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Information Technology (OCIT), OITS International Conference on

作者： Smriti Rout Rishikant Mallick Santosh Kumar Sahu Department of Computer Science Siksha’O’Anusandhan Bhubaneswar India Department of Computer Science Kalinga Institute of Industrial Technology Bhubaneswar India Geodata Processing and Interpretation Center Oil and Natural Gas Corporation Dehradun India

Feature selection (FS) is one of the important tasks of data preprocessing in data analytics. The data with a large number of features will affect the computational complexity, and increase a huge amount of resource usage and time consumption for data analytics. In the era of burgeoning data, the need to streamline datasets for analysis while maintaining predictive accuracy has become paramount. This study presents an investigation into the efficacy of feature selection techniques in reducing the dimensionality of datasets while preserving predictive accuracy. Our primary objective is to curate a subset of relevant attributes from an original dataset and ascertain if the predictive performance remains comparable to the full dataset. Through systematic experimentation and analysis, we aim to illustrate the advantages of this feature selection approach for enhancing the efficiency of datasets. In this study, we will explore various feature selection methods and assess their impact on both computational efficiency and model accuracy. By examining the reduced dataset’s ability to produce results akin to those of the complete dataset, we aim to demonstrate the potential benefits of feature selection in optimizing data analysis processes. Our findings are expected to shed light on the practical utility of feature selection techniques and provide insights into their relevance for future data-driven endeavors. This research represents a crucial step towards harnessing the power of reduced, yet effective, datasets in data science and machine learning applications.

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Automating Theory of Mind Assessment with a LLaMA-3-Powered Chatbot: Enhancing Faux Pas Detection in Autism 14

Automating Theory of Mind Assessment with a LLaMA-3-Powered ...

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14th International Conference on computer and Knowledge Engineering, ICCKE 2024

作者： Fallah, Avisa Nazari, Mohammad Ali Keramati, Ali Mirfazeli, Fatemeh Sadat School of Mathematics Statistics and Computer Science University of Tehran Tehran Iran National Brain Centre Iran University of Medical Sciences Tehran Iran Department of Neuroscience Advanced Technologies in Medicine Iran University of Medical Sciences Image Brain Institute Tehran Iran School of Electrical and Computer Engineering University of Tehran Tehran Iran Mental Health Research Center Department of Psychiatry School of Medicine Psychosocial Health Research Institute Advanced Technologies in Medicine National Brain Centre Iran University of Medical Sciences Tehran Iran

ISBN: (纸本)9798331511272

The Theory of Mind (ToM) is critical for social interactions, enabling individuals to understand and predict others’ mental states. Deficits in ToM, particularly in detecting faux pas, are common among individuals with Autism Spectrum Disorder (ASD). Traditional methods for assessing ToM, such as the Faux Pas Recognition Test (FPRT), require significant human involvement, which can limit accessibility and scalability. This study aims to explore how large language model (LLM)powered chatbots, specifically utilizing the LLaMA-3 model, can enhance the assessment of ToM by automating and improving the administration of the FPRT. The methodology involved creating a dual-phase process where the chatbot first assesses user responses and then provides rehabilitative guidance to correct misunderstandings. Additionally, the system employs a scoring mechanism combining automated evaluation with human oversight to ensure accuracy and consistency, with system prompts crafted based on chain-of-thought prompting to enhance the depth and reliability of the assessments. Our findings demonstrate the effectiveness of LLM-powered chatbots in delivering dynamic and supportive ToM assessments, suggesting its potential for broader application for both artificial intelligence and psychological practices. ©2024 IEEE.

关键词： Chain-of-thought Faux Pas Recognition Test Interactive Chatbot Language Models Theory of Mind

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Modeling Inter-Intra Heterogeneity for Graph Federated Learning

arXiv

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arXiv 2024年

作者： Yu, Wentao Chen, Shuo Tong, Yongxin Gu, Tianlong Gong, Chen School of Computer Science and Engineering Nanjing University of Science and Technology China Center for Advanced Intelligence Project RIKEN Japan State Key Laboratory of Complex & Critical Software Environment Beihang University China Jinan University China Department of Automation Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

