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7th International Conference on Image, Vision and Computing, ICIVC 2022

作者： Zhou, Yixuan Zhang, Lei Xu, Qin Liu, Yixiao Fu, Wen Tongji University Department of Traffic Information and Control Engineering Shanghai China Tongji University Shanghai Research Institute for Intelligent Autonomous Systems Shanghai China City University of Macau Faculty of Data Science China

ISBN: (数字)9781665467346

ISBN: (纸本)9781665467346

With the increasing volume of spatiotemporal traffic data, many scholars used it to explore the characteristics of urban facilities. In this paper, a method of classifying the main tertiary hospitals in Shanghai using taxi data of online car-hailing was proposed. Rasterization of the researched area and extraction of OD (Origin-Destination) records were done, and an optimum buffer distance threshold was determined. By comparing the previous studies, we designed the clustering approach (based on K-Means algorithm) and set new inputs based on trips at different distances from a researched hospital. A reasonable result was achieved: 4 types of hospitals are obtained and a discussion was conducted. This research is valuable for identifying the function and orientation of hospitals, and can help government better understand the spatial relationship of hospitals. © 2022 IEEE.

关键词： Hospitals

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An Uncovered Neurons Information-Based Fuzzing Method for DNN

An Uncovered Neurons Information-Based Fuzzing Method for DN...

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International Conference on Computer Supported Cooperative Work in Design

作者： Kecheng Tang Jian-Tao Zhou Xu Guo College of Computer Science Inner Mongolia University Hohhot China National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot China Engineering Research Center of Ecological Big Data Ministry of Education Hohhot China Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Hohhot China Inner Mongolia Key Laboratory of Social Computing and Data Processing Hohhot China Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Hohhot China Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Fuzzing is increasingly being utilized as a method to test the reliability of Deep Learning (DL) systems. In order to detect more errors in DL systems, exploring the internal logic of more DNNs has become the main objective of fuzzing. Despite advancements in the seed selection aspect of fuzzing, considerable opportunities still exist for improving testing efficiency. Current research has issues with the repeated consideration of neurons in the model that will be covered in the future by other seeds, leading to redundant seeds and lower testing efficiency. Additionally, there is a lack of a method to measure the potential of seeds to increase coverage, making it difficult to select the most worthy seeds for mutation in each iteration. We propose an uncovered neurons information based (UNIB) fuzzing method for DNN. UNIB uses clustering methods to organize the seed queue based on initial seed data, aiming to enhance the coverage rate improved in each iteration. It also integrates coverage information from the testing phase to identify the seeds with the greatest potential. The experimental results show that UNIB achieved a higher NC than the second-best method by 1.1% and 3% in LetNet-4 and LetNet-5, respectively. UNIB consistently required the fewest number of iterations to reach the same NC as other methods. For both LetNet-4 and LetNet-5, the adversarial test case sets generated by UNIB exhibited the highest diversity.

关键词： Deep learning Federated learning Current measurement Neurons Neural networks Fuzzing Software

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Additive Manufacturing of Ti6Al4V Using Directed Energy Deposition: An Elaborative Approach of Process Modelling via Computational Fluid Dynamics

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Lasers in Manufacturing and Materials Processing 2025年第1期12卷 201-225页

作者： Ur Rehman, Asif Azher, Kashif Rehman, Aqsa Shah, Mussadiq Mahmood, Muhammad Arif Tüfekci, Celal Sami Salamci, Metin Uymaz Department of Mechanical Engineering Gazi University Ankara Turkey Bartlett School of Sustainable Construction University College London LondonWC1E 6BT United Kingdom Department of Mechanical Engineering King Fahd University of Petroleum & Minerals Dhahran31261 Saudi Arabia Data Science and Analytics Institute The University of Oklahoma NormanOK73019 United States Institute of Chemical Technologies and Analytics TU Wien Getreidemarkt 9 Vienna1060 Austria Intelligent Systems Center Missouri University of Science and Technology RollaMO65409 United States Advanced Manufacturing Technologies Center of Excellence-URTEMM Ankara Turkey Additive Manufacturing Technologies Application and Research Center-EKTAM Ankara Turkey

