a better scalability for the cases of the parallelism exceed the core number. As DFC is still a prototype language, it lacks the ability to construct the DAG dynamically, which leads to low efficiency when coding for a well-structured dataflow graph. © 2021, Springer Nature Switzerland AG.

关键词： Data flow analysis

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Intrusion detection system for malicious traffic using evolutionary search algorithm

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Recent Advances in computer Science and Communications 2021年第5期14卷 1381-1389页

作者： Al-Saqqa, Samar Al-Fayoumi, Mustafa Qasaimeh, Malik Department of Information Technology King Abdullah II School of Information Technology The University of Jordan Amman Jordan Department of Computer Science King Hussein School of Computing Sciences Princess Sumaya University for Technology Amman Jordan Department of Software Engineering King Hussein School of Computing Sciences Princess Sumaya University for Technology Amman Jordan

Introduction: Intrusion detection systems play a key role in system security by identifying potential attacks and giving appropriate responses. As new attacks are always emerging, intrusion detection systems must adapt to these attacks, and more work is continuously needed to develop and propose new methods and techniques that can improve efficient and effective adaptive intrusion systems. Feature selection is one of the challenging areas that need more work because of its importance and impact on the performance of intrusion detection systems. This paper applies an evolutionary search algorithm in feature subset selection for intrusion detection systems. Methods: The evolutionary search algorithm for the feature subset selection is applied and two clas-sifiers are used, Naïve Bayes and decision tree J48, to evaluate system performance before and after features selection. NSL-KDD dataset and its subsets are used in all evaluation experiments. Results: The results show that feature selection using the evolutionary search algorithm enhances the intrusion detection system with respect to detection accuracy and detection of unknown attacks. Furthermore, time performance is achieved by reducing training time, which is reflected positively in overall system performance. Discussion: The evolutionary search applied to select IDS algorithm features can be developed by modifying and enhancing mutation and crossover operators and applying new enhanced techniques in the selection process, which can give better results and enhance the performance of intrusion detection for rare and complicated attacks. Conclusion: The evolutionary search algorithm is applied to find the best subset of features for the intrusion detection system. In conclusion, it is a promising approach to be used as a feature selection method for intrusion detection. The results showed better performance for the intrusion detection system in terms of accuracy and detection rate. © 2021 Bentham Science Publisher

关键词： Feature Selection

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Research on Restructuring Path of Children's Digital Publishing Supply and Demand Chain 2

Research on Restructuring Path of Children's Digital Publish...

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2nd International Conference on Information Science and Education, ICISE-IE 2021

作者： Chen, Huan Liu, Ying Shan, Chan Zhang, Hequn School of Journalism and Communication of Minjiang University Fujian Fuzhou350108 China Department of Software Technology Xiamen Institute of Software Technology Fujian Xiamen China Department of Computer System and Technology Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur50603 Malaysia College of Information Engineering Jimei University Xiamen361021 China

ISBN: (纸本)9781665438292

In the era of rapid development of intelligent technology, the children's digital publishing industry is facing a disruptive change, and this change may be realized through the reconstruction of the supply and demand chain. The value power of source content creation and the ability to gather users have been deconstructed. The increase in the amount of content requires platform institutions to have a more professional content operation system. The convenience of payment technology has greatly increased the digital content payment rate of users of children's digital products. A development model of the supply and demand chain of children's digital publishing with more innovative flexibility and enabling effects. © 2021 IEEE

关键词： Economics

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Single Image 3D Reconstruction Based on Attention Mechanism and Graph Convolution Network 22

Single Image 3D Reconstruction Based on Attention Mechanism ...

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8th International Conference on Computing and Artificial Intelligence, ICCAI 2022

作者： Gao, Wei Yu, Liyang Du, Yuanyuan Lu, Songfeng Software Development Center of Industrial and Commercial Bank of China Limited Zhuhai519000 China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430072 China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518057 China

ISBN: (纸本)9781450396110

This paper innovatively proposes a channel attention mechanism and graph convolutional network model adapted to 3D reconstruction, and combines the target detection model to construct a neural network that generates a target 3D model from a single RGB image. The model first generates the target 3D Voxels, and further generates a more refined 3D Meshes model through the graph convolutional network. Compared with the control group algorithm, the Pix3D datasetAPmesh index has been improved by 2.8%, which fully proves the effectiveness of the model in single-image 3D reconstruction. The experimental results show that the algorithm has good usability in the 3D reconstruction of actual mesh points. © 2022 Association for Computing Machinery. All rights reserved.

关键词： Convolution

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SLAM: Towards Efficient Multilingual Reasoning via Selective Language Alignment

arXiv

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arXiv 2025年

作者： Fan, Yuchun Mu, Yongyu Wang, Yilin Huang, Lei Ruan, Junhao Li, Bei Xiao, Tong Huang, Shujian Feng, Xiaocheng Zhu, Jingbo NLP Lab School of Computer Science and Engineering Northeastern University Shenyang China NiuTrans Research Shenyang China Harbin Institute of Technology Harbin China Meituan Inc China National Key Laboratory for Novel Software Technology Nanjing University China