Heterogeneity is a fundamental and challenging issue in federated learning, especially for the graph data due to the complex relationships among the graph nodes. To deal with the heterogeneity, lots of existing methods perform the weighted federation based on their calculated similarities between pairwise clients (i.e., subgraphs). However, their inter-subgraph similarities estimated with the outputs of local models are less reliable, because the final outputs of local models may not comprehensively represent the real distribution of subgraph data. In addition, they ignore the critical intra-heterogeneity which usually exists within each subgraph itself. To address these issues, we propose a novel Federated learning method by integrally modeling the Inter-Intra Heterogeneity (FedIIH). For the inter-subgraph relationship, we propose a novel hierarchical variational model to infer the whole distribution of subgraph data in a multi-level form, so that we can accurately characterize the inter-subgraph similarities with the global perspective. For the intra-heterogeneity, we disentangle the subgraph into multiple latent factors and partition the model parameters into multiple parts, where each part corresponds to a single latent factor. Our FedIIH not only properly computes the distribution similarities between subgraphs, but also learns disentangled representations that are robust to irrelevant factors within subgraphs, so that it successfully considers the inter- and intra- heterogeneity simultaneously. Extensive experiments on six homophilic and five heterophilic graph datasets in both non-overlapping and overlapping settings demonstrate the effectiveness of our method when compared with nine state-of-the-art methods. Specifically, FedIIH averagely outperforms the second-best method by a large margin of 5.79% on all heterophilic datasets. Code is available at https://***/blgpb/FedIIH. Copyright © 2024, The Authors. All rights reserved.

关键词： Federated learning

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IEEE VR 2023 Workshops: Workshop: 3D Reconstruction, Digital Twinning, and Simulation for Virtual Experiences (ReDigiTS 2023)

IEEE VR 2023 Workshops: Workshop: 3D Reconstruction, Digital...

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2023 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2023

作者： Cannavò, Alberto Kapralos, Bill Seinfeld, Sofia Pratticò, Filippo Gabriele Zhang, Congyi Politecnico di Torino Turin Italy Game Development and Interactive Media Program and MaxSIMhealth Lab Ontario Tech University Oshawa Canada Image Processing and Multimedia Technology Center Universitat Politècnica de Catalunya-Barcelona Tech Terrassa Spain Politecnico di Torino Turin Italy Department of Computer Science The University of Hong Kong Hong Kong Hong Kong

ISBN: (纸本)9798350348392

Today, Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR) technologies are becoming fundamental across a wide variety of application domains. All these technologies fall under the Extended Reality (XR) spectrum. Areas in which XR technologies and, in particular, immersive experiences have been successfully introduced are expanding daily, from traditional areas, such as entertainment and video game production, to novel scenarios, such as industry, healthcare, smart cities, and autonomous vehicles, to mention a few. The spread of XR and VR is also driven by technological advancements that are making new ways of operating within virtual experiences easier, and sometimes even possible. In particular, the availability of new hardware and software tools make the use of '3D reconstructions', 'digital twins' and 'simulations' commonplace in many XR and VR applications. For example, the possibility to leverage networks with higher bandwidth than never before and lower latency makes it possible to transmit the amount of data needed for massive, real-time digital twins, whereas high-performance hardware enables the reconstruction of high-fidelity digital objects and environments, while lowering the time needed for high-demanding simulations. The aim of the 3D Reconstruction, Digital Twinning, and Simulation for Virtual Experiences (ReDigiTS) workshop is to bring together researchers, practitioners, educators, and students to share ideas and promote further work in this growing area of 3D reconstruction, digital twinning, and simulation in VR and related technologies. © 2023 IEEE.

关键词： Augmented reality

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Causal-IQA: Towards the Generalization of image Quality Assessment Based on Causal Inference 41

Causal-IQA: Towards the Generalization of Image Quality Asse...

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41st International Conference on Machine Learning, ICML 2024

作者： Zhong, Yan Wu, Xingyu Zhang, Li Yang, Chenxi Jiang, Tingting School of Mathematical Sciences Peking University Beijing China National Engineering Research Center of Visual Technology National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University Beijing China Department of Computing The Hong Kong Polytechnic University Hong Kong Hefei Institute of Physical Science Chinese Academy of Sciences University of Science and Technology of China Hefei China National Biomedical Imaging Center Peking University Beijing China

Due to the high cost of image Quality Assessment (IQA) datasets, achieving robust generalization remains challenging for prevalent deep learning-based IQA *** address this, this paper proposes a novel end-to-end blind IQA method: ***, we first analyze the causal mechanisms in IQA tasks and construct a causal graph to understand the interplay and confounding effects between distortion types, image contents, and subjective human ***, through shifting the focus from correlations to causality, Causal-IQA aims to improve the estimation accuracy of image quality scores by mitigating the confounding effects using a causality-based optimization *** optimization strategy is implemented on the sample subsets constructed by a Counterfactual Division process based on the Backdoor *** experiments illustrate the superiority of Causal-IQA. Copyright 2024 by the author(s)