Additive manufacturing (AM) has revolutionized many industries. Directed energy deposition (DED) offers remarkable utility in size and is an effective method in terms of energy efficiency. The interaction of powder with a laser before coming in contact with the substrate helps to attain fair melting and deposition. However, there is a problem associated with non-uniformity and accumulation of metallic melt on the edges of the manufactured parts which needs to be understood. Herein, the DED process of Ti6Al4V alloy is presented. The phenomenon of recoil pressure and Marangoni actions along the single tracks of the melt pool is defined via the computational fluid dynamics (CFD) analysis. The discrete element modeling (DEM) improves the simulation model output as variable particle diameters are considered, thus mimicking the actual powder size distribution. The simulation results disclosed intricate facts about the flow dynamics of molten material under varying conditions. The stream traces and thermal gradients are evaluated to recognize the effect of laser movement on material cooling and surface tension. The material accumulation occurred at curves and borders due to cooling-induced surface tension alterations, affecting the attribute of the deposited layers. The bi-directional flow patterns are prompted by Marangoni convection and recoil pressure, causing distinct features, including lateral menisci and depression modes. The cooling rates impacted the liquid portion and dilution levels, critical for attaining optimal bonding between deposited layers and substrate. Experimental endorsement using Synchrotron X-ray imaging favored the simulation results, confirming the simulated Marangoni convection effects. This comparison highlights the efficiency of CFD models in predicting molten material behavior, offering a worthy tool for adjusting DED printing parameters. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature

关键词： Titanium alloys

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Twitter Stance Detection via Neural Production Systems

Twitter Stance Detection via Neural Production Systems

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Bowen Zhang Daijun Ding Guangning Xu Jinjin Guo Zhichao Huang Xu Huang College of Big Data and Internet Shenzhen Technology University Shenzhen China Computer Science & Technology Harbin Institute of Technology Shenzhen China JD Intelligent Cities Research Beijing China

Stance detection is an important task, which aims to classify the attitude of an opinionated text toward a given target. In this paper, we develop an interpretable neural production system for stance detection (NPS4SD). NPS4SD is an end-to-end deep learning model, which consists of a set of knowledge rules that are applied by binding with specific entities. NPS4SD consists of two main components: a pretrained model for learning the text representation and a variable binding network (VBN) to bind the knowledge rules with text entities. Extensive experiments are conducted to evaluate the effectiveness of the proposed NPS4SD model on three real-world datasets with in-domain, cross-target and zero-shot setups. Experimental results demonstrate that NPS4SD achieves substantially better performance than the strong competitors for the stance detection task.

关键词： Knowledge engineering Deep learning Production systems Social networking (online) Neural networks Blogs Signal processing

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PTCR-PJF: A Person-Job Fit Model for Structured Resumes

PTCR-PJF: A Person-Job Fit Model for Structured Resumes

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International Joint Conference on Neural Networks (IJCNN)

作者： Jiaming Li Jingfa Yao Guifa Teng Mingyue Yan School of Information Science and Technology Hebei Agricultural University Baoding China Hebei Software Institute Baoding China Intelligent Interconnection Equipment and Multi-Modal Big Data Application Technology Research and Development Center Baoding China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

With the declining efficiency of online recruitment and the increasing trend of structured job seeker information, existing Person-job Fit (PJF) methods are challenged by insufficient matching accuracy and the complexity of data structures. Traditional models often fail to utilize information in structured resumes fully and are inefficient in processing large volumes of heterogeneous data. To address these issues, we propose a novel Person-Job Fit model (PTCR-PJF) based on an improved Text Convolutional Neural Network (TextCNN) and Roformer joint modeling. PTCR-PJF employs two position-aware strategies: sine-cosine absolute position encoding (SPE) and rotary relative position encoding (RoPE), effectively enhancing the recognition of specific positional information in job postings. Additionally, the model employs a dual-layer text convolution module and applies chunk max pooling and adaptive max pooling to extend its perception of positional information further. It also integrates a Roformer based on RoPE to capture long-distance dependencies. Extensive experiments conducted on the large-scale real-world dataset CareerBuilder2012 and comparisons with 12 baseline models show that PTCR-PJF achieves state-of-the-art (SOTA) performance. The results indicate a significant advantage of the PTCR-PJF model in processing structured resume data, offering a new technological path for online intelligent recruitment.

关键词： Adaptation models Resumes Neural networks Predictive models Market research Transformers data models

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Spatial and Contextual Path Network for Image Inpainting

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intelligent Automation & Soft Computing 2024年第2期39卷 115-133页

作者： Dengyong Zhang Yuting Zhao Feng Li Arun Kumar Sangaiah Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and TechnologyChangsha410114China School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China International Graduate Institute of AI National Yunlin University of Science and TechnologyYunlinTaiwan Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon

Image inpainting is a kind of use known area of information technology to repair the loss or damage to the *** feature extraction is the core of image *** enough space for information and a larger receptive field is very important to realize high-precision image ***,in the process of feature extraction,it is difficult to meet the two requirements of obtaining sufficient spatial information and large receptive fields at the same *** order to obtain more spatial information and a larger receptive field at the same time,we put forward a kind of image restoration based on space path and context path *** the space path,we stack three convolution layers for 1/8 of the figure,the figure retained the rich spatial *** the context path,we use the global average pooling layer,where the accept field is the maximum of the backbone network,and the pooling module can provide global context information for the maximum accept *** order to better integrate the features extracted from the spatial and contextual paths,we study the fusion module of the two *** fusionmodule first path output of the space and context path,and then through themass normalization to balance the scale of the characteristics,finally the characteristics of the pool will be connected into a feature vector and calculate the weight *** of images in order to extract context information,we add attention to the context path refinement *** modules respectively from channel dimension and space dimension to weighted images,in order to obtain more effective *** show that our method is better than the existing technology in the quality and quantity of themethod,and further to expand our network to other inpainting networks,in order to achieve consistent performance improvements.