Despite the significant improvements achieved by large language models (LLMs) in English reasoning tasks, these models continue to struggle with multilingual reasoning. Recent studies leverage a full-parameter and two-stage training paradigm to teach models to first understand non-English questions and then reason. However, this method suffers from both substantial computational resource computing and catastrophic forgetting. The fundamental cause is that, with the primary goal of enhancing multilingual comprehension, an excessive number of irrelevant layers and parameters are tuned during the first stage. Given our findings that the representation learning of languages is merely conducted in lower-level layers, we propose an efficient multilingual reasoning alignment approach that precisely identifies and finetunes the layers responsible for handling multilingualism. Experimental results show that our method, SLAM, only tunes 6 layers' feedforward sub-layers including 6.5-8% of all parameters within 7B and 13B LLMs, achieving superior average performance than all strong baselines across 10 languages. Meanwhile, SLAM only involves one training stage, reducing training time by 4.1 - 11.9× compared to the two-stage method. Copyright © 2025, The Authors. All rights reserved.

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A Correlation And Order-Aware Rule Learning Method For Knowledge Graph Reasoning

A Correlation And Order-Aware Rule Learning Method For Knowl...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Yuefeng He Yang He Xu Zheng Bei Hui Bin Tian Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu Naval University of Engineering Wuhan

Mining high-quality logical rules is crucial as they can provide beneficial interpretability for predictions. Recent methods that incorporate logical rules into learning tasks have been proven to yield high-quality logical rules successfully. However, existing methods either rely on the rule instances observed to support rule mining, or simply embed the rule head and the rule body to learn from them. Additionally, they can not fully utilize the rich semantic information contained in logical rules and overlook the intrinsic correlations between all relations within the domain. In this paper, we propose a model called Correlation and order-Aware Rule Learning (CARL) that captures deeper semantic information in rules by allowing relations to be co-aware of each other and paying attention to logical sequence sensitivity. CARL utilizes semantic consistency between the rule body and rule head as its learning objective, continuously introducing more semantic information and logically simplifying the rule body while considering logical sequence sensitivity. We explored the internal correlations between domain relations and used the thought of knowledge distillation to simplify modules so that relations in CARL can share or perceive each other’s information or state efficiently. Experiments on link prediction tasks have demonstrated that CARL can learn higher-quality rules and yield state-of-the-art results on four popular public datasets. https://***/burning5112/CARL

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Algorithm Modeling of Smile Intensity Estimation Using a Spatial Attention Convolutional Neural Network 3

Algorithm Modeling of Smile Intensity Estimation Using a Spa...

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2020 3rd International Conference on Modeling, Simulation and Optimization Technologies and Applications, MSOTA 2020

作者： Luo, Zhenzhen Sha, Zongyu Jin, Chen Xu, Ning School of Software Jiangxi Normal University Jiangxi China School of Computer Science and Technology Wuhan University of Technology Wuhan China School of Information Engineering Wuhan University of Technology Wuhan China

Smile intensity estimation is a challenging task as it required subtle feature extraction, self-Adapted weighted model and classifier. complexity of the problem domains, and problems on fine-grained image recognition are some of the issues related to intensity estimation. In this study, we designed a self-weighted deep convolutional neural network architecture for smiles intensity estimation using graphics processing unit. In the case of using only CK+ smile images, the accuracy of the model is also higher than that of the latest technology. Our model achieved better accuracy by just using CK+ smile images than state-of-The-Art techniques. Visualizations of learned features at various layers and their deconvolutions are also presented for understanding the learning process. © Published under licence by IOP Publishing Ltd.

关键词： Image recognition

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CXR Scan:X-Ray Image Scanning Application for Lung Cancer and Tuberculosis 13

CXR Scan:X-Ray Image Scanning Application for Lung Cancer an...

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13th IEEE Annual Information technology, Electronics and Mobile Communication Conference, IEMCON 2022

作者： Jayasooriya, A.M.U.J. Wickramasekara, T.M.A.M. Jayasinghe, I.C. Gunaratne, U.A. Weerasinghe, Lokesha Dassanayake, Gaya Thamali Department of Data Science Sri Lanka Institute of Information Technology New Kandy Road Malabe Sri Lanka Department of Computer Science and Software Engineering Sri Lanka Insittite of Information Technology New Kandy Road Malabe Sri Lanka Department of Information Technology Sri Lanka Institue of Information Technology Kandy Road Malabe Sri Lanka

ISBN: (数字)9781665463164

ISBN: (纸本)9781665463164

The initial criterion for identifying lung disorders is chest radiographs. The three major lung illnesses that pose the greatest threat to public health are tuberculosis, pneumonia, and lung cancer. Chest X-ray diagnosis of pulmonary illnesses is a challenging undertaking that requires high experience. In rural places, it can be difficult to locate skilled radiologists. Due to the high frequency of TB and lung cancer radiological similarities, many individuals with lung cancer are initially misdiagnosed as having TB and treated incorrectly. According to a recent WHO survey, millions of people die each year as a result of delayed or incorrect diagnoses of lung diseases. This death rate can be reduced, by early detection of certain disorders. This paper proposes a system with 4 main components;Image processing of chest X-rays to identify the disease using Convolutional Neural networks;Predicting the probability of having LC or TB using multivariate data classification techniques;Recommending medicine and related information to support the decision-making process using gaussian naïve bayes, logistic regression model and decision tree classification methods;Visualizing the X-ray image using Augmented Reality. © 2022 IEEE.

关键词： Convolutional neural networks

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