关键词： image correlation

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Tail Classes Matter: Long-Tailed Object Detection Revisited

Tail Classes Matter: Long-Tailed Object Detection Revisited

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

作者： Yinglu Zhang Chenbo Zhang Lu Zhang Tianying Liu Jihong Guan Xinkai Liang Jiajia Zhao Shuigeng Zhou Shanghai Key Lab of Intelligent Information Processing and School of Computer Science Fudan University China Department of Computer Science & Technology Tongji University China Science and Technology on Complex System Conroland InelligentAgent Cooperation Laboratory Beijing Electro-Mechanical Engineering Institute China

Real-world data ubiquitously exhibit long-tailed distribution, which sparks the increasing interest in long-tailed object detection (LTOD). However, existing methods neglect that a lack of diverse data in tail classes will cause underrepresented tail class features, making their efforts for balancing foreground classes tend to over-fit tail classes and be less effective. In this paper, we propose a multi-class co-attention generation network to increase data diversity of tail classes by generating augmented samples. To alleviate imbalance, we develop a distribution-aware up-sampling strategy, performing differential up-sampling for different classes and design a bi-directional regulation loss to adjust both positive and negative gradients. Moreover, we construct a new dataset LVIS-X with more rare classes based on existing LTOD benchmark dataset LVIS. Experiments on LVIS and LVIS-X demonstrate the superiority of the proposed method.

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Rethinking development and major research plans of Industrial Internet in China

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Fundamental Research 2024年第1期4卷 3-7页

作者： Chunxiao Jiang Yang Cong Jiming Chen Chenghong Wang Guozheng Wu Ruizhen Zhao Zhiheng Wang Bin Xiao Ting Chen Beijing National Research Center for Information Science and Technology Tsinghua UniversityBeijing 100084China Tsinghua Space Center Tsinghua UniversityBeijing 100084China State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016China College of Control Science and Engineering Zhejiang UniversityHangzhou 310027China Division III Department of Information SciencesNational Natural Science Foundation of ChinaBeijing 100085China Division II Department of Information SciencesNational Natural Science Foundation of ChinaBeijing 100085China Chongqing Key Laboratory of Image Cognition School of Computer Science and TechnologyChongqing University of Posts and TelecommunicationsChongqing 400065China University of Electronic Science and Technology of China Chengdu 611731China

This paper gives a definition of the Industrial Internet and expounds on the academic connotation of the future Industrial *** this foundation,we outline the development and current status of the Industrial Internet in China and ***,we detail the avant-garde paradigms encompassed within the National Natural science Foundation of China(NSFC)’s“Future Industrial Internet Fundamental Theory and Key Technologies”research plan and its corresponding management *** research initiative endeavors to enhance interdisciplinary collaborations,aiming for a synergistic alignment of industry,academia,research,and practical *** primary focus of the research plan is on the pivotal scientific challenges inherent to the future industrial *** is poised to traverse the“first mile”,encompassing foundational research and pioneering innovations specific to the industrial internet,and seamlessly bridges to the“last mile”,ensuring the effective commercialization of scientific and technological breakthroughs into tangible industrial market *** anticipated contributions from this initiative are projected to solidify both the theoretical and practical scaffolding essential for the cultivation of a globally competitive industrial internet infrastructure in China.

关键词： Industrial Internet Academic connotation Major research plan Scientific problem Policy advice

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MBD-Net: Multi-Branch Dilated Convolutional Network With Cyst Discriminator for Renal Multi-Structure Segmentation

MBD-Net: Multi-Branch Dilated Convolutional Network With Cys...

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

作者： Yusheng Liu Yingjie Zhao Meihuan Wang Yichao Hao Xiuying Wang Lisheng Wang Department of Automation Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China College of Medicine and Biological Information Engineering Northeastern University Shenyang China School of Computer Science The University of Sydney Sydney NSW Australia