关键词： Image inpainting attention deep learning convolutional network

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Cortical transcriptome analysis after spinal cord injury reveals the regenerative mechanism of central nervous system in CRMP2 knock-in mice

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Neural Regeneration research 2021年第7期16卷 1258-1265页

作者： Ayaka Sugeno Wenhui Piao Miki Yamazaki Kiyofumi Takahashi Koji Arikawa Hiroko Matsunaga Masahito Hosokawa Daisuke Tominaga Yoshio Goshima Haruko Takeyama Toshio Ohshima Laboratory for Molecular Brain Science Department of Life Science and Medical BioscienceGraduate School of Advanced Science and EngineeringWaseda UniversityTokyoJapan Biomolecular Engineering Laboratory Department of Life Science and Medical BioscienceGraduate School of Advanced Science and EngineeringWaseda UniversityTokyoJapan Computational Bio Big-Data Open Innovation Laboratory(CBBD-OIL) National Institute of Advanced Industrial Science and Technology(AIST)TokyoJapan Research Organization for Nano and Life Innovation Waseda UniversityTokyoJapan Institute for Advanced Research of Biosystem Dynamics Waseda Research Institute for Science and EngineeringGraduate School of Advanced Science and EngineeringWaseda UniversityTokyoJapan Cellular and Molecular Biotechnology Research Institute National Institute of Advanced Industrial Science and Technology(AIST)TsukubaJapan Department of Molecular Pharmacology and Neurobiology Graduate School of MedicineYokohama City UniversityYokohamaJapan

Recent studies have shown that mutation at Ser522 causes inhibition of collapsin response mediator protein 2(CRMP2) phosphorylation and induces axon elongation and partial recovery of the lost sensorimotor function after spinal cord injury(SCI).We aimed to reveal the intracellular mechanism in axotomized neurons in the CRMP2 knock-in(CRMP2KI) mouse model by performing transcriptome analysis in mouse sensorimotor cortex using micro-dissection punching *** to that, we analyzed the structural pathophysiology in axotomized or neighboring neurons after SCI and found that somatic atrophy and dendritic spine reduction in sensorimotor cortex were suppressed in CRMP2KI *** analysis of the transcriptome has aided in the identification of four hemoglobin genes Hba-a1, Hba-a2, Hbb-bs, and Hbb-bt that are significantly upregulated in wild-type mice with concomitant upregulation of genes involved in the oxidative phosphorylation and ribosomal pathways after ***, we observed substantial upregulation in channel activity genes and downregulation of genes regulating vesicles, synaptic function, glial cell differentiation in CRMP2KI ***, the transcriptome profile of CRMP2KI mice has been discussed wherein energy metabolism and neuronal pathways were found to be differentially *** results showed that CRMP2KI mice displayed improved SCI pathophysiology not only via microtubule stabilization in neurons, but also possibly via the whole metabolic system in the central nervous system, response changes in glial cells, and *** together, we reveal new insights on SCI pathophysiology and the regenerative mechanism of central nervous system by the inhibition of CRMP2 phosphorylation at *** these experiments were performed in accordance with the guidelines of the Institutional Animal Care and Use Committee at Waseda University, Japan(2017-A027 approved on March 21, 2017;2018-A003 approved on March 25, 2018;2019-A026 approved on March

关键词： CNS regeneration cortex CRMP2 hemoglobin metabolic pathway spinal cord injury spine transcriptome

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RFAConv: Innovating Spatial Attention and Standard Convolutional Operation

arXiv

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arXiv 2023年

作者： Zhang, Xin Liu, Chen Song, Tingting Yang, Degang Ye, Yichen Li, Ke Song, Yingze College of Computer and Information Science Chongqing Normal University China Chongqing Engineering Research Center of Educational Big Data Intelligent Perception and Application China College of Electronic and Information Engineering Southwest University China

Spatial attention has been widely used to improve the performance ofconvolutional neural networks. However, it has certain limitations. In thispaper, we propose a new perspective on the effectiveness of spatial attention,which is that the spatial attention mechanism essentially solves the problem ofconvolutional kernel parameter sharing. However, the information contained inthe attention map generated by spatial attention is not sufficient forlarge-size convolutional kernels. Therefore, we propose a novel attentionmechanism called Receptive-Field Attention (RFA). Existing spatial attention,such as Convolutional Block Attention Module (CBAM) and Coordinated Attention(CA) focus only on spatial features, which does not fully address the problemof convolutional kernel parameter sharing. In contrast, RFA not only focuses onthe receptive-field spatial feature but also provides effective attentionweights for large-size convolutional kernels. The Receptive-Field Attentionconvolutional operation (RFAConv), developed by RFA, represents a new approachto replace the standard convolution operation. It offers nearly negligibleincrement of computational cost and parameters, while significantly improvingnetwork performance. We conducted a series of experiments on ImageNet-1k, COCO,and VOC datasets to demonstrate the superiority of our approach. Of particularimportance, we believe that it is time to shift focus from spatial features toreceptive-field spatial features for current spatial attention mechanisms. Inthis way, we can further improve network performance and achieve even betterresults. The code and pre-trained models for the relevant tasks can be found athttps://***/Liuchen1997/RFAConv. © 2023, CC0.

关键词： Convolution

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Physics-Inspired and data-Driven Two-Stage Deep Learning Approach for Wind Field Reconstruction with Experimental Validation

SSRN

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SSRN 2024年

作者： Liu, Yi Wang, Ranpeng Gu, Yin Li, Congjian Wang, Gangqiao Institute of Public Safety Research Department of Engineering Physics Tsinghua University Beijng100084 China School of Safety Science Tsinghua University Beijing100084 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Institute of Big Data intelligent Management and Decision College of Management Shenzhen University Shenzhen518055 China

Precise and reliable short-term wind forecasts play a pivotal role in smart grid, minimizing transmission and integration risks. While relatively accurate wind fields are available based on numerical methods, their heavy computational cost and discontinuity make it necessary to explore an interactive and end-to-end method. In this study, we develop a physics-inspired and data-driven two-stage deep learning approach that can reconstruct complex wind fields precisely. The proposed method integrates a physical feature extraction model of the flow field with a sparse measurement data-driven error correction approach. In particular, a well-designed and well-trained flow field feature extraction model (original model) can preserve salient features of CFD modelling, while data-driven error correction techniques may harvest the uncertainty features and fill the remaining gaps between the original model predictions and the measured data. Experimental validation illustrates that the proposed algorithm successfully accomplishes wind field reconstruction in complex terrains using sparse datasets. We show that the proposed two-stage strategy exhibits significantly improved prediction results over the purely original method, with an average accuracy improvement of 47.17% and a maximum accuracy improvement of 72.59%. Overall, the proposed method delivers the potential in accurate wind field construction and wind energy forecasting. © 2024, The Authors. All rights reserved.

关键词： Error correction

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A Residual Multi-Scale Convolutional Neural Network with Transformers for Speech Emotion Recognition

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IEEE Transactions on Affective Computing 2024年第2期16卷 915-932页

作者： Yan, Tianhao Meng, Hao Parada-Cabaleiro, Emilia Tao, Jianhua Li, Taihao Schuller, Bjorn W. Zhejiang Lab Research Center for Data Hub and Security Hangzhou311121 China Harbin Engineering University College of Intelligent Systems Science and Engineering Harbin150001 China Johannes Kepler University Linz Institute of Computational Perception Linz4040 Austria Tsinghua University Department of Automation Beijing100084 China UCAS Hangzhou Institute for Advanced Study Hangzhou310024 China University of Augsburg Chair of Embedded Intelligence for Health Care & Wellbeing Augsburg86159 Germany Imperial College London Group on Language Audio & Music LondonSW7 2AZ United Kingdom

The great variety of human emotional expression as well as the differences in the ways they perceive and annotate them make Speech Emotion Recognition (SER) an ambiguous and challenging task. With the development of deep learning, long-term progress has been made in SER systems. However, the existing convolutional neural networks present certain limitations, such as their inability to well capture features, which contain important emotional information. Moreover, the position encoding in the Transformer structure is relatively fixed and only encodes the time domain dimension, which cannot effectively obtain the position information of discriminative features in the frequency domain dimension. In order to overtake these limitations, we propose an end-to-end Residual Multi-Scale Convolutional Neural Networks (RMSCNN) with Transformer model network. Simultaneously, to further validate the effectiveness of RMSCNN in extracting multi-scale features and delivering pertinent emotion localization data, we developed the RMSC_down network in conjunction with the Wav2Vec 2.0 model. The results of the prediction of Arousal, Valence and Dominance on the popular corpora demonstrate the superiority and robustness of our approach for SER , showing an improvement of the recognition accuracy in the public dataset MSP-Podcast 1.9 version. The code is available at this GitHub. © 2010-2012 IEEE.

关键词： Convolutional neural networks

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