In surgery-based renal cancer treatment, one of the most essential tasks is the three-dimensional (3D) kidney parsing on computed tomography angiography (CTA) images. In this paper, we propose an end-to-end convolutional neural network-based framework to segment multiple renal structures, including kidneys, kidney tumors, arteries, and veins from arterial-phase CT images. Our method consists of two collaborative modules: First, we propose an encoding-decoding network, named Multi-Branch Dilated Convolutional Network (MBD-Net), consisting of residual, hybrid dilated convolutional, and reduced-dimensional convolutional structures, which improves the feature extraction ability with relatively fewer network parameters. Given that renal tumors and cysts have confusing geometric structures, we also design the Cyst Discriminator to effectively distinguish tumors from cysts without labeling information via gray-scale curves and radiographic features. We have quantitatively evaluated our approach on a publicly available dataset from MICCAI 2022 Kidney Parsing for Renal Cancer Treatment Challenge (KiPA2022), with mean Dice similarity coefficient (DSC) as 96.18%, 90.99%, 88.66% and 80.35% for the kidneys, kidney tumors, arteries, and veins respectively, winning the stable and top performance in the *** relevance—The proposed CNN-Based framework can automatically segment 3D kidneys, renal tumors, arteries, and veins for kidney parsing techniques, benefiting surgery-based renal cancer treatment.

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Isoform Function Prediction Based on Heterogeneous Graph Attention Networks

Isoform Function Prediction Based on Heterogeneous Graph Att...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Kuo Guo Yifan Li Hao Chen Hong-Bin Shen Yang Yang Department of Computer Science and Engineering Shanghai Jiao Tong University and Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Computational Biology Department School of Computer Science Carnegie Mellon University Pittsburgh PA USA

Isoforms refer to different mRNA molecules transcribed from the same gene, which can be translated into proteins with varying structures and functions. Predicting the functions of isoforms is an essential topic in bioinformatics as it can provide valuable insights into the intricate mechanisms of gene regulation and biological processes. Conventionally, gene function labels are standardized in Gene Ontology (GO) terms. However, traditional methods for predicting isoform function are largely limited by the absence of isoform-specific labels, sparse annotations, and the vast number of GO terms. To address these issues, we propose HANIso, a deep learning-based method for isoform function prediction. HANIso leverages a pretrained protein language model to extract features from protein sequences. It also integrates heterogeneous information, such as isoform sequence features, GO annotations, and isoform interaction data, using a Heterogeneous Graph Attention Network (HAN). This allows the model to learn the importance of different sources of information and their semantic relationships through the attention mechanism. Our method can predict function labels at both the gene level and isoform level. We conduct experiments on two species datasets, and the results demonstrate that our method outperforms existing methods on both AUROC and AUPRC. HANIso has the potential to overcome the limitations of traditional methods and provide a more accurate and comprehensive understanding of isoform function.

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Effect of the processing route on the microstructure and mechanical behavior of superlight Mg-9Li-1Zn alloy via friction stir processing

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Journal of Magnesium and Alloys 2022年第11期10卷 3064-3081页

作者： Mengran Zhou Zhuoran Zeng Chun Cheng Yoshiaki Morisada Qingyu Shi Jian-Yih Wang Hidetoshi Fujii Joining and Welding Research Institute(JWRI) Osaka UniversityOsakaJapan Department of Mechanical Engineering Tsinghua UniversityBeijing 100084China State Key Laboratory of Tribology Tsinghua UniversityBeijingChina Key Laboratory for Advanced Materials Processing Technology Ministry of Education of China Tsinghua UniversityBeijing 100084China College of Engineering and Computer Science Australian National UniversityACT 2601Australia Department of Materials Science and Engineering National Dong-Hwa UniversityHwa-LianTaiwanChina

In this study, the effect of the processing route using a friction stir processing(FSP) method on the microstructure and mechanical behavior of a Mg-9Li-1Zn alloy was systematically investigated. In the FSP method, the odd-numbered(1st and 3rd) process directions and even-numbered(2nd and 4th) passes were alternated to distribute the strain throughout the whole processed zone uniformly. Consequently, the processed zone had a much more uniform microstructure and hardness distribution than the processed zone obtained using the conventional FSP method. Using this method, the grain size of a Mg-9Li-1Zn sheet alloy was refined from ~31 μm to ~0.21 μm with uniformly distributedα and β phases. The processed alloy exhibited a high strength-ductility synergy with an ultimate tensile strength(UTS) of 220.1 MPa and total elongation of 70.0% at a strain rate of 10^(-3)s^(-1), overwhelmingly higher than those of the base metal, 155.6 MPa in UTS and 36.0%in elongation. The in-situ SEM-DIC analysis and TEM observation demonstrated that such an outstanding ductility with moderate strength is caused by grain boundary sliding, the dominant deformation mechanism of the ultra-fine-grained sample after FSP. The processing route with reverse processing direction was proven to be efficient in producing the ultrafine grain size microstructure and improving the mechanical properties of superlight Mg-9Li-1Zn alloy.

关键词： Mg-Li-Zn alloy Friction stir processing Microstructure In-situ SEM-DIC Mechanical properties

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